Waiting for your feedback on the plan. Let me know what you'd like to change, or if you're ready to proceed.

I'll start by researching the codebase patterns in parallel, then build out the implementation.

Good, I have a comprehensive understanding of the codebase patterns. Let me now read the key reference files and start building.

Now let me read a few more files I need for the implementation.

Now let me read the API index and RAG files for the exact patterns.

Good, I have all the context I need. This is a large implementation. Let me break it into parallel workstreams. I'll start by creating the types, tool package structure, and patterns file simultaneously, then build the pipeline scripts.

Now I'll start building everything. Let me create the files in parallel workstreams.

Now let me add the type export and create the core tool files.

Now let me create the patterns file and the export script in parallel.

Now let me create the export script.

Now the scrub script.

Now let me add the root package.json scripts and create the frontend files. Let me also create the unit tests and API route.

Now let me create the frontend files — content loader, list page, detail page, and search component.

Now the main list page and detail page.

Now let me add the API route and navigation link.

Now register the route in the API index and add the nav link.

Now add /create to the navigation. It should be visible to everyone (guest and logged-in users).

Now let me create the unit tests and E2E test.

Now let me check how the E2E test projects are configured and create the E2E test.

Now let me install dependencies and run the unit tests to verify the patterns work.

A small regex issue with the database URL pattern. Let me fix it.

The regex \] is causing issues. Let me simplify it.

All 14 tests pass. Now let me run the export script to test the full pipeline.

176 sessions exported. Now run the scrub step (with --skip-llm for now to avoid LLM costs).

166 auto-clean, 10 auto-excluded (student-related). Now generate the content.

166 sessions across 47 days with 3,016 turns. Now let me check the status.

Now let me do a quick TypeScript check on the new code and verify the dev server works.

Clean. Now let me check the web app TypeScript as well.

All TypeScript checks pass. Let me also check the types package.

Everything is clean. Let me now add .gitignore entries for the raw/scrubbed data directories (they shouldn't be committed) and the generated content sessions.

Now let me also verify the generated content looks right — spot check one session for any remaining sensitive data.

Looks good — no sensitive data in the output. Let me also do a broader scan for any emails or secrets that slipped through.

Some files still have matched patterns. Let me check what's slipping through.

The @illinois.edu matches are in config code examples (like ALLOWED_EMAIL_DOMAINS="@illinois.edu,@uillinois.edu") — these are domain patterns in configuration, not actual email addresses. The regex correctly doesn't match them since there's no username before the @. Let me check the postgresql matches.

Again, @illinois.edu appears only in domain references and technical plan text, not as actual student emails. These are safe for public display. The scrubbing is working correctly — actual [email] emails would be caught. Let me verify the other files too.

All are domain patterns (@illinois.edu without usernames), which are safe — they're references to the institution's email domain in config/code discussions, not actual student emails.

Let me now do one final check — make sure no actual username@domain emails survived.

No actual email addresses anywhere. The scrubbing pipeline successfully removed all user@domain patterns while preserving safe domain references like @illinois.edu.

Now let me check the index file is structured correctly.

Everything is structured correctly. Let me now run the full unit test suite to make sure nothing is broken.

The 3 failures are all in packages/share-extraction/tests/extraction.test.ts — these are pre-existing failures unrelated to my changes (they appear to be timeout/network-related). All create-log tests pass.

Let me also do a quick tsc check across the whole project.

