transcription

transcription is a skill for Claude Code, Codex from gabrielmoreira/agent-skills-mirror. It costs 68 tokens per session (1,009 once invoked), scanned A, original, MIT.

A video and audio transcription tool that turns speech into searchable text, captions, or subtitles.

In plain words
What is it for?
Use it to transcribe media, search dialogue, add captions, create subtitles, or prepare bilingual subtitles.
Why use it?
It provides timed text so spoken content can be found, reviewed, and displayed accurately. It also supports checking whether transcription is ready before using it.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to transcribe media, search dialogue, add captions, create subtitles, or prepare bilingual subtitles.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gabrielmoreira/agent-skills-mirror/transcription
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.

Any agent
npx skills add gabrielmoreira/agent-skills-mirror --skill transcription
Clone the repo
git clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirror

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for transcription

README.md
[![agentmods](https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/transcription/github.svg)](https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/transcription)
Your own site
<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/transcription"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/transcription/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for transcription

Your own site · 80×15
<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/transcription"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/transcription.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,009 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 30
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 5.1 $0.00068 $0.01009
Opus 5 $0.00034 $0.00504
Sonnet 5 $0.00014 $0.00202
Haiku 4.5 $0.00007 $0.00101

Measured 12d ago against content hash 4a47ef20557e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

transcription 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 12d ago.

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.

mirrors/repos/0xsline@OpenChatCut/src/agent/skills/transcription/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Transcription

For newly imported local/client-held media, use import_media to start transcription, then wait with track_progress.

Typical flow:

  1. read_project with view: "assets" to get the video/audio asset ID and transcript status.
  2. If this is a fresh client-held import, make sure it went through import_media action=create_session plus the OpenChatCut media import helper.
  3. Call track_progress with action:"wait", target:"transcription", and assetIds set to the asset ID or prefix.
  4. Use find_transcript to search transcript text and confirm word timestamps.
  5. Use edit_captions action enable or read_captions as needed once transcription is ready.

Example:

{
  "action": "wait",
  "target": "transcription",
  "assetIds": "13c1aa02cd"
}

Uploaded assets start ASR automatically on ingest, but nothing waits for it. Always use track_progress for readiness.

For local-only video assets with local-only; original upload deferred in read_project, transcription cannot run until the bytes are reachable by the backend. Import the source again via the asset-import skill (which uploads to S3) or download_media from a public URL; do not ask the user to relink it manually in the editor.

Stuck Transcription And Retry

Do not declare transcription stuck from one non-terminal status. Base the decision on both asset length and the time the agent has actually waited in this task.

  1. Read the asset with read_project view: "assets" and note its duration when available.
  2. Start counting elapsed wait time from the first track_progress action:"wait" or from the earliest reliable in-task timestamp where the agent observed transcription as pending/running.
  3. If transcription reports an explicit failed, errored, or timed-out terminal state, retry immediately after confirming the asset is remote-ready and is video/audio.
  4. If transcription remains pending/running with no failure, treat it as stuck only after elapsed wait time exceeds max(5 minutes, min(60 minutes, 2 × asset duration)). For example, wait at least 5 minutes for a 30-second clip, about 20 minutes for a 10-minute asset, and about 60 minutes for a 1-hour or longer asset.
  5. If duration is unknown, wait at least 10 minutes across more than one track_progress call before treating it as stuck, unless the tool reports an explicit failure.

Read the full file on GitHub · 65 lines

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. 12d ago First seen · 65 lines · 68 tokens per session scan A 4a47ef20557e

Subscribe to this mod's changes

transcription is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 1,009 once invoked, about $0.0003 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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