Summarize
trpc-group/trpc-agent-go
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
Analyze explicit Riffrec product-feedback captures, including riffrec-.zip, the Riffrec session.json + events.json + recording.webm + voice.webm bundle, or media/notes the user identifies as a…
Install Spec Riffrec Feedback Analysis
$ npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-riffrec-feedback-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/spec-riffrec-feedback-analysis .claude/skills/spec-riffrec-feedback-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "spec-riffrec-feedback-analysis" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-riffrec-feedback-analysis into .claude/skills/spec-riffrec-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-riffrec-feedback-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-riffrec-feedback-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-riffrec-feedback-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/spec-riffrec-feedback-analysis .agents/skills/spec-riffrec-feedback-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "spec-riffrec-feedback-analysis" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-riffrec-feedback-analysis into .agents/skills/spec-riffrec-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-riffrec-feedback-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-riffrec-feedback-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/spec-riffrec-feedback-analysis .cursor/skills/spec-riffrec-feedback-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "spec-riffrec-feedback-analysis" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-riffrec-feedback-analysis into .cursor/skills/spec-riffrec-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-riffrec-feedback-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/leo-kuang-ai/spec-first.git --path skills/spec-riffrec-feedback-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-riffrec-feedback-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/spec-riffrec-feedback-analysis .gemini/skills/spec-riffrec-feedback-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "spec-riffrec-feedback-analysis" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-riffrec-feedback-analysis into .gemini/skills/spec-riffrec-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-riffrec-feedback-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install leo-kuang-ai/spec-first spec-riffrec-feedback-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/spec-riffrec-feedback-analysis .github/skills/spec-riffrec-feedback-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "spec-riffrec-feedback-analysis" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-riffrec-feedback-analysis into .github/skills/spec-riffrec-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-riffrec-feedback-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install leo-kuang-ai/spec-first spec-riffrec-feedback-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/leo-kuang-ai/spec-first.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/spec-riffrec-feedback-analysis .opencode/skills/spec-riffrec-feedback-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "spec-riffrec-feedback-analysis" agent skill from https://github.com/leo-kuang-ai/spec-first/tree/master/skills/spec-riffrec-feedback-analysis into .opencode/skills/spec-riffrec-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spec-riffrec-feedback-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
spec-riffrec-feedback-analysis- GitHub stars
- 107
- Token cost
- ~1.4k tokens
- SKILL.md length
- 585 words
- Files
- 11 (incl. scripts, references)
- Skills in repo
- 35
- Repo updated
- First seen
- Licence
- MIT
At a glance
Analyze explicit Riffrec product-feedback captures, including riffrec-.zip, the Riffrec session.json + events.json + recording.webm + voice.webm bundle, or media/notes the user identifies as a…
- Riffrec setup and capture guidance
- SKILL.md covers Choose the path, Common rules, Dispatch Authorization Boundary and Analyzer entrypoint
- Runs Shell and Python scripts from its folder; calls bash; needs OPENAI_API_KEY
- Generic podcasts
What it does
Spec Riffrec Feedback Analysis is an agent skill from leo-kuang-ai/spec-first. Analyze explicit Riffrec product-feedback captures, including riffrec-.zip, the Riffrec session.json + events.json + recording.webm + voice.webm bundle, or media/notes the user identifies as a Riffrec feedback capture. Also use for Riffrec setup and capture guidance. Do not trigger for generic podcasts, meetings, audio/video transcription, or unrelated capture/share requests.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts and reference files (for example `evals/cases/generic-audio-not-triggered.yaml`, `evals/eval.yaml` and `evals/fixtures/scripts/asks-a-question.sh`).
It sits in Media & Creative, covering Customer feedback analysis, Transcription and Podcasting. The repository describes itself as: 仓库原生 AI Coding Harness —— 把一次性 AI 对话变成可治理、可验证、可沉淀的工程闭环 · spec-first.cn. The licence is MIT.
When your agent uses it
- Riffrec setup and capture guidance
- Generic podcasts
- Audio/video transcription
- Unrelated capture/share requests
Example prompts
- “/spec-riffrec-feedback-analysis”
Requirements
- Python 3
- A Bash shell
- A credential in OPENAI_API_KEY
What it can do on your machine
Read from SKILL.md and the folder at commit 74655dc. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Shell and Python), which the agent can run.
Shell commands in SKILL.md call:
bash
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
OPENAI_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Spec Riffrec Feedback Analysis loads about 1.4k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 105 tokens; SKILL.md has 585 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from leo-kuang-ai/spec-first at commit 74655dc, republished under its MIT licence (© leo-kuang-ai). 585 words, ~1,436 tokens.