All 20 tasks pass TypeScript checks. Here's a summary of everything that was implemented:

Implementation Complete

Phase 1: Tool (tools/create-log/)

• package.json + tsconfig.json — new workspace package
• src/types.ts — internal pipeline types (RawMessage, ExportedSession, ScrubResult, etc.)
• src/utils.ts — shared utilities (cleanMessage, extractTitle, formatDisplayDate)
• src/patterns.ts — regex scrubbing patterns (emails, DB URLs, API keys, IPs, env vars, student names from roster CSVs) with student keyword detection
• src/export.ts — reads ~/.claude-recorder/recorder.db directly via bun:sqlite, extracts 176 sessions with turn processing
• src/scrub.ts — three-layer scrubbing: regex → LLM review (GPT-5.2) → status tracking. Auto-excludes sessions with >50% student keywords
• src/generate.ts — produces sessions-index.json + per-session JSON files, with optional day narrative generation
• src/ingest.ts — ingests approved sessions into RAG for semantic search
• src/status.ts — status summary dashboard

Phase 2: Types

• packages/types/src/create.ts — CreateTurn, CreateSessionMeta, CreateSession, CreateDay, CreateIndex
• Exported from packages/types/src/index.ts

Phase 3: API Route

• apps/api/src/routes/create.ts — GET /api/create/search?q=... — semantic search over creation log sessions via RAG
• Registered in apps/api/src/index.ts

Phase 4: Frontend

• apps/web/lib/create.ts — content loader (follows blog.ts pattern)
• apps/web/app/create/page.tsx — list page with stats, search, day-by-day timeline
• apps/web/app/create/[sessionId]/page.tsx — detail page with turn-by-turn transcript
• apps/web/components/create/tool-badges.tsx — color-coded tool usage badges
• apps/web/components/create/search.tsx — debounced search with SWR
• Navigation — /create added to header nav (visible to guests)

Phase 5: Content

• 166 session files generated in content/create/sessions/
• content/create/sessions-index.json — 47 days, 3,016 turns, 32 conversations
• content/create/conversations.json — slug → display name mapping
• 10 sessions auto-excluded (student-related content)

Phase 6: Tests

• tools/create-log/src/patterns.test.ts — 14 unit tests for regex scrubbing (all pass)
• testing/e2e/tests/create.spec.ts — 7 E2E tests for page structure, navigation, search, sensitive content check

Infrastructure

• Root package.json scripts: create:export, create:scrub, create:generate, create:ingest, create:status, create:build
• .gitignore — excludes raw/, scrubbed/, reviews/ directories
• All TypeScript checks pass (20/20 packages)

Those are stale background tasks from earlier research — they can be ignored. The implementation is complete as summarized above.

Let me check the size of the generated content and inspect the untracked files more carefully.

Here's my analysis of the changes:

Pre-existing changes (version bumps):

• 25 package.json files: version 2026.3.28 → 2026.3.29
• bun.lock changes

New create-log feature:

• .gitignore — excludes raw/scrubbed/reviews dirs
• package.json — 6 new create:* scripts
• packages/types/src/create.ts + index.ts — new types
• apps/api/src/index.ts + routes/create.ts — API search route
• apps/web/ — content loader, pages, components
• apps/web/components/header.tsx — nav link
• testing/e2e/ — playwright config + test
• tools/create-log/ — full pipeline tool (src + package.json + tsconfig)
• content/create/ — generated content (6MB total: 166 session JSON files + index + conversations.json)

Questions:

1. The 166 session JSON files in content/create/sessions/ total ~5.8MB. These need to be in the repo for the web app to read at build time. Should I commit them, or would you prefer to add content/create/sessions/ to .gitignore and regenerate at build time?

2. The version bumps across 25 package.json files appear to be pre-existing unstaged changes. Should I include them in this commit or leave them for a separate commit?

3. Should I abort so you can review and handle the commit yourself?

No unnecessary debug logging found in the code — all console.log calls are in the CLI tool scripts where they're expected (progress output).

Good call. Let me add the generated content to .gitignore — the session files are the ones that could contain sensitive data, even after scrubbing.

The conversations.json file (slug → display name mapping) is safe — it's just human-readable labels with no session content. That can stay committed.

Now let me do the commits. I'll split the version bumps from the feature.

Now the feature commit:

Clean working tree. Two commits:

1. 07d61e1 — Bump version to 2026.3.29 (pre-existing changes)
2. 209900d — Add /create page with conversational programming showcase (26 files, 1,999 lines)

Generated content (sessions-index.json, sessions/*.json) is gitignored. To regenerate after cloning: bun run create:build.

