Transcript Fixer

Use a two-phase loop:

1. Stage 1 applies deterministic, already-known corrections.
2. Native AI Correction reads the complete transcript, fixes one-off errors, verifies uncertain entities, and compounds reusable fixes.

Native AI Correction is the default. Stage 1 alone is incomplete. Stage 3 API exists only for automation that has no Claude/Codex agent available.

Operating contract

• Finish Stage 1 → Native AI Correction → compound confirmed recurring fixes. Do not report a transcript clean after Stage 1 alone.
• Skip Native AI only when the human explicitly limits this run to the dictionary pass or a dated artifact proves Native AI already ran on this exact transcript.
• In Claude Code or Codex, do not run Stage 3. Use Stage 1 plus the native workflow.
• Never rewrite speech for fluency. A correction must explain a plausible ASR error and preserve who said what.
• Never infer or reassign speaker identities. Preserve speaker-label lines; human-confirmed labels and user verdicts are authoritative.
• Leave unresolved text unchanged and enqueue it. A visible garble is safer than a fluent wrong guess.
• Treat a single-line asr_note value as correction provenance: it intentionally cites old forms and is excluded from matching. Multi-line YAML ledger values are not masked; keywords, titles, other ASR-derived metadata, and body text remain in correction scope.
• Read references/native_ai_full_workflow.md in full before performing a native pass. Read the task-specific references named below before their corresponding action.

Run context

Run every entrypoint through uv run; entrypoints that need third-party Python packages declare them with PEP 723, while stdlib/internal-only utilities may omit the metadata block. Execute commands from the skill directory printed when this skill was invoked, or prefix every script path with that directory. Do not rely on $CLAUDE_SKILL_DIR; it is not available in every harness.

If the bundle location is genuinely unknown, use the installation-resolution procedure in references/installation_setup.md. Do not select the first result from a broad find: caches, backups, and old versions can coexist.

Quick start

# Initialize once
uv run scripts/fix_transcription.py --init

# Stage 1 for one project domain. --apply-domain trusts that explicitly
# selected, human-curated project domain at every risk level.
uv run scripts/fix_transcription.py \
  --input meeting.md --stage 1 \
  --domain myproject --apply-domain --json

# Several sibling domains may be loaded as one union.
uv run scripts/fix_transcription.py \
  --input meeting.md --stage 1 \
  --domain myproject,myproject-alt --apply-domain --json

# Preview without writing the Stage 1 output.
uv run scripts/fix_transcription.py \
  --input meeting.md --stage 1 --domain myproject --dry-run

# Scan all documented context traps after the native read-through.
uv run scripts/fix_transcription.py --scan-traps \
  --context-file ~/.transcript-fixer/contexts/myproject.md \
  --input meeting.md

Safe mode is the Stage 1 default: low-risk rules apply; medium/high-risk matches defer to *_needs_review.md and the persistent review queue. Applied: 0 is a valid result, not proof that the transcript is clean.

The Stage 1 JSON contract is:

{
  "applied": 0,
  "deferred": 0,
  "output_path": null,
  "needs_review_path": null,
  "input_unchanged": true,
  "review_enqueued": 0,
  "stage1_only_incomplete": true,
  "stage2_total_chunks": 0,
  "stage2_failed_chunks": 0,
  "stage2_degraded": false
}

Read all ten fields. stage1_only_incomplete is additive to the original six-field caller contract and must remain true for a Stage 1 script run; only the caller can close it by running Native AI, or by explicitly choosing the agent-less Stage 2/3 route. The three stage2_* telemetry fields are always present: Stage 1 reports 0, 0, and false; Stage 2/3 replace them with the actual API outcome. Do not infer no-op or success from whether a sidecar exists.

For a native end-to-end example, read references/example_session_dji_minutes.md.

Choose the route

Use vocabulary and stakes as the primary tier signals; use length only as a tiebreaker. A five-minute medical interview can require the full tier, while a long plain two-person memo can use the fast tier.

Native correction checklist

1. Give the file its final name before Stage 1. Queue anchors store absolute paths. Use a human-readable project filename before any deferral can enqueue. When the input arrives as inline text with no file yet — a slash-command argument, a pasted block — write it to a file before anything else; --input and the queue anchors both need a path, and a scratch location is fine when nothing downstream will archive it. No --domain given and none obvious from context? Omitting the flag already defaults to searching every domain (--domain's own default), so don't block on picking one — run Stage 1 bare and let safe mode gate what auto-applies. If a specific candidate still needs resolving, one step of the ladder is cheap enough to keep even at fast tier though the rest of it isn't: native_ai_full_workflow.md step 4's rung 1, a single cross-domain corrections.db lookup — not the full verification ladder the tier table tells you to skip, just that one query.

2. Recover the raw baseline before reading a pre-corrected transcript. If an ingest pipeline or previous API pass already touched the text, diff against the raw source first. Judge upstream edits as edits, not as ground truth.

