LeanCTX — Context Intelligence for AI systems.
$ npx -y skills add yvgude/lean-ctx --agent claude-code
Repo: yvgude/lean-ctx
What's inside
LeanCTX — AI Value Gate for AI Coding Agents
LeanCTX — short for Lean Context — is an AI Value Gate and context engineering layer for AI coding agents. It runs locally alongside your coding agent, helping it read repositories, run development commands, and send focused context to the model: it understands the task, routes the right context, compresses what it sends, and tracks the cost and outcome of that work. Savings depend on the workload and enabled modes; the local savings ledger and Shadow Mode show the measured result against a comparable baseline. Zero config required. Local-first.
| Problem | With LeanCTX |
|---|---|
| Repeated file reads resend unchanged content | Cached re-reads return a compact deterministic reference |
| Raw development commands include repetitive noise | Command-specific compression preserves salient output |
| Every turn re-sends the whole history | Proxy compresses each request, prompt-cache-safe |
| Context resets every chat | Session memory persists across chats |
| No visibility into context usage | Real-time dashboard + budget control |
Control what your AI can see — and what it costs. LeanCTX is an AI Value Gate for coding agents: it understands tasks, routes and compresses context, remembers what it learns, and measures cost against accepted outcomes.
Token savings are the receipt. Intelligence is the product. Works with Cursor, Claude Code, Copilot, Windsurf, Codex, Gemini and 30+ other agents — no config needed.
lean-ctx setup command, no config changes neededModels are converging on commodity. The durable edge isn't which model you call — it's your context: what your agents read, what they remember, and what you can prove. And the layer that optimizes and owns that context can't come from the vendor that bills per token or keeps your memory in a black box — it has to sit on your side.
That's the shift behind "agent entities" that live in your chat and remember your company (Claude in Slack, ClickUp Brain): a context login, not a model login — you end up renting your own company knowledge back. LeanCTX is the opposite layer. It keeps the moat yours: local-first, portable (.ctxpkg), and model-agnostic — swap OpenAI, Anthropic or Gemini without losing context or cache. Own your context; don't rent it back.
LeanCTX treats context and AI spend as managed resources, not afterthoughts. One binary covers the capabilities that decide how well an AI agent performs:
Your AI agent reads files and runs commands. LeanCTX compresses both automatically.
Workload-specific token reduction on eligible context, with recovery paths and a local Shadow Mode baseline for measurement
File reads: 10 read modes (full, map, signatures, diff, lines:N-M, density:X, …) — cached re-reads cost ~13 tokens
Target density (density:0.4): SDE-style budget compression — keeps the highest-entropy lines until ~40% of the original tokens remain, deterministic
JIT disclosure: signatures carries line spans and points at lines:N-M for targeted expansion — outline first, bodies on demand
Shell output: 95+ shell-output patterns compress git, npm, cargo, docker, kubectl, terraform and more (270 passthrough rules)
Tree-sitter AST: structural understanding for 27 languages — not just text compression
Reversible by design (CCR): compression never discards content — pruned or truncated payloads move to a content-addressed store with a deterministic handle, so the model can pull the original bytes back on demand via ctx_expand, ctx_retrieve, an in-band marker, or GET /v1/references/{id}. Five recovery paths →
Not every task or file needs the same depth. LeanCTX classifies the task, then sends the signal rather than the noise.
ModePredictor: learns the optimal read mode per file type from past sessionsIntentEngine: classifies query complexity so simple lookups stay cheapRelevant code, sessions, and connected sources become focused context instead of a larger prompt.
.ctxpkg package and move it across machines or models, instead of locking it in a vendor's black boxPerformance is the cost of a useful result, not just speed. LeanCTX records costs and outcomes locally; CPAO (Cost per Accepted Outcome) is the north-star metric for comparing useful AI work.
ctx_proof, ctx_verify): 4-layer verification engine with CI drift gatesShadow Mode compares LeanCTX treatment with a configured baseline without changing the active workflow. Its reports show cost, tokens, CPAO, and whether quality held, then recommend savings only when the comparison supports them.
LeanCTX automatically tracks local cost and outcome signals; it does not add those reports to agent context. CPAO — cost per accepted outcome — is the north-star metric, while Shadow Mode provides a baseline comparison for savings.
lean-ctx savings --period week # costs, token savings, and CPAO
lean-ctx value-report --format markdown --last 20 # recent outcome quality
lean-ctx shadow --latest # latest baseline comparison
# Enable shadow mode for savings comparison
echo '[shadow]\nenabled = true' >> ~/.config/lean-ctx/config.toml
# After using LeanCTX for a while:
lean-ctx savings
lean-ctx shadow --latest
ctx_url_read): pull a public web page, PDF, or YouTube transcript into context as compressed, citation-backed text — facts/quotes return claims with a confidence score + source URL, relevance-ranked research-compression distils to a token budget, SSRF-guarded (http/https only)ctx_refactor): language-server-powered rename, references, go-to-definition via rust-analyzer, typescript-language-server, pylsp, goplsctx_agent, ctx_handoff): agent handoff with context transfer bundles, diary system, synchronized shared statectx_expand search_all): FTS5-powered cross-archive search over all previously archived tool outputsShowing a partial view of a very large repo.
FAQ
lean-ctx is a Claude Code plugin with 2 hand-picked skills for development work, indexed on Flowy. Install it with the command on its page. It includes lean-ctx-review, lean-ctx. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
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