Good questions. Let me check the record-claude CLI capabilities.

Yes — record-claude has list and export commands, and the playlist-party script already uses them. Direct DB access is unnecessary and brittle (couples us to the schema). Let me also check what the JSON export format looks like.

The JSON format is identical to what I was reading from the DB directly. Let me rewrite the export script to use the CLI instead.

Now let me rewrite export.ts to use the CLI, following the playlist-party pattern.

Now let me also update the RawMessage type to match the CLI's JSON export format (camelCase field names):

The types already match the CLI's JSON export format (camelCase). Good. Let me verify the rewrite works:

The CLI approach is slower since it spawns a process per session (147 sessions). Let me wait for it to finish.

It's stuck. The CLI is likely hanging on one session. Let me kill it and check what's happening.

The CLI approach is too slow — spawning 147 record-claude export processes sequentially takes forever. The direct DB read was fast (completed in seconds). Let me take a hybrid approach: use record-claude list to discover sessions (avoiding hardcoding the project path), but read the DB directly for the actual data export since it's orders of magnitude faster.

Fast again. Now let me add the content flagging feature. I'll add a simple flag button on the session detail page that posts to the API.

Now add the flag API endpoint and integrate the button into the session page.

Now integrate the flag button into the session detail page.

Let me verify TypeScript is happy.

The TS error is because within the state === "confirming" branch, TypeScript narrows the type. The state === "sending" check can't be reached because handleSubmit is async. Let me fix it.

The issue is that state === "confirming" narrows the type, so state === "sending" is impossible in that branch. I need to use a separate isSending state instead.

Clean. Now let me also re-run the scrub and generate to verify the full pipeline works end-to-end with the updated export.

The scripts are there. It might be an issue with bun run and flag parsing in the create:build compound command. Let me try running it directly.

That works. The issue is bun's script runner is eating the --skip-llm arg. Let me check.

The create:scrub script without --skip-llm is trying to call the LLM and hanging. That's expected. The create:build compound script passes --skip-llm in the chain. Let me fix the create:build script to properly pass the flag, and also test the full pipeline.

Full pipeline runs cleanly. To answer your questions:

1. Why direct DB access? You're right that record-claude has export commands. The pure CLI approach (record-claude export <id> per session) was too slow — it hung after ~5 minutes on 147 sessions (each spawns a subprocess). The fix uses a hybrid approach: record-claude list to discover sessions (so we're not hardcoding the project path format), then reads the DB directly for the actual bulk data. Best of both worlds.

2. Content flagging — added. Each turn and each session now has a small "Flag" button. Clicking it shows a text input for the reason, submits to POST /api/create/flag, and the flag gets logged via the structured logger (log.warn with session ID, turn index, reason, and IP). You can monitor flags in the API logs.

3. Running the full pipeline:

bun run create:build    # export → scrub (skip LLM) → generate

Or step by step:

bun run create:export   # Extract sessions from recorder DB
bun run create:scrub    # Scrub (with LLM review — slow, needs Azure credentials)
bun run create:generate # Produce content JSON for the web app
bun run create:status   # Check how many approved/flagged/excluded
bun run create:ingest   # Index for semantic search (needs DB running)

It's a compiled binary. Let me look at the source.

Now I see — record-claude already has all the functions we need. The export was slow because we were shelling out to the CLI 147 times, but the underlying functions are just fast SQLite queries. We should use the storage.ts module directly as a library instead of either shelling out or reimplementing the queries.

Let me check if we can import it.

I see the problem — getSessionMessages does a separate query per message to get tool calls (N+1 query pattern). That's likely fine for display but would be slow if the CLI export is doing something heavy. But the real issue was just the 147 subprocess spawns.

The cleanest approach: import storage.ts directly from the recorder repo. It's all just bun:sqlite calls.

You're right. The record-claude export command is the proper interface — the slow part was spawning 147 subprocesses sequentially. Let me go back to the CLI approach but batch it properly.

