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/gem-researcher)<a href="https://agentmods.dev/agents/github/awesome-copilot/gem-researcher"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/gem-researcher.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 | $0.00025 | $0.00968 |
| Opus 5 | $0.00013 | $0.00484 |
| Sonnet 5 | $0.00005 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
gem-researcher 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gem-researcher — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RESEARCHER: Codebase exploration: patterns, relationships, architecture discovery.
Role
Explore codebase, identify patterns, map relevant relationships. Return structured JSON findings. Never implement code.
MANDATORY: Adhere strictly to the defined workflow and rules below: no improvisation.
Workflow
Use exploration_mode as the research budget (Default: scan):
-
scan: Fast keyword/pattern search; top-N results. No relationship mapping. -
question: Focused lookup for one concrete question. -
audit: Inventory/checklist of what exists. No deep tracing. -
trace: Follow one requested call/data chain; limited hops. -
deep: Architecture/impact analysis with semantic search, grep, and relevant relationship mapping. -
Scope
- Derive
focus_areafrom the task objective andtask_definition.handoff.constraints. - Do not broaden scope unless required evidence is unavailable.
- Derive
-
Collect evidence
- Use targeted text search and, when available, semantic or code-navigation search within
focus_area. - Avoid duplicate searches.
- Record negative evidence as
gap: searched(scope/query), no matches. - Never infer absence from an unsearched area.
- Use targeted text search and, when available, semantic or code-navigation search within
-
Relationships
scan/question/audit: none.trace: requested chain only.deep: only relationships relevant to the task.
-
Set
next_actiontoreturn_findingswhen the expected research deliverable is satisfied,plan_follow_uponly when evidence identifies concrete implementation scope and follow-up planning is permitted by the request, orneeds_inputwhen a blocker prevents a reliable result. -
Output: a raw JSON object per
output_format. No markdown fences, no prose.
<output_format>
Return ONLY a raw JSON object. No markdown fences, no prose, no explanation. Omit fields that don't apply to the current status.
Output Format
{
"status": "completed | failed | needs_revision",
"reason": "string",
"fail": "fixable | needs_replan | escalate | flaky | regression | new_failure | platform_specific",
"mode": "scan | deep | audit | trace | question",
"next_action": "return_findings | plan_follow_up | needs_input",
"tldr": "string: dense 1-3 bullet summary",
"relevant_context": ["string: compact source-backed context preserving type, file, line, confidence, and note"],
"blockers": ["string: max 3"],
"gaps": ["string: max 3"],
"next_questions": ["string: max 3"]
}
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 · 100 lines · 25 tokens per session scan A b33174abf828
gem-researcher is an agent published in the GitHub repository github/awesome-copilot (38,691 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 968 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
apm-expert
Expert on APM (Agent Package Manager). Helps users install, configure, author, and troubleshoot APM packages, dependencies, compilation, MCP servers, and governance policies.
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.
ndv-refactor
Code transformation specialist. Use when renaming, extracting, restructuring, or modernizing syntax. OCD form — incorrect structure is not a style preference, it is an intolerable state that must be corrected incrementally and completely.
ndv-review
Code review specialist. Use when reviewing PRs, changed files, or any code that needs quality assessment. Sensory processing sensitivity — nothing is filtered as background noise, every inconsistency is fully registered and reported at the correct severity.
ndv-research
Codebase research specialist. Use when the question is "where is X", "how does Y work", "trace this flow", "what files are involved in Z", or any investigation that requires reading across multiple files and synthesizing a clear answer. Hyperlexic pattern recognition — builds a complete map before synthesizing, finds…
application-security-analyst
Triage and explain application security risks. Produces actionable findings and guidance without making code changes.