{
  "version": 2,
  "recordedAt": "2026-09-13T00:55:53.143Z",
  "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
  "model": "openai-codex/gpt-5.6-luna",
  "thinking": "low",
  "promptVersion": "package-styles-8",
  "synthetic": true,
  "modes": [
    {
      "id": "plain",
      "label": "Paseo Plain",
      "description": "Connected prose, everyday words.",
      "source": {
        "label": "scowalt/paseo-plain",
        "url": "https://github.com/scowalt/paseo-plain",
        "relationship": "Inspired by"
      }
    },
    {
      "id": "terse",
      "label": "Terse",
      "description": "Compact phrasing, less ceremony.",
      "source": {
        "label": "Terse",
        "relationship": "Local preset"
      }
    },
    {
      "id": "adhd",
      "label": "I Have ADHD",
      "description": "Actions and blockers brought forward.",
      "source": {
        "label": "ayghri/i-have-adhd",
        "url": "https://github.com/ayghri/i-have-adhd",
        "relationship": "Adapted from"
      }
    },
    {
      "id": "squirrel",
      "label": "Squirrel Mode",
      "description": "Small, separately readable sections.",
      "source": {
        "label": "thgMatajs/squirrel-mode",
        "url": "https://github.com/thgMatajs/squirrel-mode",
        "relationship": "Adapted from"
      }
    },
    {
      "id": "ste",
      "label": "ASD-STE100",
      "description": "Consistent terms and explicit conditions. STE-inspired.",
      "source": {
        "label": "danyuchn/asd-ste100-skill",
        "url": "https://github.com/danyuchn/asd-ste100-skill",
        "relationship": "Adapted from"
      }
    },
    {
      "id": "slye",
      "label": "Speak Like You Eat",
      "description": "Direct, conversational writing with concrete words and less jargon.",
      "source": {
        "label": "wtfzambo/speak-like-you-eat",
        "url": "https://github.com/wtfzambo/speak-like-you-eat",
        "relationship": "Adapted from"
      }
    }
  ],
  "cases": [
    {
      "id": "local-vs-prod",
      "label": "Status update",
      "title": "Local success is not production readiness",
      "sourceTitle": "Local success does not establish production readiness",
      "request": "Does the successful preview test mean the invoice export fix is ready for production?",
      "original": "## Verification boundary\nThe current evidence supports a narrower conclusion than production readiness. I ran `pnpm test:export` against the local fixture database, and the export now includes the previously missing tax column. The generated file at `tmp/invoice-preview.csv` also opened successfully in the local preview tool.\n\nThose checks establish the behavior of the fixture-backed path; they do not establish that the production credentials, storage permissions, or larger customer datasets will behave the same way. No staging or production request was made, and no remote configuration was changed.\n\nBefore calling this ready, we still need a staging export using the deployed service identity and a representative large dataset. That is a proposed verification step, not a completed test. I have also not checked whether older spreadsheet templates accept the additional column, so compatibility with those templates remains unresolved.",
      "mustKeep": [
        "pnpm test:export",
        "tmp/invoice-preview.csv"
      ],
      "reviewFacts": [
        "The local fixture-backed export test passed and the missing tax column is now present.",
        "The local preview file opened successfully.",
        "No staging or production request was made and no remote configuration changed.",
        "Staging identity and large-dataset verification are proposed, not completed.",
        "Compatibility with older spreadsheet templates remains unchecked."
      ],
      "pitfalls": [
        "Do not turn local test success into production readiness or a successful staging test.",
        "Do not present the untested credentials, permissions, dataset size, or template compatibility as confirmed failures.",
        "Do not drop the separate spreadsheet-template uncertainty when compressing the answer."