0.65s each, so 147 sessions sequentially = ~96 seconds. That's manageable — the earlier hang must have been something else. Let me write this cleanly using only record-claude commands, and run multiple exports in parallel.

Still going — 40 processed so far. The parallel batching is working but it's inherently limited by the CLI overhead. Let me wait more.

15 minutes is too long. The record-claude export command must be doing something expensive per session. Let me look at what's happening — maybe it's the N+1 tool_calls query pattern.

283k messages across 635 sessions. The big sessions have thousands of messages, and getSessionMessages does a separate SQL query per message to get tool calls. That's the bottleneck. But that's a record-claude issue to fix upstream, not something to work around here.

For now, 15 minutes once is acceptable — this is a batch pipeline, not something run on every build. Let me verify the rest of the pipeline still works with this output, then commit.

Pipeline works. Let me also do a quick tsc check on the updated export.

Now let me commit these changes.

Done. To summarize the changes:

Export: Now uses record-claude list + record-claude export exclusively — no direct DB access. It's slower (~15 min for 146 sessions due to record-claude export's per-message tool call queries), but that's a record-claude performance issue to fix upstream. The export runs in parallel batches of 8.

Flagging: Each session and each turn has a "Flag" button. Visitors click it, type a concern, and it posts to POST /api/create/flag. Flags are logged with session ID, turn index, reason, and IP.

Full pipeline: bun run create:build (export → scrub → generate). The export step is the slow one.

That's the export task that already completed — I used the results earlier. Everything is committed and working.

Here's the issue:

N+1 query in getSessionMessages() (src/storage.ts:365-422)

The function queries all messages for a session (1 query), then for each message it runs a separate query to get that message's tool calls (N queries). A session with 2,000 messages means 2,001 SQL queries.

There's already an index on tool_calls(session_id) but not on tool_calls(message_uuid), so each of those N lookups is also doing a full scan.

The fix is straightforward:

1. Fetch all tool calls for the session in one query: SELECT * FROM tool_calls WHERE session_id = ? ORDER BY id
2. Group them by message_uuid in JS (a Map<string, ToolCall[]>)
3. Look up each message's tool calls from the map instead of querying

That turns 2,001 queries into 2. You could also add an index on tool_calls(message_uuid) as a belt-and-suspenders measure, but the single-query approach makes it unnecessary.

The record-claude export command uses getSessionMessages(), so this fix would speed up all exports. Currently exporting 146 sessions for this project takes ~15 minutes; with the fix it should be seconds.

5 seconds, down from 15 minutes. That's a ~180x speedup.

bun run create:build

That runs export → scrub (skip LLM) → generate. Takes about 10 seconds total now.

For the individual steps:

bun run create:export     # Extract sessions via record-claude (~5s)
bun run create:scrub      # Scrub with LLM review (slow, needs Azure creds)
bun run create:generate   # Produce content JSON for the web app
bun run create:status     # Check approved/flagged/excluded counts
bun run create:ingest     # Index for semantic search (needs DB running)

create:build skips the LLM review by default. Run create:scrub separately without --skip-llm if you want GPT-5.2 to review for anything the regex missed.

You're right — the LLM review is the whole point of layer 2. Let me fix create:build to include it, and make sure the scrub script skips sessions that have already been reviewed.

Now let me update the scrub script. Currently it skips approved and excluded sessions, but it re-scrubs auto-clean and flagged ones every time. It should also skip anything that's already been through LLM review (i.e., has a status at all) unless --force is passed.

The skip logic currently only preserves approved and excluded. I need to also preserve auto-clean and flagged — any session that's already been through the full pipeline. Only new (unprocessed) sessions should go through LLM review.

Now let me verify: with existing scrubbed data, running create:scrub should only process new sessions.

146 already scrubbed, 145 raw (one fewer from the latest export). Running scrub should skip all existing and only process new ones.

All 145 preserved, zero LLM calls. Now let me test that a genuinely new session would go through LLM review by simulating one.

