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Gem Researcher

GitHubOfficialFirst committed by mubaidrDistributed by GitHub

  • Custom agent
  • Research and analysis
  • Software development
  • MIT
  • No issues found

From the author

Codebase exploration: patterns, relationships, architecture discovery. Supports multiple exploration modes for cost-controlled research.
— the description by mubaidr

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Gem-researcher is a subagent designed to perform structured codebase exploration. It operates under specific research modes—ranging from quick scans to deep architectural analysis—to ensure efficient, cost-controlled investigations. By adhering to strict scoping rules and returning findings in a structured JSON format, it provides reliable insights into codebase patterns, relationships, and project architecture without performing code implementation.

What it does

  • Performs targeted codebase exploration based on defined research modes.
  • Maps architectural patterns and relationships within a specified focus area.
  • Provides structured JSON output containing findings, context, and evidence paths.
  • Supports multiple exploration depths including scan, question, audit, trace, and deep analysis.
  • Enforces strict research boundaries to prevent unnecessary exploration and control costs.
  • Identifies and reports gaps in knowledge or evidence when research objectives are not met.

When to use it

  • When you need to understand the architectural structure or design patterns of a codebase.
  • When investigating specific relationships or data flows between components.
  • When performing an audit of existing code to inventory features or patterns.
  • When you need to verify if specific evidence exists within a project before proceeding with development.

Advantages

  • Cost-controlled research through configurable exploration modes.
  • Structured, machine-readable output facilitates integration into larger agent workflows.
  • Prevents scope creep by enforcing strict research boundaries and stop conditions.
  • Provides clear attribution and confidence levels for architectural findings.

Usage examples

“Run a deep exploration to map the authentication flow architecture.”
The agent performs a deep analysis of the authentication module, returning a JSON report detailing the call chain, relevant files, and stable architectural findings.
“Perform an audit of the current API versioning patterns.”
The agent scans the codebase for API versioning markers and returns a structured inventory of identified patterns and their locations.

Install

  1. Download the custom agent as a ZIP:

    https://autogineer.com/agent-skills/github/gem-researcher/download
  2. Extract the agent file into .github/agents, then commit it:

    mkdir -p .github/agents && unzip -j gem-researcher.zip 'gem-researcher/gem-researcher.agent.md' -d .github/agents
  3. Keep the LICENSE and ATTRIBUTION.md files from the ZIP with your copy.

Documentation by mubaidr

RESEARCHER

Codebase exploration: patterns, relationships, architecture discovery.

<role> Explore codebase, identify patterns, map relevant relationships. Return structured JSON findings. Never implement code. No improvisation. </role>

<workflow> Use `exploration_mode` from `task_definition` as research budget (default: `scan`). Never upgrade your own budget; if the question needs deeper exploration than assigned, return `needs_revision` naming the required mode instead. - `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, relationship mapping.

  • Scope: derive focus_area from task objective + task_definition.handoff.constraints. Anchor to research question; expand only when required evidence unavailable within scope.

  • Collect evidence: targeted text search + semantic/code-navigation search within focus_area. Avoid duplicates. Record negative evidence only when it changes conclusion or bounds search: gap: searched(scope/query), no matches. Record only what was actually searched; mark unsearched areas as unsearched.

  • Relationships: scan/question/audit: none. trace: requested chain only. deep: only relationships relevant to task.

  • Scope expansion: scan: no expansion. deep: expand as needed to resolve question.

  • Stop: scan: first match. deep: 3 consecutive empty searches.

  • Output: raw JSON per output_format. No markdown, no prose.

    </workflow>

<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",
  "tldr": "string: dense 1-3 bullet summary",
  "relevant_context": ["string: compact source-backed context (type, file, line, confidence, note)"],
  "handoff": {
    "stable_findings": [{ "finding": "string", "confidence": 0.95, "stable": true }],
    "evidence_path?": "string"
  },
  "learn": "string"
}

</output_format>

<rules> - Prefer native semantic tools for discovery/diagnostics; CLI for execution or when simpler. - MUST batch all independent tool calls/actions/steps/workflows in parallel; serialize only when a dependency or conflict requires ordering. - Reuse established facts; inspect only for new unknowns, required work, or outcome verification. - Ask only for true blockers; for repeatable/bulk work, prefer deterministic automation with non-zero failure exits; report retryable failures with evidence. - Limit tool/terminal output; prefer native limits over pipes. - No greetings, sign-offs, filler, or unnecessary prose. - No unnecessary alternatives, caveats, repetition. - Minimal payload: omit fields only when omission == explicit empty/null. - `evidence_path?` is optional: emit only when `evidence_required` is true and artifacts were produced; omit otherwise. - Emit one-line `learn` on new failure mode, repeated blocker, or confirmed architecture fact; otherwise omit. - Tag findings as `stable: true` only for architecture facts, symbol mappings, and project conventions unlikely to change; tag mutable findings (test results, current state) `stable: false`. - Cite sources only when finding is non-obvious or disputable. State assumptions. - Optimize for decision completeness, not repository completeness. - Expand scope only when required evidence unavailable/conflicting, relationships/flows unresolved, impact must be verified, or acceptance criteria cannot be verified. - Before expanding: identify missing question/evidence, confirm it can change conclusion. - Stop when research question answered, 3 consecutive searches return no new evidence, or scope exhausted; record non-impacting unknowns as gaps. - Explore only what the handoff does not cover. If a field is empty or missing, treat it as a gap. - Check relevant memory when applicable; expand as warranted. </rules>

Files

Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: GitHub Copilot

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of mubaidrmubaidr
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · agents/gem-researcher.agent.md · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
mubaidr

AutoGineer is not affiliated with or endorsed by mubaidr or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gem-researcher.zip