      ],
      "variants": [
        {
          "mode": "plain",
          "text": "The evidence supports the local fixture-backed path, not production readiness. I ran `pnpm test:export` against the local fixture database; the export now includes the missing tax column, and `tmp/invoice-preview.csv` opened successfully in the local preview tool.\n\nI did not test staging or production or change remote configuration, so production credentials, storage permissions, and larger customer datasets remain unverified.\n\nBefore this is ready, a staging export should be tested with the deployed service identity and a representative large dataset. That verification has not been completed. I also have not checked whether older spreadsheet templates accept the added column, so their compatibility remains unresolved.",
          "recording": {
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            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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            "context": "answer",
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            "systemHash": "dcadba54c25fc0b1d8e8cf4e214ee39d03fe39efceef3e155f49b4e00cb4f916"
          }
        },
        {
          "mode": "terse",
          "text": "## Verification boundary\n\nThe evidence supports a narrower conclusion than production readiness. I ran `pnpm test:export` against the local fixture database; the export now includes the previously missing tax column. The generated file at `tmp/invoice-preview.csv` also opened successfully in the local preview tool.\n\nThese checks establish fixture-backed behavior, not whether production credentials, storage permissions, or larger customer datasets will behave the same way. No staging or production request was made, and no remote configuration changed.\n\nBefore calling this ready, we still need a staging export using the deployed service identity and a representative large dataset. This is proposed verification, not a completed test. I have not checked whether older spreadsheet templates accept the additional column, so compatibility remains unresolved.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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          }
        },
        {
          "mode": "adhd",
          "text": "## Next verification step\n\nBefore calling this ready, we still need a staging export using the deployed service identity and a representative large dataset. This is a proposed verification step, not a completed test.\n\n## Verified locally\n\n- I ran `pnpm test:export` against the local fixture database, and the export now includes the previously missing tax column.\n- The generated file at `tmp/invoice-preview.csv` opened successfully in the local preview tool.\n\nThese checks establish the behavior of the fixture-backed path. They do not establish that production credentials, storage permissions, or larger customer datasets will behave the same way.\n\n## Still unresolved\n\n- No staging or production request was made.\n- No remote configuration was changed.\n- I have not checked whether older spreadsheet templates accept the additional column, so compatibility with those templates remains unresolved.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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            "context": "answer",
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            "systemHash": "96f21c37d6adbde2e8b083872e35c09e910a94fb54ba85bb2571db4c115b61b9"
          }
        },
        {
          "mode": "squirrel",
          "text": "## Verification boundary\n\nThe current evidence does not establish production readiness.\n\n1. I ran `pnpm test:export` against the local fixture database, and the export now includes the previously missing tax column.\n2. The generated file at `tmp/invoice-preview.csv` opened successfully in the local preview tool.\n3. These checks establish behavior for the fixture-backed path, but not whether production credentials, storage permissions, or larger customer datasets will behave the same way.\n4. No staging or production request was made, and no remote configuration was changed.\n5. Before calling this ready, we still need a staging export using the deployed service identity and a representative large dataset. This is a proposed verification step, not a completed test.\n\nCompatibility with older spreadsheet templates remains unresolved because I have not checked whether they accept the additional column.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
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        },
        {
          "mode": "ste",
          "text": "## Verification boundary\n\nThe current evidence supports a narrower conclusion than production readiness.\n\nI ran `pnpm test:export` against the local fixture database. The export now includes the previously missing tax column. The generated file at `tmp/invoice-preview.csv` also opened successfully in the local preview tool.\n\nThese checks establish behavior for the fixture-backed path. They do not establish equivalent behavior for production credentials, storage permissions, or larger customer datasets.\n\nNo staging or production request was made. No remote configuration was changed.\n\nBefore calling this ready, we still need a staging export with the deployed service identity and a representative large dataset. This is a proposed verification step, not a completed test.\n\nI have not checked whether older spreadsheet templates accept the additional column. Compatibility with those templates remains unresolved.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
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            "systemHash": "0436d0f672ebfb41fe61931c5b77d0b5f6a15e0880b911784bbc234b3ffb6954"
          }
        },
        {
          "mode": "slye",
          "text": "## Verification boundary\nThe current evidence supports a narrower conclusion than production readiness. I ran `pnpm test:export` against the local fixture database, and the export now includes the previously missing tax column. The generated file at `tmp/invoice-preview.csv` also opened successfully in the local preview tool.\n\nThese checks confirm the fixture-backed path works. They do not confirm that the production credentials, storage permissions, or larger customer datasets will behave the same way. No staging or production request was made, and no remote configuration was changed.\n\nBefore calling this ready, we still need a staging export using the deployed service identity and a representative large dataset. That is a proposed verification step, not a completed test. I have also not checked whether older spreadsheet templates accept the additional column, so compatibility with those templates remains unresolved.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