3. Load project priors and read the complete transcript. Read ~/.transcript-fixer/contexts/<domain>.md when present, then read the whole file before deciding early ambiguities.

4. Run Stage 1 and inspect the real result. Prefer explicit project domains plus --apply-domain --json. Read deferred and review_enqueued; never silently discard the sidecar or queue gap.

5. Diff Stage 1 against raw/original. If a rule changed correct speech, work from the original, retire the stored pair with --report-false-positive "<from>" "<to>" --domain <domain>, and verify it no longer fires.

6. Triage every candidate.

   • Confident: the sound change is plausible and context or an authoritative local source settles it.
   • Needs verification: a person, company, product, model, ticker, place, number, or other load-bearing term without a source.
   • Uncertain: evidence does not settle it; leave the original and enqueue.

7. Apply the smallest edit that explains the sound. Do not add words the speaker did not say. Correct ASR-derived metadata too, while leaving asr_note intact.

8. Run a second pass.

   • Every tier: run --scan-traps and inspect both hits and unparsed.
   • Full tier: use a fresh-context reviewer on exactly one corrected file. Require a compact residual table or explicit no new residuals; an empty/truncated response is a failed review.

9. Enqueue every unresolved item. Follow Review queue safety below and review_queue_dashboard.md.

10. Verify and finalize. Diff the file actually edited, run numeric consistency when numbers matter, rerun plain Stage 1, re-grep known corrections, and confirm every change traces to a triage decision.

11. Compound the learning in the same turn. Route each stable pattern to its correct home; do not leave confirmed fixes only in chat. Native-pass edits never reach Stage 1's correction history, so harvest them mechanically right after the final diff:

    # Diff raw vs corrected into parseable trap candidates (review artifact —
    # you adjudicate the printed list; --write auto-appends only the recurring
    # (≥2x) non-bare candidates; --write-all also appends the one-off set)
    uv run scripts/harvest_corrections.py raw.md corrected.md \
      --context-file ~/.transcript-fixer/contexts/<domain>.md
    

    Every emitted bullet is round-trip verified through the real trap parser before printing, and pairs already documented in the context file are skipped. High-frequency candidates are strong traps; single-occurrence ones need a human judgment — that is why --write leaves them out by default — and ⚠️ 裸形 candidates are never auto-written. This replaces hand-writing trap bullets from memory.

12. Propagate entity fixes deliberately. Search only the owning project’s derived notes/summaries, review every hit, and exclude raw ASR and correction sidecars because they preserve the evidence trail.

The detailed provenance bar, local-first entity ladder, second-pass prompt, queue payload, and finalization rules are in references/native_ai_full_workflow.md.

Cross-skill caller contract

A caller pipeline has two independent obligations:

1. Run Stage 1 with the explicitly configured project domain(s), --apply-domain, and --json. If deferred > review_enqueued, persist the review sidecar outside any temporary directory or surface the gap as failure.
2. Run Native AI with this skill loaded, or report Stage 1 only — incomplete. Agent-less automation may use Stage 3 instead.

Canonical call:

uv run scripts/fix_transcription.py \
  --input "$staged" --stage 1 \
  --domain "$domains" --apply-domain --json

A caller that wires only the script path never loads this contract. Script-path integration alone is therefore a Stage 1 prefilter, not transcript correction.

Keep project domains warm: every confirmed recurring correction from the native pass must be added back to the correct project domain, roster, or context file.

Dictionary and identity safety

Read references/false_positive_guide.md and references/dictionary_identity_and_context.md before adding a rule.

A context trap is a cue, not permission to replace blindly. --scan-traps supports canonical → and legacy ≈ mappings with the same directional contract: left is observed ASR, right is intended text. Wrap an exact FROM phrase containing spaces in backticks:

- **`CC 思维链`/`CC 思维连` → 目标术语** — only under the domain's documented cue

This demonstrates an exact ASR phrase candidate, not a person-name candidate. The domain context remains the authority for the real target and cue; the scanner only locates the literal FROM forms.

Before adding any real-word-shaped rule, measure the project corpus:

uv run scripts/fix_transcription.py \
  --probe "candidate" --corpus /path/to/project-transcripts/

uv run scripts/fix_transcription.py \
  --add "candidate" "canonical" --domain myproject \
  --check-corpus --corpus /path/to/project-transcripts/

User verdicts compound immediately: fix the file, update the proper destination, and record confirmed-correct results so later runs do not ask again.

Review queue safety

Read references/review_queue_dashboard.md before enqueueing or resolving.

Minimum item:

[
  {
    "file": "/absolute/path/to/transcript.md",
    "line": 142,
    "original": "<suspect-token-only>",
    "suggested": "<best-candidate>",
    "kind": "entity",
    "context": "<verbatim whole sentence>",
    "evidence": "<what was checked>"
  }
]

Safety rules:

• file is mandatory for this workflow. Without it, acceptance can record a verdict without editing the transcript.
• original is only the suspect token/span; never put the whole sentence there.
• context is copied verbatim; line is the key, not line_hint.
• suggested is the key, not suggestion. Use actions, not action_pack.
• Resolve one occurrence at a time; sweep sibling entity occurrences only after the whole batch is resolved.
• Read resolved_text after an override; the listing can still display the rejected suggestion.
• If the file moved or drifted, run --reanchor-review. Add --reanchor-root or --reanchor-to when requested. Do not hand-edit around a pending item.
• Promote every decision_note by meaning; storing a note does not change the dictionary, roster, context, or false-positive state.

