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transcript-pipeline转录管道

Agent Skill

transcript-pipeline 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

190

周安装

8

GitHub Stars

公开资料未说明

下载量

67
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:transcript-pipeline(转录管道)
来源仓库:https://github.com/prakharmnnit/skills-and-personas
仓库路径:skills/transcript-pipeline
安装命令:
npx skills add https://github.com/prakharmnnit/skills-and-personas --skill transcript-pipeline
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/prakharmnnit/skills-and-personas --skill transcript-pipeline

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

Transcript Pipeline Skill

Run a deterministic, auditable transcript-to-tutorial workflow with optional resource enrichment.

Purpose

Use this skill to convert raw class captions into high-quality study notes while preserving accountability through ledger + validation artifacts.

Use scripts for deterministic work. Use chat/stage prompts for language-heavy transformation.

Core Contract

  1. Keep stage order: ingest -> refine -> synthesize -> enhance -> validate -> publish.
  2. Run deterministic gates with scripts, never with LLM self-certification.
  3. Preserve traceability in .pipeline/* artifacts.
  4. Keep learner-facing notes readable and sanitized.
  5. Treat validation status as PASS/FAIL source of truth.

Scripts

Use these scripts from scripts/:

  • ingest_zoom_captions.py - deterministic ingestion and segment ledger creation
  • run_chat_pipeline.py - guided orchestration for stage handoffs and validation
  • validate_coverage.py - hard-gate coverage validation
  • publish_tutorial_notes.py - learner-facing file naming and sanitization
  • merge_chunks.py - merge chunk outputs for large transcripts
  • run_colab_notebook_pipeline.py - AI/ML Colab appendix and code explainer pipeline
  • update_ai_notes_with_resources_and_colab.py - AI/ML notes enrichment utility
  • resource_enrichment.py - authenticated enrichment for Notion/Canva/Drive resources

Stage Workflow

Stage 0: Ingest (Deterministic)

Run:

python scripts/ingest_zoom_captions.py "<transcript_or_session_path>"

Required outputs:

  • .pipeline/segment_ledger.jsonl
  • .pipeline/segment_manifest.jsonl

Stage 1: Refine (Chat Stage)

Load references/stage1-refine.md.

Produce:

  • .pipeline/refined_transcript.md
  • .pipeline/topic_inventory.json
  • .pipeline/corrections_log.csv
  • .pipeline/uncertainty_report.json

Stage 2: Synthesize (Chat Stage)

Load references/stage2-synthesize.md.

Produce:

  • .pipeline/structured_notes.md
  • .pipeline/coverage_matrix.json

Stage 3: Enhance (Chat Stage)

Load:

  • references/stage3-enhance.md
  • references/tutorial-tech-bar-raiser.md

Produce:

  • .pipeline/enhanced_notes.md
  • final_notes.md
  • bootcamp_index.md

Stage 4: Validate (Deterministic)

Run:

python scripts/validate_coverage.py --pipeline-dir .pipeline

Validation guidance: references/stage4-validate.md.

Hard gates:

  1. Segment coverage accountability
  2. Uncertainty retention
  3. No orphan claims

Stage 5: Publish

Run:

python scripts/publish_tutorial_notes.py --root "<sessions_root>" --session-dir "<session_dir>"

Result:

  • Published tutorial filename in canonical format
  • Learner-safe note without noisy source tags
  • Updated course index links

One-Command Guided Mode

Use guided runner for chat-window workflows:

python scripts/run_chat_pipeline.py run "<transcript_or_session_path>" --deep-pass

This enforces required handoffs and deep quality gates.

Optional Resource Enrichment Stage

Run when class notes include external links (Notion/Canva/Drive):

python scripts/resource_enrichment.py --all-sessions

Single session:

python scripts/resource_enrichment.py --session-dir "<session_dir>"

Auth options:

  • Notion: NOTION_TOKEN_V2, NOTION_ACTIVE_USER
  • Canva: RESOURCE_PLAYWRIGHT_STORAGE_STATE

Reference: references/resource-enrichment-authenticated-flow.md.

Optional AI/ML Colab Enrichment

Run for Colab-backed AI/ML classes:

python scripts/run_colab_notebook_pipeline.py

Reference: references/colab-notebook-explainer-pipeline.md.

Large Transcript Handling

If input exceeds context comfort:

  1. Run Stage 1 by chunks.
  2. Merge chunk artifacts:
python scripts/merge_chunks.py --chunk-dirs "<chunkA/.pipeline>" "<chunkB/.pipeline>" --output-dir "<session/.pipeline>"
  1. Continue Stage 2 onward on merged artifacts.

Required Outputs Checklist

Learner-facing:

  • final_notes.md
  • <Domain> Class <NN> [DD-MM-YYYY] - <Topic>.md
  • bootcamp_index.md

Pipeline/audit:

  • .pipeline/segment_ledger.jsonl
  • .pipeline/segment_manifest.jsonl
  • .pipeline/refined_transcript.md
  • .pipeline/topic_inventory.json
  • .pipeline/corrections_log.csv
  • .pipeline/uncertainty_report.json
  • .pipeline/structured_notes.md
  • .pipeline/coverage_matrix.json
  • .pipeline/enhanced_notes.md
  • .pipeline/validation_report.md
  • .pipeline/exceptions.json (if fail)

Quality gates:

  • .pipeline/deep_pass_report.md (when --deep-pass)
  • .pipeline/deep_pass_exceptions.json (when --deep-pass)

Resource enrichment (optional):

  • .resources/resource_enrichment_report.json

Execution Rules

  • Fail fast on missing required artifacts.
  • Report missing outputs explicitly by file path.
  • Retry only from earliest failing stage.
  • Keep resource extraction status explicit (success/fallback/blocked).

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.74%
按下载量换算23

Claude

28.07%
按下载量换算19

Cursor

19.84%
按下载量换算13

Gemini CLI

9.08%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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