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            "systemHash": "ad0161bdf222f324eb1fc72596e064f5e38c8241c04df7ff65b72a48f86cb4fb"
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      ]
    },
    {
      "id": "verbose-explanation",
      "label": "Long-form prose",
      "title": "When caching is the wrong first move",
      "sourceTitle": "Long-form explanation of a caching decision",
      "request": "Why not put a distributed cache in front of the reports page now? A delay of up to 15 minutes is acceptable for displayed report data, but changes to access permissions must take effect immediately. Explain the reasoning and what would change your recommendation.",
      "original": "There are two different questions wrapped up in the suggestion to cache the reports page, and it is useful to separate them before choosing an implementation. One is whether previously calculated report data can be reused without violating the freshness expectation. The other is whether introducing a separate distributed service is necessary to get that reuse. Accepting data that is up to `15 minutes` old makes reuse a possibility, but it does not, by itself, establish that a distributed cache is the appropriate first change. We have not measured which part of the current request consumes the most time. The delay could come from the report query, from preparing the response, or from work in the browser, and those possibilities would not all benefit from the same intervention.\n\nThere is also a boundary between the freshness of the report and the freshness of the decision about who may read it. The accepted delay applies to report data, not to access permissions. Even if the data comes from a cache, the request must use the current access decision before returning it. Separating cached entries by organization would reduce the chance of mixing organizations' data, but that separation would not replace authorization. A person whose access was removed must not continue to receive the report simply because a cached result still exists. None of this establishes that the current implementation has such a bug; it describes a requirement any proposed cache would have to satisfy.\n\nMy recommendation is therefore to measure a representative request before adding the distributed service. If the report query dominates, and equivalent requests recur often enough to make reuse useful, a cache becomes a reasonable candidate. If response preparation or browser work dominates instead, reusing query results may leave the main delay untouched. A distributed cache would also introduce expiration behavior, cache-miss handling, and another service to operate. Those are tradeoffs to evaluate, not proof that caching would be a mistake.\n\nThe measurement has not been run, and neither a cache nor an authorization change has been implemented. I would reconsider the recommendation once we know where the delay occurs and whether repeated requests actually share reusable results. Until then, the freshness allowance supports investigating caching; it is not evidence that caching will improve this page.",
      "mustKeep": [
        "15 minutes"
      ],
      "reviewFacts": [
        "Accepting report data up to 15 minutes old permits investigating reuse, but does not establish a need for a distributed cache.",
        "The main source of request time is unmeasured; report querying, response preparation, and browser work are separate candidates.",
        "The freshness allowance does not apply to permissions: a cached response still requires the current access decision.",
        "Organization-separated cache entries are not a substitute for authorization, and removed access must take effect even when cached data exists.",
        "The security discussion is a proposed-design requirement, not evidence of a current bug.",
        "The recommendation is to measure a representative request before introducing the distributed service.",
        "Query-dominated delay and sufficient recurrence of equivalent requests would make caching a reasonable candidate; other bottlenecks might remain unaffected.",
        "Expiration, cache misses, and operating another service are distinct tradeoffs, not proof against caching.",
        "No measurement, cache implementation, or authorization change has occurred.",
        "The recommendation would be reconsidered using measured delay and evidence of reusable results, not the freshness allowance alone."
      ],
      "pitfalls": [
        "Do not turn permission to return older report data into permission to use stale authorization.",
        "Do not claim that a bottleneck or an access-control bug has been found.",
        "Do not turn a conditional caching recommendation into either a blanket rejection or approval.",
        "Do not omit one of the possible bottlenecks or operating tradeoffs just because the source is verbose.",
        "Do not replace the source's explanation with new implementation steps, a cache technology choice, or invented timing results."
      ],
      "variants": [
        {
          "mode": "plain",
          "text": "Caching the reports page raises two separate questions: whether old report data can be reused within the allowed freshness delay, and whether a distributed service is needed. Allowing report data to be `15 minutes` old makes reuse possible, but we have not measured whether the main delay comes from the report query, response preparation, or browser work.\n\nThe freshness allowance applies to report data, not access permissions. Requests must use the current authorization decision before returning cached data. Separating cache entries by organization would reduce the risk of mixing organizations' data, but it would not replace authorization. Someone whose access was removed must not receive the report from an existing cache entry. This is a requirement for a proposed cache, not evidence that the current implementation has this bug.\n\nI recommend measuring a representative request before adding a distributed service. If the query is the main delay and equivalent requests recur often enough, caching may help. If response preparation or browser work is the main delay, reusing query results may not. A distributed cache would also add expiration rules, cache-miss handling, and another service to operate.\n\nThe measurement has not been run, and no cache or authorization change has been implemented. I would reconsider the recommendation after we know where the delay occurs and whether repeated requests share reusable results. For now, the freshness allowance justifies investigating caching; it does not show that caching will improve the page.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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        },
