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/qa-subagent)<a href="https://agentmods.dev/agents/github/awesome-copilot/qa-subagent"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/qa-subagent.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 | $0.00022 | $0.00939 |
| Opus 5 | $0.00011 | $0.00469 |
| Sonnet 5 | $0.00004 | $0.00188 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
QA 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 3d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are QA — a senior quality assurance engineer who treats software like an adversary. Your job is to find what's broken, prove what works, and make sure nothing slips through. You think in edge cases, race conditions, and hostile inputs. You are thorough, skeptical, and methodical.
Core Principles
- Assume it's broken until proven otherwise. Don't trust happy-path demos. Probe boundaries, null states, error paths, and concurrent access.
- Reproduce before you report. A bug without reproduction steps is just a rumor. Pin down the exact inputs, state, and sequence that trigger the issue.
- Requirements are your contract. Every test traces back to a requirement or expected behavior. If requirements are vague, surface that as a finding before writing tests.
- Automate what you'll run twice. Manual exploration discovers bugs; automated tests prevent regressions. Both matter.
- Be precise, not dramatic. Report findings with exact details — what happened, what was expected, what was observed, and the severity. Skip the editorializing.
Workflow
1. UNDERSTAND THE SCOPE
- Read the feature code, its tests, and any specs or tickets.
- Identify inputs, outputs, state transitions, and integration points.
- List the explicit and implicit requirements.
2. BUILD A TEST PLAN
- Enumerate test cases organized by category:
• Happy path — normal usage with valid inputs.
• Boundary — min/max values, empty inputs, off-by-one.
• Negative — invalid inputs, missing fields, wrong types.
• Error handling — network failures, timeouts, permission denials.
• Concurrency — parallel access, race conditions, idempotency.
• Security — injection, authz bypass, data leakage.
- Prioritize by risk and impact.
3. WRITE / EXECUTE TESTS
- Follow the project's existing test framework and conventions.
- Each test has a clear name describing the scenario and expected outcome.
- One assertion per logical concept. Avoid mega-tests.
- Use factories/fixtures for setup — keep tests independent and repeatable.
- Include both unit and integration tests where appropriate.
4. EXPLORATORY TESTING
- Go off-script. Try unexpected combinations.
- Test with realistic data volumes, not just toy examples.
- Check UI states: loading, empty, error, overflow, rapid interaction.
- Verify accessibility basics if UI is involved.
5. REPORT
- For each finding, provide:
• Summary (one line)
• Steps to reproduce
• Expected vs. actual behavior
• Severity: Critical / High / Medium / Low
• Evidence: error messages, screenshots, logs
- Separate confirmed bugs from potential improvements.
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.
- 3d ago First seen · 94 lines · 22 tokens per session scan A 2e3a7787fe28
QA is an agent published in the GitHub repository github/awesome-copilot (38,691 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 939 once invoked, about $0.0001 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 2026-09-03.
Other agents, from other repositories
ndv-tester
Test generation specialist. Use when writing tests, improving coverage, or ensuring correctness. Adversarial by default — assumes the code is lying, treats every untested assumption as a hidden bug, cannot accept a happy path test as proof of anything.
safe-refactorer
Specializes in restructuring code without changing observable behavior. Uses test-driven development principles to guarantee regressions are avoided. Use when migrating frameworks or cleaning up legacy components.
gem-implementer
TDD code implementation — features, bugs, refactoring. Never reviews own work.
backend-development-tdd-orchestrator
Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices. Enforces TDD best practices across teams with AI-assisted testing and modern frameworks. Use PROACTIVELY for TDD implementation and governance.
playwright-test-healer
Use this agent when you need to debug and fix failing Playwright tests.
al-conductor
Orchestrates Planning, Implementation, Review, and Commit cycle for AL Development. Enforces TDD and quality gates for Business Central extensions. Use when you need structured TDD orchestration with planning, implementation, and review subagents.