Gem Researcher
GitHubOfficialFirst committed by mubaidrDistributed by GitHub
- Custom agent
- Research and analysis
- Software development
- MIT
- No issues found
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
Download the custom agent as a ZIP:
https://autogineer.com/agent-skills/github/gem-researcher/downloadExtract 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/agentsKeep 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_areafrom 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 asunsearched.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
- ATTRIBUTION.mdAdded by AutoGineer868 B
- LICENSELicence file added by AutoGineer1.0 KiB
- gem-researcher.agent.md4.4 KiB
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
mubaidr
- 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