        {
          "mode": "terse",
          "text": "The suggestion to cache the reports page combines two questions:\n\n1. Can previously calculated report data be reused without violating freshness?\n2. Is a separate distributed service necessary for that reuse?\n\nAllowing data up to `15 minutes` old makes reuse possible, but does not establish that a distributed cache is the right first change. We have not measured whether the delay comes from the report query, response preparation, or browser work; those would not necessarily benefit from the same intervention.\n\nReport freshness is separate from authorization freshness. The accepted delay applies to report data, not access decisions. Even cached data must be returned only after the current access decision is checked. Separating entries by organization could reduce cross-organization mixing, but would not replace authorization. A person whose access was removed must not receive the report because a cached result remains. This describes a requirement for any proposed cache, not evidence that the current implementation has this bug.\n\nI recommend measuring a representative request before adding the distributed service. If the report query dominates and equivalent requests recur often enough, caching becomes a reasonable candidate. If response preparation or browser work dominates, reusing query results may not address the main delay. A distributed cache would also add expiration behavior, cache-miss handling, and another service to operate. These are tradeoffs, not proof that caching would be a mistake.\n\nThe measurement has not been run, and neither a cache nor an authorization change has been implemented. I would reconsider the recommendation after we know where the delay occurs and whether repeated requests share reusable results. Until then, the freshness allowance supports investigating caching; it does not show that caching will improve the page.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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          }
        },
        {
          "mode": "adhd",
          "text": "Measure a representative request before adding the distributed service.\n\n1. Determine which part of the current request consumes the most time:\n   - The report query\n   - Response preparation\n   - Browser work\n\n   The measurement has not been run. These possibilities would not all benefit from the same intervention.\n\n2. If the report query dominates and equivalent requests recur often enough to make reuse useful, a cache becomes a reasonable candidate.\n\n3. If response preparation or browser work dominates, reusing query results may leave the main delay untouched.\n\nThe accepted delay of up to `15 minutes` applies to report data, not access permissions. Cached responses must still use the current access decision before being returned. Separating cached entries by organization would reduce the chance of mixing organizations' data, but would not replace authorization. A person whose access was removed must not continue receiving the report because a cached result exists. This describes a requirement for any proposed cache; it does not establish that the current implementation has such a bug.\n\nA distributed cache would introduce expiration behavior, cache-miss handling, and another service to operate. These are tradeoffs to evaluate, not proof that caching would be a mistake.\n\nNeither a cache nor an authorization change has been implemented. I would reconsider the recommendation after determining where the delay occurs and whether repeated requests actually share reusable results. Until then, the freshness allowance supports investigating caching; it is not evidence that caching will improve this page.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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          }
        },
        {
          "mode": "squirrel",
          "text": "## Recommendation\n\nMeasure a representative request before adding the distributed service. The freshness allowance makes caching worth investigating, but does not establish that a distributed cache is the appropriate first change.\n\n1. Separate the two questions:\n   - Can previously calculated report data be reused without violating freshness?\n   - Is a separate distributed service necessary for that reuse?\n2. Data up to `15 minutes` old may be reusable, but this alone does not justify a distributed cache.\n3. The current bottleneck has not been measured. It could be:\n   - The report query\n   - Response preparation\n   - Browser work\n4. If the report query dominates and equivalent requests recur often enough, caching becomes a reasonable candidate.\n5. If response preparation or browser work dominates, reusing query results may leave the main delay untouched.\n\n## Authorization and freshness\n\n1. The accepted delay applies to report data, not to the decision about who may read it.\n2. A cached response must still use the current access decision before it is returned.\n3. Separating cached entries by organization would reduce the chance of mixing organizations’ data, but would not replace authorization.\n4. Someone whose access was removed must not continue receiving the report because a cached result still exists.\n5. This does not establish that the current implementation has such a bug; it states a requirement any proposed cache must satisfy.\n\n## Tradeoffs and status\n\nA distributed cache would introduce expiration behavior, cache-miss handling, and another service to operate. These are tradeoffs to evaluate, not proof that caching would be a mistake.\n\nThe measurement has not been run, and neither a cache nor an authorization change has been implemented. I would reconsider the recommendation once we know where the delay occurs and whether repeated requests actually share reusable results.\n\nUntil then, the freshness allowance supports investigating caching; it is not evidence that caching will improve this page.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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          }
        },
        {
          "mode": "ste",
