agentmods.dev

transcript-fixer

A transcript-cleaning workflow for correcting speech-to-text mistakes while preserving the original wording, speakers, and confirmed names.

In plain words
What is it for?
Use it to clean dictated or recorded transcripts, verify uncertain names and entities, and save confirmed recurring corrections for later transcripts.
Why use it?
It combines known dictionary fixes with a complete AI review, so one-off recognition errors are not missed and speaker identities are not guessed.

Skill for Claude CodeCodex

1.4k +6 changed today A scan 190 tokens original MIT
View the source file daymade/claude-code-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/daymade/claude-code-skills/transcript-fixer
Any agent
npx skills add daymade/claude-code-skills --skill transcript-fixer
Clone the repo
git clone --depth 1 https://github.com/daymade/claude-code-skills

Made for: Claude Code, Codex.

Per session 190 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,959 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00190 $0.05959
Opus 5 $0.00095 $0.02979
Sonnet 5 $0.00038 $0.01192
Haiku 4.5 $0.00019 $0.00596

Measured today against content hash d44be72a1357, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

transcript-fixer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

The scan reads SKILL.md. This mod also ships 33 executable files (scripts/__init__.py, scripts/check_type_hints.py, scripts/cli/__init__.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

daymade-audio/transcript-fixer/SKILL.md · 340 lines

How it starts

The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.
  • Before correcting any person name, directly read both the configured global people roster and the owning project's explicit identity roster or alias ledger. Stage 1 auto-loads only global ASR 变体 entries; it does not load project rosters or expose suppressed, disabled, and unlisted entries. If an expected source is missing or the sources conflict, leave the name unchanged and enqueue or ask once. Never use occurrence frequency as identity evidence. Read references/dictionary_identity_and_context.md before settling the name.
  • Leave unresolved text unchanged and enqueue it. A visible garble is safer than a fluent wrong guess.
  • Treat an unfamiliar token as unknown, not as an error. Exhaust the local evidence ladder first. For a load-bearing token that remains unresolved, use the clip-level cross-recognizer rung only when source audio and a permitted second engine are already available; otherwise enqueue or ask. Agreement from a genuinely different recognizer family strongly corroborates the sound, but never chooses between homophonic spellings or overrides the person-name gate. Read native workflow step 4, rung 7 before using it.
  • 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.

Read the full file on GitHub · 340 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed · +1 lines d44be72a1357
  2. 2d ago First seen · 339 lines · 190 tokens per session scan A 9afda989a724

Subscribe to this mod's changes

transcript-fixer is a skill published in the GitHub repository daymade/claude-code-skills (1,367 stars, last pushed today), licensed MIT. It adds 190 tokens to every session and 5,959 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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