Download SKILL.mdSave it as
.claude/skills/spec-riffrec-feedback-analysis/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.- name
- spec-riffrec-feedback-analysis
- description
- Analyze explicit Riffrec product-feedback captures, including `riffrec-*.zip`, the Riffrec `session.json` + `events.json` + `recording.webm` + `voice.webm` bundle, or media/notes the user identifies as a Riffrec feedback capture. Also use for Riffrec setup and capture guidance. Do not trigger for generic podcasts, meetings, audio/video transcription, or unrelated capture/share requests.
Riffrec Feedback Analysis
Turn raw product feedback into structured evidence for downstream agents. This skill is the consumption side of Riffrec, a capture tool that records synchronized screen + voice + event sessions and emits a riffrec-*.zip bundle.
Choose the path
Route to the matching reference based on the input. Read only that reference; do not load the others.
- Setup — user has no recording yet and asks how to install Riffrec, capture a session, or share feedback. Read
references/install-riffrec.md. - Quick bug report — input is a short recording (under ~60 seconds), the user describes a single specific issue, or asks for "quick", "small", or "just transcribe". Read
references/quick-bug-report.md. Emit one concise bug report; skip the full artifact set and brainstorm handoff. Discovering broader scope returns an escalation handoff and never authorizes a durable extensive rerun. - Extensive analysis — input is a longer recording, contains multiple issues / requirements / workflow walkthroughs, or the user wants requirements or brainstorm material. Read
references/extensive-analysis.md. Produce a ready-to-brainstorm handoff; invokespec-brainstormonly when the original request or a new confirmation authorizes that public workflow.
When the input is ambiguous (e.g., a zip arrived without context), inspect the recording length and event count before choosing. If still unclear, ask the user which path applies before running anything heavy.
Common rules
- Keep raw recordings, audio chunks, zip contents, session dumps, and extracted screenshots local-only by default. Do not commit
raw/orframes/directories unless the user explicitly asks and privacy is acceptable. - Text/metadata artifacts (requirements kickoff material, analysis summaries, problem analyses, source manifests) may be committed when they are needed for traceability and contain no sensitive data.
- Use repo-relative screenshot paths in any committed doc so later agents can open the evidence without absolute local paths.
Media transcription is a separate third-party egress. Before analyzer execution, record transcription_egress_authorization: authorized | missing. It is authorized only when the current user or visible upstream handoff explicitly requests third-party transcription for this recording; analysis intent, a local file, an ambient OPENAI_API_KEY, or worker dispatch authority does not grant it. Pass --transcribe only when authorized. Otherwise pass --no-transcribe, preserve local frames/events/notes analysis, and report the missing transcript limitation. The analyzer returns the authorization source, provider identity, and whether a provider request was sent.
Show full SKILL.md (219 more words)Show less
Dispatch Authorization Boundary
在把 recording evidence、transcript、screenshots 或 source-mapping context 交给任何 worker 前，记录：
worker_dispatch_authorization: authorized | missing
capability_probe: not_applicable | attempted | unavailable
worker_dispatch_capability: available | missing | unknown
worker_context_isolation: isolated | inherited | unknown
worker_model_override: supported | unsupported | unknown
worker_bounded_parallelism: supported | unsupported | unknownworkflow invocation does not authorize dispatch。只有当前用户或可见 upstream handoff 明确请求 subagent、delegated work、persona 或 parallel work 时才可派发；输入文件、分析规模、工具权限或本 Skill 被调用都不构成授权。缺授权时不得探测 tool schema，固定为 capability_probe: not_applicable + worker_dispatch_capability: unknown，inline 或 serial 执行并记录 dispatch_authorization_missing。只有授权后才把 current-session registry/schema 作为 provider_untrusted evidence 检查：确认缺失时记录 subagent_capability_missing；surface 不可用、schema 不完整或候选不唯一时记录 worker_capability_unproven，均 inline 或 serial。隔离、模型覆盖和有界并发只取 live facts；required isolation 未满足时保持依赖 gate 打开，model unknown 时继承，parallelism unknown 时串行。记录 worker_dispatch_outcome。任何派发还必须遵守 local-only/privacy 边界，只发送完成 bounded unit 所需的最小证据。Inline fallback 不得声称 independent analyst coverage。
Analyzer entrypoint
All non-setup paths share the same analyzer, which ships in this skill's scripts/ directory. The Bash tool's working directory is the user's project, not the skill directory, so a bare scripts/<name> path will not resolve. Invoke it by the skill's own absolute path: set SKILL_DIR to the directory you loaded this spec-riffrec-feedback-analysis SKILL.md from, in the same command (shell state does not persist between Bash calls):
SKILL_DIR="<absolute path of the directory containing this SKILL.md>"
bash "$SKILL_DIR/scripts/run-python.sh" "$SKILL_DIR/scripts/analyze_riffrec_zip.py" /path/to/input --no-transcribeAccepted inputs: a Riffrec .zip, an .mp4 / .mov / .webm video, an .m4a / .mp3 / .wav audio file, or a meeting-notes .md. Use --output-dir <dir> to control where artifacts land. In repos with docs/brainstorms/, the default remains docs/brainstorms/riffrec-feedback/ as a documented evidence/kickoff-artifact exception; it is not the durable spec-brainstorm output convention. The quick path overrides the output dir to a temp location so nothing pollutes the repo.
