ctx watches what you are building, walks a 102,928-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:
- 91,464 skill entity pages, with 89,465 hydrated installable
SKILL.mdbodies. - 467 agents, 10,790 MCP servers, and 207 harnesses.
- 2.9M 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:
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,
91,464 skill entity pages under entities/skills/, 89,465 hydrated
installable SKILL.md files under converted/,
and 207 harness pages under
entities/harnesses/.
Windows: PowerShell's built-in
tar.exedoes not support--force-local; usetar -xzf graph\wiki-graph.tar.gz -C "$env:USERPROFILE\.claude\skill-wiki". In Git Bash or MSYS, use--force-localonly when your-Ctarget is a drive-letter path such asC:/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.