Core commands:

uv run scripts/fix_transcription.py --enqueue-review items.json
uv run scripts/fix_transcription.py --list-review --review-status all --json
uv run scripts/fix_transcription.py --show-review <id> --json
uv run scripts/fix_transcription.py --reanchor-review <id>
uv run scripts/fix_transcription.py \
  --resolve-review <id> --decision accepted --by reviewer

Numbers, artifacts, and batches

Read references/advanced_correction_evidence.md when any of these conditions holds:

• A number, bound, price, share, deadline, or magnitude drives a decision.
• Two recordings exist for one meeting.
• A whiteboard, slide, or photographed written artifact can independently settle a name/term.
• Several related files should share one correction list.
• A 10+ file batch is being delegated.

Numeric-slot scan:

uv run scripts/scan_numeric_consistency.py transcript.md --domain myproject

Its output is candidates, never automatic edits. For a single load-bearing number, wire the original audio and decide by ear through the review dashboard.

For delegated batches, every agent owns one file, cannot cross-file replace, and returns a residual list. Afterward compare git diff --name-only with the explicit file list and inspect every unexpected file under the repository's worktree-safety rules.

Finalization

• Native mode edits the original file directly. Rerun plain --stage 1 to confirm; a clean no-op writes no Stage 1 sidecar.
• When a newer *_stage1.md exists and the original was not edited after it, a plain Stage 1 rerun atomically promotes it and removes disposable sidecars. It retains *_changes.md and *_needs_review.md because only the reviewer can know that every associated decision is closed. --apply-all never takes this promotion path.
• Do not use the existence of an output file as the success signal; read JSON/exit status and independently read the final file.
• Preserve raw transcripts, *_changes.md, and *_needs_review.md as evidence until every associated decision is closed.
• Re-grep a known corrected form in the final file and verify no correction remains only in asr_note or a sidecar.
• If a queued item was renamed away, repair it with --reanchor-review rather than resolving it with a false terminal verdict.

Agent-less API route

Only when no Claude/Codex agent can perform Native AI Correction:

export GLM_API_KEY="<api-key>"
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected

Read references/glm_api_setup.md, references/installation_setup.md, and the explicitly API-oriented portions of references/workflow_guide.md. When a chunk fails after retries, the API route keeps that chunk and its original surrounding separators byte-for-byte and prints a warning; if every chunk fails, the complete output equals the input. For fix_transcription.py --stage 2|3 --json, read the additive stage2_total_chunks, stage2_failed_chunks, and stage2_degraded fields: stage2_degraded: true is not a fully corrected run even though the safely retained artifact is emitted. The enhanced wrapper exits nonzero after writing that retained artifact when any Stage 2 chunk is degraded. Verify the output rather than assuming the warning means a corrected result exists.

The enhanced API wrapper can also add paragraph breaks, reduce repeated filler, and present corrections for interactive review. Those are API-wrapper features; they do not authorize Native AI to rewrite wording for fluency.

Utility commands

# Extract likely errors without editing
uv run scripts/fix_transcription.py --extract-uncertain \
  --input meeting.md --output ./review

# Import curated preset rules
uv run scripts/fix_transcription.py --load-presets tech

# Repair timestamps
uv run scripts/fix_transcript_timestamps.py meeting.txt --in-place

# Split and rebase sections
uv run scripts/split_transcript_sections.py meeting.txt \
  --first-section-name "intro" \
  --section "main::<verbatim marker>" \
  --rebase-to-zero

# Word-level review diff
uv run scripts/generate_word_diff.py original.md corrected.md output.html

# Harvest native-pass edits into context-trap candidates
uv run scripts/harvest_corrections.py raw.md corrected.md \
  --context-file ~/.transcript-fixer/contexts/myproject.md --write

# Multi-format Stage 1/API comparison report
uv run scripts/generate_diff_report.py \
  original.md original_stage1.md original_stage2.md \
  --output ./diff_reports

# Setup health
uv run scripts/fix_transcription.py --validate

Read references/script_parameters.md before using less-common flags. Read references/database_schema.md before custom SQL; correction columns are from_text and to_text.

Reference map

All references are one level from this file.

Bundled scripts are executed, not loaded into context. The primary entry points are fix_transcription.py, scan_numeric_consistency.py, fetch_minute_audio.py, review-dashboard/server.py, and the diff/timestamp/splitting utilities listed above.

Handoff

After correction, hand off to /daymade-audio:meeting-minutes-taker only when the user wants a structured summary. Do not create meeting minutes automatically: transcript correction and summarization are separate scopes.