history-to-skill
Enables coding agents to turn GitHub repository history (PRs, issues, commits, CI, docs) into a source-linked maintainer skill by collecting evidence via the GitHub API and building a repository knowledge graph.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@history-to-skillgenerate a maintainer skill for pallets/flask"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
history-to-skill
Turn GitHub history into a cited maintainer skill for your coding agent.
Pull requests, issues, commits, CI, and architecture docs contain the maintenance knowledge a README
leaves out. history-to-skill connects that evidence in a repository knowledge graph and gives an
Agent Skills-compatible coding agent the material to generate a source-linked operating guide.

The image is a real pallets/flask run: 20 completed changes + 13 documents → 267 nodes, 928
edges, and 3 change communities. See the reproducible Flask example.
Get started in 30 seconds
Install the CLI, MCP server, and Agent Skill:
uv tool install 'history-to-skill[mcp]'
history-to-skill install --platform codex
codex mcp add history-to-skill -- history-to-skill-mcpThen ask your coding agent:
/history-to-skill pallets/flaskClaude Code and Cursor users can select their own skill directory convention. See the coding-agent setup for their MCP configuration:
history-to-skill install --platform claude
history-to-skill install --platform cursorUse --project to install inside the current repository, or --force to replace an older
installation. With pipx, run pipx install 'history-to-skill[mcp]'.
The MCP server exposes three stateless tools: collect repository evidence, query the evidence graph, and inspect an existing bundle. Every call has explicit inputs; no hidden conversation or server session is required.
Related MCP server: github-rag-mcp
What you get
The read-only collector produces a bounded evidence bundle:
repository history + docs
│
▼
evidence-graph.json typed nodes and source-linked edges
graph-context.md hotspots, communities, and ranked retrieval views
corpus.md bounded and redacted repository evidence
source-map.md stable URLs for every citation
SYNTHESIS.md contract for the coding agent
│
▼
<repo>-maintainer/
├── SKILL.md
└── references/
├── architecture.md
├── conventions.md
├── workflows.md
├── failure-patterns.md
└── source-map.mdThe generated skill answers practical questions such as:
Which test, lint, type-check, and release commands does CI actually run?
Which implementation and test paths repeatedly change together?
Where are the repository's component boundaries and change hotspots?
Which fixes or approaches have already failed?
What evidence supports each repository-specific instruction?
Every claim must cite a repository file, PR, issue, or commit. Graph relationships guide retrieval; they are never presented as proof on their own.
Run the collector directly
You can generate the evidence bundle without installing the Agent Skill:
history-to-skill pallets/flask flask-maintainer --output ./history_skill_workFor an existing checkout:
history-to-skill ~/code/my-service --limit 100 --max-documents 30Private repositories use GITHUB_TOKEN or GH_TOKEN. Public repositories work without a token,
subject to GitHub's unauthenticated rate limit.
Why a graph
A flat search can find a change mentioning a parser regression. The evidence graph can also retrieve the implementation paths, their tests, the component they belong to, and other completed changes that touched the same boundary.
The graph contains inspectable relationships:
completed PRs and commits
changefiles;files
belong_totop-level components;files changed together receive weighted
co_changededges;repeated co-changes form deterministic Louvain communities;
labels, documents, issues, and repository structure remain linked to their sources.
Graph-aware retrieval combines lexical seeds with personalized PageRank. NetworkX models the graph, SciPy performs sparse ranking, and broad changes are excluded from pairwise co-change expansion to avoid noisy, quadratic edge growth.
Designed for inspectability
Guarantee | Behavior |
Read only | Remote repositories are queried through the GitHub API; their code is never executed. |
Bounded | History items, documents, and text sizes have explicit limits. |
Source linked | Claims resolve to stable file, PR, issue, or commit identifiers. |
Injection resistant | Repository text is quarantined as untrusted evidence. |
Honest graph semantics | Co-change and PageRank results are retrieval leads, not dependency claims. |
Reproducible | Graph construction, Louvain communities, and ranking are deterministic. |
See the architecture, evidence format, coding-agent integration, live benchmarks, and research notes.
Scope
This is a change-history graph, not a symbol-level call graph, vulnerability scanner, or replacement for reading current implementation code. It extracts maintenance knowledge evidenced by repository history and documentation. Vague squash commits, private review conversations, and undocumented release procedures necessarily provide weaker evidence.
Development
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e '.[dev]'
ruff format --check .
ruff check .
mypy history_to_skill
agentskills validate "$PWD"
pytest --cov=history_to_skill --cov-report=term-missing
python -m build
twine check dist/*CI runs the checks across Python 3.10–3.13 and smoke-tests the built wheel. Contributions are welcome; see CONTRIBUTING.md.
If history-to-skill saves you a repository-archeology pass, a star helps other maintainers find it.
License
MIT. Repository content processed by the tool remains subject to its original license and access rules.
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