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Powered by Awesome Copilot GitHub contributors from allcontributors.org
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
A community-created collection of custom agents, instructions, skills, hooks, workflows, and plugins to supercharge your GitHub Copilot experience.
[!TIP] Explore the full collection on the website → awesome-copilot.github.com
The website offers full-text search and filtering across hundreds of resources, plus the Learning Hub for guides and tutorials.
Using this collection in an AI agent? A machine-readable
llms.txtis available with structured listings of all agents, instructions, and skills.
New to GitHub Copilot customization? The Learning Hub on the website offers curated articles, walkthroughs, and reference material — covering everything from core concepts like agents, skills, and instructions to hands-on guides for hooks, agentic workflows, MCP servers, and the Copilot coding agent.
| Resource | Description | Browse |
|---|---|---|
| 🤖 Agents | Specialized Copilot agents that integrate with MCP servers | All agents → |
| 📋 Instructions | Coding standards applied automatically by file pattern | All instructions → |
| 🎯 Skills | Self-contained folders with instructions and bundled assets | All skills → |
| 🔌 Plugins | Curated bundles of agents and skills for specific workflows | All plugins → |
| 🍳 Cookbook | Copy-paste-ready recipes for working with Copilot APIs | — |
For most users, the Awesome Copilot marketplace is already registered in the Copilot CLI/VS Code, so you can install a plugin directly:
copilot plugin install <plugin-name>@awesome-copilot
If you are using an older Copilot CLI version or a custom setup and see an error that the marketplace is unknown, register it once and then install:
copilot plugin marketplace add github/awesome-copilot
copilot plugin install <plugin-name>@awesome-copilot
See CONTRIBUTING.md · AGENTS.md for AI agent guidance · Security · Code of Conduct
The customizations here are sourced from third-party developers. Please inspect any agent and its documentation before installing.
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
name: signal-write
description: 'Emit structured agent signals — hands-up, blocked, done, checkpoint, partnership. Signals are written as JSON to .signals/ for dashboard consumption and noted in the journal for persistence.'Emit structured signals from a desk to the operator or other desks.
hands-upTwo desks disagree and can't settle it against external facts. This is the system working — the operator reads where desks disagree, not where they perform confidence.
blockedA desk can't proceed without input — missing access, ambiguous scope, need a decision only the operator can make.
doneWork is complete and ready for review. Artifacts are on the bench.
checkpointSignificant progress worth the operator knowing about, but work continues. Not blocked, not done — just a marker.
partnershipUsed by the TA (room coordinator) to report coordination quality. Self-assessment scores reflect coordination, not code accuracy:
.signals/This is the primary output — it's what the dashboard reads.
Create desks/<desk-name>/.signals/<timestamp>.json:
{
"signal_type": "execution",
"subtype": "checkpoint",
"timestamp": "2026-07-19T21:30:00Z",
"run_id": "<optional; set to pair this with an outcome signal>",
"agent_name": "<desk-name>",
"self_assessment": {
"intent": 4,
"confidence": 5,
"accuracy": 4,
"completeness": 3
},
"patterns": {
"what_worked": "description of what went well",
"what_was_hard": "description of challenges",
"skill_gap": "areas for improvement"
},
"escalation": {
"reason": null,
"blocked_on": null,
"recommendation": null
}
}
| Signal | signal_type | subtype |
|---|---|---|
| hands-up | "escalation" | "hands-up" |
| blocked | "escalation" | "blocked" |
| done | "execution" | "done" |
| checkpoint | "execution" | "checkpoint" |
| partnership | "partnership" | "partnership" |
The subtype field preserves the specific signal state for
dashboard consumers. signal_type controls sort priority
(escalation → top).
Note: The signals-dashboard canvas extension reads
subtypewhen present and falls back tosignal_typefor display. If consuming signals in your own tooling, prefersubtypefor the specific state.
Ordering: include a
timestamp(ISO 8601 UTC). The dashboard orders signals by it and falls back to file mtime only when it's absent — a git clone/checkout resets mtimes, so mtime alone is not a dependable clock.
Also append a short marker to the desk's journal for persistence:
## <date> — [signal:<type>] <summary>
- <key details>
The journal note is the trail marker. The JSON file is the machine-readable signal.
The signals-dashboard can pair a desk's self-assessment with an outcome — an independent rating of the realized result — and show the honesty gap (how far the desk's confidence was from the delivered quality). Outcome signals are optional and are usually emitted by a reviewer/evaluator, not the desk itself.
Write them to the same .signals/ directory:
{
"signal_type": "outcome",
"run_id": "<same run_id as the signal it rates>",
"agent_name": "<reviewer name>",
"quality_rating": 4,
"effort_to_merge": "minimal",
"issues_found": ["optional short strings"],
"timestamp": "2026-07-19T22:00:00Z"
}
run_id correlates an outcome with the execution/partnership
signal it rates — set the same run_id on both. If it's absent, the
dashboard falls back to the nearest outcome emitted shortly after the
latest signal.quality_rating (0–5) is the realized quality; the dashboard
compares it to the desk's self-assessed confidence to compute the
honesty gap.effort_to_merge — "minimal", "moderate", or "significant".issues_found — optional array of short strings.
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