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.
npx skills add sugarforever/01coder-agent-skills --skill subtitle-correctiongit clone --depth 1 https://github.com/sugarforever/01coder-agent-skillsWrote 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.
[](https://agentmods.dev/skills/sugarforever/01coder-agent-skills/subtitle-correction)<a href="https://agentmods.dev/skills/sugarforever/01coder-agent-skills/subtitle-correction"><img src="https://agentmods.dev/badge/skills/sugarforever/01coder-agent-skills/subtitle-correction.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.02431 |
| Opus 5 | $0.00036 | $0.01215 |
| Sonnet 5 | $0.00015 | $0.00486 |
| Haiku 4.5 | $0.00007 | $0.00243 |
Grade A, and why
subtitle-correction 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subtitle Correction Skill
This skill corrects speech recognition errors in subtitle files while strictly preserving timeline information.
Interactive Workflow
Step 1: Request Terminology from User
IMPORTANT: Before starting any correction, ALWAYS ask the user for domain-specific terms.
Prompt the user with:
在开始修正之前，请提供一些关键术语，帮助我更准确地识别和修正语音识别错误：
1. **专有名词**：人名、品牌名、产品名等（如：Anthropic、Claude）
2. **技术术语**：框架、库、工具名称（如：LangChain、OpenAI、PyTorch）
3. **领域词汇**：行业特定词汇（如：checkpointer、middleware、runtime）
4. **其他关键词**：视频中频繁出现的重要词汇
请用逗号分隔，例如：`LangChain, Agent, OpenAI, checkpointer`
For English users:
Before I begin correction, please provide key terms to help me accurately identify speech recognition errors:
1. **Proper nouns**: Names, brands, products (e.g., Anthropic, Claude)
2. **Technical terms**: Frameworks, libraries, tools (e.g., LangChain, PyTorch)
3. **Domain vocabulary**: Industry-specific terms (e.g., checkpointer, middleware)
4. **Other keywords**: Important words that appear frequently
Please separate with commas, e.g.: `LangChain, Agent, OpenAI, checkpointer`
Step 2: Confirm Understanding
After receiving terms, confirm by:
- Listing the terms received
- Identifying the likely domain/context (AI/ML tutorial, web dev, etc.)
- Asking if there are any additional terms before proceeding
Example response:
收到以下术语：
- 技术框架：LangChain, LangGraph, OpenAI
- 技术概念：Agent, checkpointer, runtime
看起来这是一个 LangChain 智能体开发的教程视频。
还有其他需要补充的术语吗？如果没有，我将开始修正字幕。
Step 3: Process with Terms
Use the provided terms to:
- Build a mental model of expected vocabulary
- Identify likely speech recognition errors
- Apply consistent corrections throughout
When User Doesn't Provide Terms
If user says "没有" / "no" / "直接开始":
- Proceed with correction using built-in patterns
- Flag uncertain corrections for user review
- After completion, ask if any terms were missed
Core Workflow
- Read the subtitle file - Load the .srt file provided by the user
- Identify error patterns - Recognize common speech recognition mistakes
- Apply corrections - Fix errors while preserving timestamps exactly
- Output corrected file - Return or save based on user's context
What ships with it
3 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.
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.
- 7d ago First seen · 307 lines · 73 tokens per session scan A 9a14cecadd23
subtitle-correction is a skill published in the GitHub repository sugarforever/01coder-agent-skills (133 stars, last pushed 2mo ago), licensed MIT. It adds 73 tokens to every session and 2,431 once invoked, about $0.0004 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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