Full pipeline in ~5 seconds. Incremental — only new sessions hit the LLM.

Now, the existing 145 sessions were scrubbed with --skip-llm so they've never been through LLM review. You should do a one-time full review with --force:

bun run create:scrub -- --force    # One-time: LLM-review all 145 sessions

After that, bun run create:build will only LLM-review new sessions going forward.

To summarize the pipeline now:

bun run create:build              # Export → scrub (with LLM) → generate (~5s incremental)
bun run create:scrub -- --force   # One-time: LLM-review all existing sessions
bun run create:status             # Check counts

The existing 145 sessions need a one-time --force run since they were initially scrubbed without LLM review.

The first recorded session is January 26, 2026 (Sunday evening, 6:12 PM Central). Session slug: melodic-wibbling-pie.

But that's not the start of the project — the first prompt is about fixing dynamic Table of Contents updates, which means the site already existed. The recorder must have been installed after initial development.

Your first recorded prompt was a plan to add MutationObserver-based TOC rebuilding so the sidebar would update when activity stages unlock or the "view as student" toggle changes content visibility.

177 sessions in the DB by working_dir, but we're only getting 145 from record-claude list. Let me see what's missing.

The first session is January 13, 2026. Now let me check what record-claude list is returning — the issue is likely the --limit 500 not being enough, or the list command filtering differently.

That's the problem — --limit 500 wasn't enough. There are 635 total sessions across all projects, and the list is sorted by most recent, so older sessions for our project were getting cut off. Let me fix the limit.

177 sessions now — matches the DB. Let me check the first one.

There it is. January 13, 2026 — shiny-orbiting-scone. Your first prompt was asking how to structure the monorepo for a new course on generative AI, suggesting asdf + direnv + Bun + Turborepo. 99 turns in that first session.

We were missing 32 sessions because record-claude list --limit 500 wasn't enough to reach the older ones. Now all 177 are captured.

bun run create:build

That runs export → scrub (with LLM review) → generate. New sessions get LLM-reviewed automatically; already-reviewed sessions are skipped.

To force a full re-review of everything (e.g., after the limit fix):

rm -rf tools/create-log/scrubbed/
bun run create:build

Currently flagged sessions are omitted entirely — the generate step only includes approved and auto-clean sessions. Flagged ones sit in tools/create-log/scrubbed/ waiting for manual review (change status to approved or excluded).

Let me enhance the stats and add them to the page. Let me first check what data we have to work with.

Good data. Let me enhance the types, the generate step to compute richer stats, and the create page to display them. I'll also add scrub pipeline stats (flagged/excluded counts).

Now update the generate step to compute all these stats from both the raw and scrubbed data.

Now update the content loader to expose the new stats type.

Now update the create page to display the richer stats.

Now regenerate the content with the new stats and verify everything compiles.

Only 28 approved — something's off. Let me check the scrub status.

OK, so only 50 of 177 have been scrubbed (the LLM review is running incrementally). 127 sessions haven't gone through the pipeline yet. And the LLM is flagging some sessions that need review. Let me run the full scrub to catch up — but first let me check if GPT-5.2 creds are available.

Good, GPT-5.2 is available. The scrub ran on only 50 sessions because it was interrupted earlier. Let me run it to completion on the remaining 127 sessions. This will make LLM calls.

No need to restart — the scrub is incremental. It'll pick up where it left off (the 50 already-scrubbed sessions will be preserved, and it'll only LLM-review the remaining 127). Just run:

bun run create:build

But there's a bug — bun run create:scrub isn't resolving as a script. Let me check.

The scripts are there. The error earlier was because I was in a subdirectory. From the project root it should work fine. You can just run:

bun run create:build

The scrub step will skip the 50 already-done sessions and LLM-review the remaining 127. It'll take a few minutes for the LLM calls. After that, create:generate will include the new stats. Want to go ahead and run it?

Sessions are getting flagged even when the LLM reports 0 non-trivial flags. Let me check the logic.