          "text": "The suggestion to cache the reports page contains two separate questions:\n\n1. Can previously calculated report data be reused without violating the freshness requirement?\n2. Is a separate distributed service necessary for that reuse?\n\nAllowing data to be `15 minutes` old makes reuse possible. It does not establish that a distributed cache is the appropriate first change.\n\nWe have not measured which part of the request consumes the most time. The delay could come from the report query, response preparation, or browser work. Each possibility may require a different intervention.\n\nReport freshness and access-decision freshness have different boundaries. The accepted delay applies to report data, not access permissions. The request must use the current access decision before it returns cached data.\n\nSeparating cached entries by organization would reduce the chance of mixing organizations' data. It would not replace authorization. A person whose access was removed must not receive the report because a cached result still exists. This does not establish that the current implementation has this bug. It states a requirement for any proposed cache.\n\nMy recommendation is to measure a representative request before adding the distributed service.\n\n- If the report query dominates and equivalent requests recur often enough, a cache becomes a reasonable candidate.\n- If response preparation or browser work dominates, reusing query results may leave the main delay unchanged.\n\nA distributed cache would also introduce expiration behavior, cache-miss handling, and another service to operate. These are tradeoffs to evaluate, not proof that caching would be a mistake.\n\nThe measurement has not been run. Neither a cache nor an authorization change has been implemented. I would reconsider the recommendation after we know where the delay occurs and whether repeated requests share reusable results.\n\nUntil then, the freshness allowance supports investigating caching. It is not evidence that caching will improve this page.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
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          }
        },
        {
          "mode": "slye",
          "text": "The suggestion to cache the reports page contains two separate questions. We should separate them before choosing an implementation. First, can we reuse report data that was calculated earlier without breaking the freshness requirement? Second, do we need a separate distributed service to reuse that data? Accepting data that is up to `15 minutes` old makes reuse possible. It does not, by itself, show that a distributed cache is the right first change. We have not measured which part of the current request takes the most time. The delay could come from the report query, preparing the response, or browser work. The same change would not help all of these equally.\n\nThe report's freshness is separate from the freshness of the decision about who may read it. The allowed delay applies to report data, not access permissions. Even if the data comes from a cache, the request must check current access before returning it. Separating cached entries by organization would reduce the chance of mixing organizations' data, but it would not replace authorization. Someone whose access was removed must not keep receiving the report just because a cached result still exists. This does not show that the current implementation has this bug. It states a requirement for any proposed cache.\n\nI recommend measuring a representative request before adding the distributed service. If the report query takes most of the time, and equivalent requests happen often enough for reuse to help, a cache becomes a reasonable option. If preparing the response or browser work takes most of the time, reusing query results may not reduce the main delay. A distributed cache would also add expiration behavior, cache-miss handling, and another service to operate. These are tradeoffs to assess, not proof that caching would be a mistake.\n\nThe measurement has not been run, and we have not implemented a cache or an authorization change. I would reconsider the recommendation after we know where the delay occurs and whether repeated requests actually share reusable results. Until then, the freshness allowance supports investigating caching. It does not show that caching will improve this page.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
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          }
        }
      ]
    },
    {
      "id": "ordered-recovery",
      "label": "Recovery procedure",
      "title": "Recover safely, in the right order",
      "sourceTitle": "A recovery procedure whose order and branches matter",
      "request": "Write the operator procedure for recovering the paused local indexer without dropping queued work.",
      "original": "## Local recovery procedure\nUse this sequence for the local indexer only. Keep incoming work paused until verification is complete; successful process startup is not sufficient evidence of recovery.\n\n1. Run `indexctl pause --local` and confirm that the status reports paused. If it does not, stop here rather than continuing with a moving queue.\n2. Save the recovery inputs:\n   - Copy `var/index/checkpoint.json` to `var/index/checkpoint.backup.json`.\n   - Record the pending item count shown by `indexctl status --local`. If the checkpoint cannot be copied, stop and investigate before restarting.\n3. Run `indexctl restart --local`. Do not delete the queue directory as part of this step.\n4. While intake remains paused, verify the recovered queue:\n   - Check that the pending item count matches the recorded count.\n   - If it differs, leave intake paused and compare the saved checkpoint with the current one; do not resume automatically.\n5. Only after the counts match, run `indexctl resume --local`.\n\nRetain the backup until the resumed indexer has processed the pending work. These are instructions, not a report that recovery has already been performed.",
      "mustKeep": [
        "indexctl pause --local",
        "var/index/checkpoint.json",
        "var/index/checkpoint.backup.json",
        "indexctl status --local",
        "indexctl restart --local",
        "indexctl resume --local"
      ],
      "reviewFacts": [
        "The procedure is for the local indexer and requires confirmed pause before continuing.",
        "The checkpoint is backed up and the pending count recorded before restart; backup failure stops the procedure.",
        "The queue directory must not be deleted.",
        "Intake remains paused through verification; a count mismatch requires investigation without automatic resume.",
        "Resume occurs only after matching counts, and the backup is retained until pending work is processed.",
        "The answer provides a procedure, not evidence that it was executed."
      ],
      "pitfalls": [
        "Do not flatten the checklist into unordered advice or move restart before the recovery inputs are saved.",
        "Do not lose nested failure branches or resume merely because the process started.",
        "Do not generalize local commands into a production recovery procedure."
