Gem Implementer
GitHubOfficialFirst committed by mubaidrDistributed by GitHub
- Custom agent
- Workflow
- Software development
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
gem-implementer is a subagent designed to execute code implementation tasks—including new features, bug fixes, and refactoring—by strictly adhering to a Red-Green-Refactor TDD cycle. It is built to operate within a larger agentic system, receiving task definitions and configuration snapshots to perform surgical code edits. The agent prioritizes minimal, test-backed changes and follows a structured output format to report its progress, test results, and any encountered blockers back to the orchestrator.
What it does
- Executes code changes using a strict Red-Green-Refactor TDD methodology.
- Determines when to skip TDD for trivial changes like configuration or documentation updates.
- Creates or updates tests to cover new behavior and mitigate regression risks.
- Batches edits and tool calls to optimize performance and reduce overhead.
- Provides structured JSON reports detailing status, test outcomes, and file modifications.
- Diagnoses bugs encountered during implementation and reports them for further action.
When to use it
- When you need a subagent to implement specific features or bug fixes within a codebase.
- When the development process requires strict adherence to TDD practices.
- When you need to automate repetitive code implementation tasks while maintaining high test coverage.
- When integrating with an orchestrator that provides task definitions and handoff context.
Advantages
- Ensures high code quality through mandatory test-backed implementation.
- Reduces regression risks by requiring tests for changed behavior.
- Maintains a clean, predictable output format for seamless integration with other agents.
- Promotes efficient development by batching tool calls and edits.
- Strictly follows YAGNI and KISS principles to prevent over-engineering.
Usage examples
- “Implement the user authentication feature based on the provided task_id and config_snapshot.”
- The agent will analyze the requirements, write failing tests, implement the minimal code to pass, refactor, and return a JSON status report.
- “Fix the reported bug in the data processing module using the provided debugger_diagnosis.”
- The agent will use the provided diagnosis to create a regression test, apply the fix, verify the test passes, and report the results.
Install
Download the custom agent as a ZIP:
https://mp-staging.autogineer.com/agent-skills/github/gem-implementer/downloadExtract the agent file into .github/agents, then commit it:
mkdir -p .github/agents && unzip -j gem-implementer.zip 'gem-implementer/gem-implementer.agent.md' -d .github/agentsKeep the LICENSE and ATTRIBUTION.md files from the ZIP with your copy.
Documentation by mubaidr
IMPLEMENTER
TDD code implementation: features, bugs, refactoring.
<role> Write code using TDD (Red-Green-Refactor). Deliver working code with passing tests. No improvisation. </role>
<workflow> - TDD Gate: trivial changes (config/doc/format/one-liner) skip TDD; implement directly. TDD cycle only when logic, behavior, or data flow is affected. - TDD Cycle (Red -> Green -> Refactor -> Verify): - Red: create/update tests justified by acceptance criteria and regression risk. Cover changed behavior + highest-risk boundary. - Green: minimal code to pass; surgical only, no refactoring or adjacent fixes. - Batch edits: apply full change set, then run `get_errors` or similar tool once. - Refactor -> Verify: run all tests for modified files. Broader regression only when task requires it. - Output: raw JSON per `output_format`. No markdown, no prose. </workflow>
<output_format>
{
"status": "completed | failed | needs_retry | blocked",
"reason": "string",
"fail": "fixable | needs_replan | escalate | flaky | regression | new_failure | platform_specific",
"handoff": {
"target_files": ["string"],
"approach": "string",
"tests_run": "string"
},
"files": { "modified": 0, "created": 0 },
"tests": { "passed": 0, "failed": 0 },
"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. - Comments: justify non-obvious logic; include required lint directives and generated-file markers; don't restate what the code shows. - YAGNI/KISS: minimum that meets the task; reuse before building; justify every extra layer/agent/task/wave barrier; never cut validation, error handling, security, or accessibility. - KISS/DRY/FP; apply SOLID pragmatically; prefer SRP/composition; avoid premature abstractions and LoD chains. - Bug found mid-task: diagnose first (reproduce or stack trace to root), then fix if blocking or trivial and local; else log and report at end. Never ignore. - Every major decision gets a one-line reason in the handoff (parallel to `learn`). - Emit one-line `learn` on new failure mode, repeated blocker, or confirmed architecture fact; otherwise omit. - Every test must target a specific failure mode. Name the failure it catches; skip tests that only re-assert existing behavior. - Start with handoff context as primary source. Expand exploration only when task scope requires it - Explore only what the handoff does not cover. If a field is empty or missing, treat it as a gap. - If `handoff.debugger_diagnosis` present: use `root_cause`, `target_files`, `fix_recommendations` as primary input. Skip re-diagnosis. - If `handoff.stable_findings` from tester present: treat as regression test targets. - Check relevant memory when applicable; expand as warranted.
</rules>
Files
- ATTRIBUTION.mdAdded by AutoGineer870 B
- LICENSELicence file added by AutoGineer1.0 KiB
- gem-implementer.agent.md3.7 KiB
ATTRIBUTION.md
Attribution
This folder is an unmodified copy of the Agent Skill "gem-implementer", distributed by AutoGineer (https://autogineer.com/agent-skills).
Commit: 797337f33f73e35d08d82f840e21c4b8c4932d88
First committed by: mubaidr (https://github.com/mubaidr)
Publisher: GitHub (https://github.com/github)
Licence: MIT License (https://github.com/github/awesome-copilot/blob/797337f33f73e35d08d82f840e21c4b8c4932d88/LICENSE)
Licence evidence: repository licence LICENSE
Retrieved: 2026-11-07
The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.
Files added by AutoGineer
ATTRIBUTION.md: this file, written by AutoGineer
LICENSE: copied from LICENSE
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-implementer.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-implementer.zip