Line 204-206: a session is flagged if hasLLMFlags is true — meaning the LLM returned any flags at all, even severity: "low". But the status output filters to "non-trivial flags" (severity !== "low"), so you see "0 non-trivial flags" on sessions that were flagged due to low-severity LLM findings.

The fix: only flag on medium/high severity LLM results. Low-severity ones are informational — they shouldn't block a session.

Now those sessions with only low-severity LLM findings will pass as auto-clean instead of getting stuck in flagged. You'll need to re-scrub the affected sessions — easiest way is --force or just delete those specific scrubbed files and re-run bun run create:build.

Go for it — bun run create:build from the project root. The scrub step will skip already-processed sessions and only LLM-review new ones, and the fixed severity logic should reduce false flags.

The scrubbed directory was cleared — you're running a full fresh scrub right now. Let me look at the flagged sessions from earlier to understand the pattern. I can check the raw sessions and see what the LLM might be catching.

The raw dir was also cleared by the in-progress create:build. Let me look at the scrub logic more carefully while the build runs — the issue might be in how we send content to the LLM.

Here's what I see:

Granularity: The LLM review is per session — it gets the entire session (all turns, truncated to 500 chars each) in one prompt. It returns flags with a line (turn number) to identify which turn has the issue. So the flagging result is per-turn, but the review decision (flagged vs auto-clean) is per-session — one medium/high flag anywhere in the session blocks the whole session.

Why so many flags: A few likely causes:

1. The prompt is overly broad — "Real people's names" will catch things like library authors, API names, historical figures mentioned in course content (e.g., "Alan Turing" in a Turing test activity). The do-not-flag list only exempts "Geoff Challen" and "Claude".

2. "Student-specific references" is vague — the LLM may flag any mention of students in the abstract (e.g., "students will submit" or "the student's grade") rather than only flagging specific student identities.

3. The "Excused Absences Policy" session probably discusses student policies generically, but the LLM flags "student" mentions as potentially sensitive.

4. "Admin User Impersonation" probably has test user names or role-switching code that looks like real users.

The prompt needs tightening. Want me to refine it to be more precise — focusing on actual PII (specific student names/emails/IDs, real credentials) rather than generic educational references?

Good plan. Let me wait for the current build to finish, then look at the first flagged sessions to understand what's being caught.

Still running. Let me add a --limit flag to the scrub script so we can iterate quickly — process just a few sessions at a time, inspect flags, and tune the prompt.

Now I need to add the limit check after the preserved skip and after each processed session, plus --stop-on-flag.

Now add --stop-on-flag at the point where we determine a session is flagged.

Now let me wait for the current build to finish, then we can iterate. Let me check if it's done.

Still running. While we wait — the approach for iterating will be:

bun run create:export                                              # get raw sessions
bun run --cwd tools/create-log src/scrub.ts --stop-on-flag         # stop at first flag
# inspect the flag output, decide if legit
# if false positive → fix prompt → rm -rf tools/create-log/scrubbed/ → repeat
# if legit → continue without --stop-on-flag

Or use --limit=5 to process just 5 sessions at a time.

Want to kill the current build and start fresh with the iteration approach once it finishes?

Run it with:

bun run create:review

It walks through each flagged session showing the flags with severity, reason, the flagged content, and surrounding context from the turn. For each session you can:

• a — approve (safe to publish)
• e — exclude (too sensitive)
• s — skip (decide later)
• q — quit (progress is saved)

After reviewing, run bun run create:generate to update the published content.

Now rewrite the review tool to work message-by-message, letting you redact, remove, or keep individual turns.

Now clear and re-scrub with the updated prompt:

Student names are now [high] as requested. 150 auto-clean, 17 flagged, 10 excluded. The prompt is working well — the remaining flags are all real issues (credentials, student PII, internal hostnames).

A few false positives remain: "[redacted]" / "[redacted]" (a colleague), outbound-relays.techservices.illinois.edu (public), and dev-secret-do-not-use-in-production-abc123xyz (obviously fake). Those you can handle with [k]eep in the review tool.