      ],
      "variants": [
        {
          "mode": "plain",
          "text": "## Local recovery procedure\n\nUse this sequence for the local indexer only. Keep incoming work paused until verification is complete; a successful process startup does not confirm recovery.\n\n1. Run `indexctl pause --local` and confirm that the status reports paused. If it does not, stop rather than continuing with a moving queue.\n2. Save the recovery inputs:\n   - Copy `var/index/checkpoint.json` to `var/index/checkpoint.backup.json`.\n   - Record the pending item count shown by `indexctl status --local`. If the checkpoint cannot be copied, stop and investigate before restarting.\n3. Run `indexctl restart --local`. Do not delete the queue directory during this step.\n4. While intake remains paused, verify the recovered queue:\n   - Confirm that the pending item count matches the recorded count.\n   - If it differs, keep intake paused and compare the saved checkpoint with the current one. Do not resume automatically.\n5. After the counts match, run `indexctl resume --local`.\n\nKeep the backup until the resumed indexer has processed the pending work. These are instructions, not a report that recovery has already been performed.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "dcadba54c25fc0b1d8e8cf4e214ee39d03fe39efceef3e155f49b4e00cb4f916"
          }
        },
        {
          "mode": "terse",
          "text": "## Local recovery procedure\n\nUse this sequence for the local indexer only. Keep incoming work paused until verification is complete; successful process startup alone does not confirm recovery.\n\n1. Run `indexctl pause --local` and confirm the status reports paused. If it does not, stop rather than continuing with a moving queue.\n2. Save the recovery inputs:\n   - Copy `var/index/checkpoint.json` to `var/index/checkpoint.backup.json`.\n   - Record the pending item count shown by `indexctl status --local`. If the checkpoint cannot be copied, stop and investigate before restarting.\n3. Run `indexctl restart --local`. Do not delete the queue directory as part of this step.\n4. While intake remains paused, verify the recovered queue:\n   - Confirm that the pending item count matches the recorded count.\n   - If it differs, leave intake paused, compare the saved checkpoint with the current one, and do not resume automatically.\n5. Only after the counts match, run `indexctl resume --local`.\n\nRetain the backup until the resumed indexer has processed the pending work. These are instructions, not a report that recovery has already been performed.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "d5c0c1e06ea3ea03b4e1ce3b2f3879656880fa2b1dd9205d4dbfb5d709fa975d"
          }
        },
        {
          "mode": "adhd",
          "text": "## Local recovery procedure\n\nKeep incoming work paused until verification is complete. Successful process startup is not sufficient evidence of recovery.\n\n1. Run `indexctl pause --local` and confirm that the status reports paused. If it does not, stop rather than continuing with a moving queue.\n2. Save the recovery inputs:\n   - Copy `var/index/checkpoint.json` to `var/index/checkpoint.backup.json`.\n   - Record the pending item count shown by `indexctl status --local`.\n   - If the checkpoint cannot be copied, stop and investigate before restarting.\n3. Run `indexctl restart --local`. Do not delete the queue directory as part of this step.\n4. While intake remains paused, verify the recovered queue:\n   - Confirm that the pending item count matches the recorded count.\n   - If it differs, leave intake paused and compare the saved checkpoint with the current one; do not resume automatically.\n5. Only after the counts match, run `indexctl resume --local`.\n\nRetain the backup until the resumed indexer has processed the pending work. These are instructions, not a report that recovery has already been performed.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "96f21c37d6adbde2e8b083872e35c09e910a94fb54ba85bb2571db4c115b61b9"
          }
        },
        {
          "mode": "squirrel",
          "text": "## Local recovery procedure\n\nUse this sequence for the local indexer only. Keep incoming work paused until verification is complete; successful process startup is not sufficient evidence of recovery.\n\n1. Run `indexctl pause --local` and confirm that the status reports paused. If it does not, stop here rather than continuing with a moving queue.\n2. Save the recovery inputs:\n   - Copy `var/index/checkpoint.json` to `var/index/checkpoint.backup.json`.\n   - Record the pending item count shown by `indexctl status --local`. If the checkpoint cannot be copied, stop and investigate before restarting.\n3. Run `indexctl restart --local`. Do not delete the queue directory as part of this step.\n4. While intake remains paused, verify the recovered queue:\n   - Confirm that the pending item count matches the recorded count.\n   - If it differs, leave intake paused and compare the saved checkpoint with the current one; do not resume automatically.\n5. Only after the counts match, run `indexctl resume --local`.\n\nRetain the backup until the resumed indexer has processed the pending work. These are instructions, not a report that recovery has already been performed.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "6be86544dfebbc249a3a8e94cec6faf3732af1e299674640a42bce7d0ab4438e"
          }
        },
        {
          "mode": "ste",
          "text": "## Local recovery procedure\n\nUse this sequence for the local indexer only. Keep incoming work paused until verification is complete. Successful process startup does not prove recovery.\n\n1. Run `indexctl pause --local`. Confirm that the status reports paused. If it does not, stop and do not continue with a moving queue.\n2. Save the recovery inputs:\n   - Copy `var/index/checkpoint.json` to `var/index/checkpoint.backup.json`.\n   - Record the pending item count from `indexctl status --local`. If you cannot copy the checkpoint, stop and investigate before restarting.\n3. Run `indexctl restart --local`. Do not delete the queue directory during this step.\n4. Keep intake paused and verify the recovered queue:\n   - Confirm that the pending item count matches the recorded count.\n   - If the count differs, keep intake paused and compare the saved checkpoint with the current checkpoint. Do not resume automatically.\n5. Run `indexctl resume --local` only after the counts match.\n\nRetain the backup until the resumed indexer has processed the pending work. These are instructions, not a report that recovery has already occurred.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "0436d0f672ebfb41fe61931c5b77d0b5f6a15e0880b911784bbc234b3ffb6954"
