claude-ctx 1.0.19

Skill and agent recommendation system for Claude Code — knowledge graph, wiki, and intake quality gates

ctx — Skill, Agent, MCP & Harness Recommendations

ctx watches what you are building, walks a 79,958-node graph, and recommends a small, top-scored bundle of skills, agents, and MCP servers for the current task. If you use your own local/API model instead of Claude Code, ctx has a separate harness setup flow: tell it the model and goal, review the recommended harness, then install with dry-run/update/uninstall controls.

Current shipped snapshot:

• 68,494 skill entity pages, with 67,024 hydrated installable SKILL.md bodies.
• 467 agents, 10,790 MCP servers, and 207 harnesses.
• 2.6M graph edges across semantic similarity, tags, slug tokens, source overlap, direct links, quality, usage, type affinity, and graph structure.
• 28,612 long skill bodies converted through the micro-skill gate instead of shipping raw long prompts.
• Entity updates for skills, agents, MCPs, and harnesses print benefits/risks and skip replacement unless you explicitly approve the update.

Why it exists

• Discovery — with 91K+ skill pages, 460+ agents, 10K+ MCP servers, and 207 harnesses, you can't possibly know which exist or which apply to your current work.
• Context budget — loading everything wastes tokens and degrades quality. You need the right 10–15 per session.
• Skill rot — skills you installed months ago and never used are cluttering context. Stale ones should be flagged automatically.

Install

pip install claude-ctx
ctx-init                    # terminal wizard: hooks, graph, model, harness goal
ctx-init --graph --hooks --model-mode skip  # fast runtime graph + Claude Code hooks
ctx-init --graph --graph-install-mode full  # expand the full markdown wiki locally
ctx-init --wizard           # force the same wizard from scripts/tests
ctx-init --model-mode custom --model openai/gpt-5.5 --goal "build a CAD agent"

Optional extras: pip install "claude-ctx[embeddings]" for the semantic backend, pip install "claude-ctx[harness]" for local/API model harness runs, pip install "claude-ctx[dev]" for the test toolchain.

Pre-built knowledge graph

Graph-backed recommendations need the pre-built graph. By default, ctx-init --graph installs the fast runtime artifact: graph/wiki-graph-runtime.tar.gz in source checkouts, or the matching GitHub release asset from pip installs. It contains graphify-out/*, the shipped skill index needed for recommendations, and the 207 harness pages needed by ctx-harness-install:

ctx-init --graph

The full LLM-wiki artifact remains available for local browsing, Obsidian, and expanded markdown pages:

ctx-init --graph --graph-install-mode full

The full wiki-graph.tar.gz includes the shipped skill index, 68,494 skill entity pages under entities/skills/, 67,024 hydrated installable SKILL.md files under converted/, and 207 harness pages under entities/harnesses/.

Windows: PowerShell's built-in tar.exe does not support --force-local; use tar -xzf graph\wiki-graph.tar.gz -C "$env:USERPROFILE\.claude\skill-wiki". In Git Bash or MSYS, use --force-local only when your -C target is a drive-letter path such as C:/Users/....

Use

After ctx-init --hooks or the wizard hook step, ctx observes Claude Code's PostToolUse and Stop events. Typical flow:

ctx-scan-repo --repo .     # scan current repo and stack signals
ctx-scan-repo --repo . --recommend  # include skill/agent/MCP recommendations
ctx-agent-add --agent-path ./code-reviewer.md --name code-reviewer
ctx-harness-add --repo https://github.com/earthtojake/text-to-cad --tag cad
ctx-harness-install text-to-cad --dry-run   # inspect before cloning/running anything
ctx-harness-install text-to-cad             # install after reviewing the plan
ctx-harness-install text-to-cad --update --dry-run
ctx-harness-install text-to-cad --uninstall --dry-run
ctx-skill-quality list     # four-signal quality score for every skill
ctx-skill-quality explain python-patterns   # drill into a single skill
ctx-skill-health dashboard # structural health + drift detection
ctx-toolbox run --event pre-commit          # run a council on the current diff
ctx-monitor serve          # local dashboard: http://127.0.0.1:8765/

Before pushing, run the local PR gate:

python scripts/ci_preflight.py --profile pr

It uses the same changed-file classifier as GitHub Actions, then runs the matching local checks: stats, ruff, mypy, pip check, unit coverage, canaries, package build, twine, docs, graph validation, browser, and similarity gates as needed. Use --profile full before release work to force the source/package gates even for docs-only or graph-only changes.

The ctx-monitor dashboard shows currently loaded skills, agents, MCP servers, installed harness records, and generic-harness validation/escalation state. It provides load/unload buttons where ctx owns the live action, a graph view (/graph?slug=...), the LLM-wiki entity browser (/wiki/<slug>), a filterable skills grid, a session timeline, audit/runtime log views, and a live SSE event stream. Installed harness records appear in /loaded; harness pages appear in /wiki and /graph. Harness install/update/uninstall actions stay in ctx-harness-install.

When ctx-skill-add, ctx-agent-add, ctx-mcp-add, or ctx-harness-add finds an existing entity, ctx prints a benefits/risks update review and skips replacement by default. Re-run with --update-existing to apply the catalog or local asset update after review.

Step-by-step entity onboarding: https://stevesolun.github.io/ctx/entity-onboarding/

Full docs, architecture, and every module: https://stevesolun.github.io/ctx/

License

MIT — see LICENSE.