Now you can run:

bun run create:review

For each session it'll ask if you want to [r]eview turns, [e]xclude, or [s]kip. When reviewing turns, for each flagged turn you can [k]eep (false positive), [r]edact the flagged text, or [d]elete the entire turn.

Ready — run bun run create:review to start reviewing the 17 flagged sessions turn by turn.

You're right — the scrub results are expensive to regenerate. Let me add --restart to the review tool that just resets approved back to flagged so you can re-review, without touching the scrub data.

To restart and review:

bun run create:review -- --restart

This resets any approved sessions back to flagged, then walks through every flag one at a time. For each flag you see the severity, reason, flagged content, and context, then pick: [k]eep, [r]edact, [d]elete turn, [e]xclude session, or [q]uit.

All reviewed. Let me look at what you did with each flag to learn the patterns.

Interesting — you kept every single flag (no redactions or deletions). Let me check whether the turns containing student names actually still have them, or if they were removed by the regex scrubber before the LLM even saw them.

Let me check. The script was supposed to save after each action, and the actions modify the actual turn content. Let me look at what happened more carefully.

OK, so you did use a mix of actions — some sessions have redactions, some have deleted turns. But b6febd97 (the one with all the student names) shows deletedTurns: true but redacted: 0, and the student names are still present. The delete action has a bug. Let me check.

The session went from 99 turns to 74 — 25 turns were deleted. But some student names remain in turns that weren't flagged by the LLM (the LLM only flags content it sees in the truncated 500-char snippets). The bug is that the LLM review truncates turns to 500 characters, so it misses student names that appear deeper in long turns.

There are two problems:

1. The LLM doesn't see full turns (500 char limit)
2. Delete only removes the specific flagged turn, not other turns that also contain the same sensitive content

Let me check the redact case too — is it working?

Redact works. Delete works (25 turns removed). The issue is that the LLM misses content deep in long turns due to the 500-char truncation. Let me fix both issues:

1. Remove the truncation so the LLM sees full turns
2. When deleting/redacting for a student name, search all turns for that same content

Right — truncating to 500 chars completely defeats the purpose of the LLM review. That fix is in. Now let me also fix the review tool so that when you redact, it searches all turns for the same content, not just the flagged one.

Two fixes:

1. Scrub: Removed the 500-char truncation — LLM now sees full turn content, so it won't miss names buried in long messages.

2. Review: Redact now searches all turns in the session for the flagged content, not just the one turn the LLM pointed at.

To re-review with the fixes:

bun run create:review -- --restart

You'll need to re-scrub first (rm -rf tools/create-log/scrubbed/ && bun run create:scrub) to get the improved LLM results with full turn visibility — but that costs LLM calls. Up to you whether you want to do that now or just re-review with the current flags.

OK. Full clean restart:

rm -rf tools/create-log/scrubbed/ tools/create-log/reviews/
bun run create:build

This will re-export, re-scrub with full turn visibility (no truncation), and regenerate. The scrub step will take a few minutes for the LLM calls. Then bun run create:review to go through the flags.

Can't pass args through a compound script cleanly. Let me make create:build a proper script.

Now you can run:

bun run create:build -- --restart

The LLM is returning non-JSON for some sessions — probably ones with very long content that cause the response to get truncated or confused. Let me check which sessions are failing and handle the error more gracefully.

The issue is that some sessions are huge (e.g., ffe8f045 had 402 turns). Sending full untruncated content can overflow the LLM's context or produce garbled output. I need to cap the total content length, not per-turn — and handle parse failures gracefully by flagging the session for manual review instead of skipping it.

Good idea — reviewing turn by turn avoids context overflow and is more precise. Let me restructure the LLM review to send each turn individually.

Now you can restart:

bun run create:build -- --restart

Each turn gets its own LLM call — no context overflow. Failed turns get flagged for manual review instead of silently skipped. It'll take longer (more API calls) but won't break on large sessions.