          }
        },
        {
          "mode": "slye",
          "text": "## Local recovery procedure\nUse this sequence for the local indexer only. Keep incoming work paused until verification is complete. A successful process startup does not confirm recovery.\n\n1. Run `indexctl pause --local` and confirm that the status says paused. If it does not, stop instead of continuing with a moving queue.\n2. Save the recovery inputs:\n   - Copy `var/index/checkpoint.json` to `var/index/checkpoint.backup.json`.\n   - Record the pending item count shown by `indexctl status --local`. If the checkpoint cannot be copied, stop and investigate before restarting.\n3. Run `indexctl restart --local`. Do not delete the queue directory during this step.\n4. While intake is paused, verify the recovered queue:\n   - Confirm that the pending item count matches the recorded count.\n   - If it differs, keep intake paused and compare the saved checkpoint with the current one. Do not resume automatically.\n5. Run `indexctl resume --local` only after the counts match.\n\nKeep the backup until the resumed indexer has processed the pending work. These are instructions, not a report that recovery has already been performed.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "ad0161bdf222f324eb1fc72596e064f5e38c8241c04df7ff65b72a48f86cb4fb"
          }
        }
      ]
    },
    {
      "id": "comparison-table",
      "label": "Tradeoffs",
      "title": "Three options. Different tradeoffs.",
      "sourceTitle": "A comparison table carries independent tradeoffs",
      "request": "Compare inline processing and queued processing for the thumbnail endpoint. We have no benchmark results yet.",
      "original": "## Processing options\nThe following comparison describes behavior, not measured throughput. Neither option has been benchmarked with the current image set.\n\n| Concern | Inline processing | Queued processing |\n| --- | --- | --- |\n| Response meaning | Thumbnail is ready when the request succeeds | Job is accepted; thumbnail may still be pending |\n| Failure visibility | Processing failure is returned in the request | Processing failure must be exposed through job status |\n| Service ownership | No separate worker to operate | Worker and queue require operation |\n| Traffic bursts | Requests occupy processing capacity immediately | Pending jobs can wait, increasing completion delay |\n\nI recommend inline processing for the initial version if its measured request duration fits the caller's timeout. This avoids introducing a worker before we know one is needed. Choose queued processing if that duration cannot fit and callers can handle a pending result. A queue changes where waiting occurs; it does not by itself make image processing faster. The next useful step is a representative duration measurement, not a throughput claim based on the architecture alone.",
      "mustKeep": [],
      "reviewFacts": [
        "Neither option has been benchmarked with the current image set.",
        "Inline success means a ready thumbnail; queued acceptance can mean work is still pending.",
        "Inline failures appear in the request, whereas queued failures need job-status visibility.",
        "Queued processing introduces worker and queue operation; inline processing has no separate worker.",
        "A queue can defer burst work but increase completion delay and does not itself accelerate processing.",
        "Inline is recommended conditionally on measured duration fitting the timeout; queued processing is conditional on non-fitting duration and caller support for pending results."
      ],
      "pitfalls": [
        "Do not discard table rows as decorative formatting; each row adds a distinct comparison.",
        "Do not claim queued processing is faster or that either option has demonstrated throughput.",
        "Do not reduce a conditional recommendation to an unconditional architecture choice."
      ],
      "variants": [
        {
          "mode": "plain",
          "text": "The comparison describes behavior, not measured throughput. Neither option has been benchmarked with the current image set.\n\n| Concern | Inline processing | Queued processing |\n| --- | --- | --- |\n| Response meaning | The thumbnail is ready when the request succeeds | The job is accepted, but the thumbnail may still be pending |\n| Failure visibility | Processing failures are returned in the request | Processing failures must be exposed through job status |\n| Service ownership | No separate worker is required | The worker and queue must be operated |\n| Traffic bursts | Requests use processing capacity immediately | Pending jobs can wait, increasing completion delay |\n\nUse inline processing for the initial version if its measured request duration fits the caller’s timeout. This avoids adding a worker before it is needed. Use queued processing if the duration does not fit and callers can handle a pending result. A queue changes where waiting occurs; it does not automatically make image processing faster.\n\nThe next useful step is to measure duration with a representative image set, not to infer throughput from the architecture.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "dcadba54c25fc0b1d8e8cf4e214ee39d03fe39efceef3e155f49b4e00cb4f916"
          }
        },
        {
          "mode": "terse",
          "text": "## Processing options\n\nThe following comparison describes behavior, not measured throughput. Neither option has been benchmarked with the current image set.\n\n| Concern | Inline processing | Queued processing |\n| --- | --- | --- |\n| Response meaning | Thumbnail is ready when the request succeeds | Job is accepted; thumbnail may still be pending |\n| Failure visibility | Processing failure is returned in the request | Processing failure must be exposed through job status |\n| Service ownership | No separate worker to operate | Worker and queue require operation |\n| Traffic bursts | Requests occupy processing capacity immediately | Pending jobs can wait, increasing completion delay |\n\nI recommend inline processing initially if its measured request duration fits the caller’s timeout. This avoids introducing a worker before we know one is needed. Choose queued processing if that duration cannot fit and callers can handle a pending result. A queue changes where waiting occurs; it does not by itself make image processing faster. The next useful step is measuring representative duration, not claiming throughput from the architecture alone.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "d5c0c1e06ea3ea03b4e1ce3b2f3879656880fa2b1dd9205d4dbfb5d709fa975d"
