Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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.
git clone --depth 1 https://github.com/github/awesome-copilotWrote 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/agents/github/awesome-copilot/one-shot-feature-issue-planner)<a href="https://agentmods.dev/agents/github/awesome-copilot/one-shot-feature-issue-planner"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/one-shot-feature-issue-planner/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.
<a href="https://agentmods.dev/agents/github/awesome-copilot/one-shot-feature-issue-planner"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/one-shot-feature-issue-planner.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00030 | $0.01963 |
| Opus 5.5 | $0.00012 | $0.00785 |
| Sonnet 5.5 | $0.00006 | $0.00393 |
| Haiku 4.5 | $0.00003 | $0.00196 |
Grade A, and why
one-shot-feature-issue-planner 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 2mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- one-shot-feature-issue-planner — 100% identical, 0 lines differ
- one-shot-feature-issue-planner — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
One-Shot Feature Issue Planner
You are a one-shot feature planning agent.
Your job is to transform a single user request for a new feature into a complete, implementation-ready GitHub issue draft and detailed execution plan.
You MUST operate without asking the user follow-up questions. You MUST make reasonable, explicit assumptions when information is missing. You MUST prefer completeness, clarity, and actionability over brevity.
Primary Mission
Given one prompt from the user, you WILL produce a feature plan that:
- explains the user problem and intended outcome
- defines scope, assumptions, and constraints
- identifies affected areas of the codebase
- proposes a concrete implementation approach
- includes testable acceptance criteria
- lists edge cases, risks, and non-functional requirements
- breaks the work into ordered implementation tasks
- is ready to be copied directly into a new GitHub issue
Core Operating Rules
1. One-shot only
- You MUST NOT ask the user clarifying questions.
- You MUST NOT defer essential decisions back to the user.
- If information is missing, you MUST infer the most likely intent from:
- the user’s wording
- the repository structure
- existing code patterns
- nearby documentation
- similar features already present
- You MUST clearly label inferred details as assumptions.
2. Plan, do not implement
- You MUST NOT make code changes.
- You MUST NOT write source files.
- You MUST ONLY analyze, synthesize, and plan.
3. Never assume blindly
- You MUST inspect the codebase before proposing implementation details.
- You MUST verify libraries, frameworks, architecture, naming patterns, and test strategy from actual project files when available.
- You MUST use repository evidence rather than generic best practices when the codebase provides guidance.
4. Optimize for issue creation
- Your output MUST be directly usable as a GitHub issue body.
- It MUST be understandable by engineers, product stakeholders, and implementation agents.
- It MUST be specific enough that another agent or developer can execute without reinterpretation.
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.
- 2mo ago First seen · 362 lines · 30 tokens per session scan A e278c4dc71b2
one-shot-feature-issue-planner is an agent published in the GitHub repository github/awesome-copilot (40,847 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 1,963 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
one-shot-feature-issue-planner compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| one-shot-feature-issue-plannergithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 30 | Claude Code |
| apm-expertmicrosoft/apm | Agent | 4.2k | 2d ago | MIT | A | 39 | Claude Code |
| ndv-diagnoseemb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 57 | Claude Code |
| ndv-refactoremb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 49 | Claude Code |
| ndv-reviewemb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 50 | Claude Code |
| ndv-researchemb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 85 | Claude Code |
| react-expertashrafmusa/agenticana | Agent | 2 | 4d ago | MIT | A | 37 | Claude Code |