The Spec-First output format used by the extensive path is documented in references/spec-first-feedback-format.md.
© leo-kuang-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 10 other files (scripts, references) in skills/spec-riffrec-feedback-analysis of leo-kuang-ai/spec-first.
- SKILL.md
- evals/cases/generic-audio-not-triggered.yaml
- evals/eval.yaml
- evals/fixtures/scripts/asks-a-question.sh
- evals/fixtures/scripts/check-generic-refused.sh
- references/extensive-analysis.md
- references/install-riffrec.md
- references/quick-bug-report.md
- references/spec-first-feedback-format.md
- scripts/analyze_riffrec_zip.py
- scripts/run-python.sh
Open the folder on GitHubat commit 74655dc
Compare with similar skills
Spec Riffrec Feedback Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Spec Riffrec Feedback Analysis this skillleo-kuang-ai/spec-first | 107 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Summarizetrpc-group/trpc-agent-go | 1.9k | 22 repos | ~552 | Automated safety check: Pass | Apache-2.0 | |
| VideoiBigQiang/feedgrab | 614 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Podcast Transcript FetcherVarnan-Tech/opendirectory | 674 | — | ~1.9k | Automated safety check: Notes | MIT | |
| Transcribegnekt/My-Brain-Is-Full-Crew | 3.9k | — | ~5k | Automated safety check: Pass | Custom licence | |
| Cliptalk Interview EditorGML-MMGroup/ClipTalk | 136 | — | ~460 | Automated safety check: Pass | Custom licence |
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Related
Categories
Questions about Spec Riffrec Feedback Analysis
What does Spec Riffrec Feedback Analysis do?
Analyze explicit Riffrec product-feedback captures, including riffrec-.zip, the Riffrec session.json + events.json + recording.webm + voice.webm bundle, or media/notes the user identifies as a…. Spec Riffrec Feedback Analysis is an agent skill from leo-kuang-ai/spec-first.webm bundle, or media/notes the user identifies as a Riffrec feedback capture.
When should I use Spec Riffrec Feedback Analysis?
Spec Riffrec Feedback Analysis fits situations like: riffrec setup and capture guidance; generic podcasts; audio/video transcription; unrelated capture/share requests.
How do I install Spec Riffrec Feedback Analysis in Claude Code?
Run `npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a claude-code`. Or copy the skill folder (skills/spec-riffrec-feedback-analysis in leo-kuang-ai/spec-first) into .claude/skills/spec-riffrec-feedback-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Spec Riffrec Feedback Analysis in Codex?
Run `npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a codex`. Or copy the skill folder (skills/spec-riffrec-feedback-analysis in leo-kuang-ai/spec-first) into .agents/skills/spec-riffrec-feedback-analysis in your project. Codex loads it when a task matches its description.
Can I use Spec Riffrec Feedback Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add leo-kuang-ai/spec-first --skill spec-riffrec-feedback-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/spec-riffrec-feedback-analysis, .gemini/skills/spec-riffrec-feedback-analysis, .github/skills/spec-riffrec-feedback-analysis and .opencode/skills/spec-riffrec-feedback-analysis in your project.
What does Spec Riffrec Feedback Analysis need to run?
Going by SKILL.md and its folder, Spec Riffrec Feedback Analysis needs a shell and Python for the scripts in its folder, the command-line tools its instructions call (bash) and credentials named OPENAI_API_KEY. Our summary lists: Python 3; A Bash shell; A credential in OPENAI_API_KEY.
Does Spec Riffrec Feedback Analysis access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is Spec Riffrec Feedback Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Spec Riffrec Feedback Analysis use?
Spec Riffrec Feedback Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Spec Riffrec Feedback Analysis use?
About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.6k tokens, read only when the agent opens those files.
What are the alternatives to Spec Riffrec Feedback Analysis?
Skills that share tags, products or a category with Spec Riffrec Feedback Analysis: Summarize (trpc-group/trpc-agent-go, 1.9k stars), Video (iBigQiang/feedgrab, 614 stars), Podcast Transcript Fetcher (Varnan-Tech/opendirectory, 674 stars) and Transcribe (gnekt/My-Brain-Is-Full-Crew, 3.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Spec Riffrec Feedback Analysis?
leo-kuang-ai (a GitHub user) maintains it in leo-kuang-ai/spec-first, which has 107 GitHub stars. The repository holds 35 skills in this directory. The repository was last updated on October 8, 2026.
Source: leo-kuang-ai/spec-first on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.