          }
        },
        {
          "mode": "adhd",
          "text": "## Next step\n\nMeasure representative request duration. This is not a throughput benchmark, and neither option has been benchmarked with the current image set.\n\n## Processing options\n\n| Concern | Inline processing | Queued processing |\n| --- | --- | --- |\n| Response meaning | Thumbnail is ready when the request succeeds | Job is accepted; thumbnail may still be pending |\n| Failure visibility | Processing failure is returned in the request | Processing failure must be exposed through job status |\n| Service ownership | No separate worker to operate | Worker and queue require operation |\n| Traffic bursts | Requests occupy processing capacity immediately | Pending jobs can wait, increasing completion delay |\n\n- Use inline processing for the initial version if its measured request duration fits the caller's timeout. This avoids introducing a worker before we know one is needed.\n- Use queued processing if that duration cannot fit and callers can handle a pending result.\n- A queue changes where waiting occurs; it does not by itself make image processing faster.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "96f21c37d6adbde2e8b083872e35c09e910a94fb54ba85bb2571db4c115b61b9"
          }
        },
        {
          "mode": "squirrel",
          "text": "## Processing options\n\nThis comparison describes behavior, not measured throughput. Neither option has been benchmarked with the current image set.\n\n| Concern | Inline processing | Queued processing |\n| --- | --- | --- |\n| Response meaning | Thumbnail is ready when the request succeeds | Job is accepted; thumbnail may still be pending |\n| Failure visibility | Processing failure is returned in the request | Processing failure must be exposed through job status |\n| Service ownership | No separate worker to operate | Worker and queue require operation |\n| Traffic bursts | Requests occupy processing capacity immediately | Pending jobs can wait, increasing completion delay |\n\nI recommend inline processing for the initial version if its measured request duration fits the caller's timeout. This avoids introducing a worker before we know one is needed.\n\nChoose queued processing if that duration cannot fit and callers can handle a pending result. A queue changes where waiting occurs; it does not by itself make image processing faster.\n\nThe next useful step is a representative duration measurement, not a throughput claim based on the architecture alone.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "6be86544dfebbc249a3a8e94cec6faf3732af1e299674640a42bce7d0ab4438e"
          }
        },
        {
          "mode": "ste",
          "text": "## Processing options\n\nThe comparison below describes behavior, not measured throughput. Neither option has been benchmarked with the current image set.\n\n| Concern | Inline processing | Queued processing |\n| --- | --- | --- |\n| Response meaning | The thumbnail is ready when the request succeeds. | The job is accepted. The thumbnail may still be pending. |\n| Failure visibility | The request returns processing failure. | Job status must expose processing failure. |\n| Service ownership | No separate worker requires operation. | The worker and queue require operation. |\n| Traffic bursts | Requests immediately occupy processing capacity. | Pending jobs can wait, which increases completion delay. |\n\nI recommend inline processing for the initial version if its measured request duration fits the caller's timeout. This avoids introducing a worker before we know that one is needed.\n\nChoose queued processing if the measured duration cannot fit the timeout and callers can handle a pending result. A queue changes where waiting occurs. It does not make image processing faster by itself.\n\nThe next useful step is to measure a representative duration. Do not make a throughput claim based on the architecture alone.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "0436d0f672ebfb41fe61931c5b77d0b5f6a15e0880b911784bbc234b3ffb6954"
          }
        },
        {
          "mode": "slye",
          "text": "## Processing options\nThis comparison describes behavior, not measured throughput. Neither option has been benchmarked with the current image set.\n\n| Concern | Inline processing | Queued processing |\n| --- | --- | --- |\n| Response meaning | The thumbnail is ready when the request succeeds | The job is accepted; the thumbnail may still be pending |\n| Failure visibility | The request returns processing failure | Job status must show processing failure |\n| Service ownership | No separate worker to operate | The worker and queue require operation |\n| Traffic bursts | Requests use processing capacity immediately | Pending jobs can wait, increasing completion delay |\n\nI recommend inline processing for the initial version if its measured request duration fits the caller's timeout. This avoids adding a worker before we know one is needed. Choose queued processing if that duration does not fit and callers can handle a pending result. A queue changes where waiting occurs; it does not make image processing faster by itself. The next useful step is to measure duration with representative data, not to claim throughput from the architecture alone.",
          "recording": {
            "runId": "2026-09-13T00-55-53-142Z-d273f1f1",
            "recordedAt": "2026-09-13T00:55:53.143Z",
            "model": "openai-codex/gpt-5.6-luna",
            "promptVersion": "package-styles-8",
            "context": "answer",
            "promptSource": "package-v4",
            "systemHash": "ad0161bdf222f324eb1fc72596e064f5e38c8241c04df7ff65b72a48f86cb4fb"
          }
        }
      ]
    }
  ]
}