Chisle
The coding-agent plugin that bills 52% of a bare model across 20 live tasks. Terse persona + tool-output compressor + context diet.
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Read the full README ↗An MCP server for codebase context, built for AI coding agents.
Your AI agent reads 19 files to answer "who calls this function?" because it has the text of your codebase but never the map. It re-derives structure on every task, burns tokens chasing grep chains, and hallucinates dependencies that don't exist. Sense indexes your repo once and serves a symbol graph plus semantic code search over MCP. Claude Code, Cursor, Codex CLI, and any MCP client finish the same task in 10 tool calls instead of 19, on 156K tokens instead of 228K, with the same correctness (bench/).
One binary, one local index, four tools for your AI. No SaaS account, no API key, no cloud dependency.
Sense sits on your machine, has no learning curve, and isn't for you. It's for your AI.
New here? Start with the Guide for a map to every doc, by who you are.
A codebase is structure, not just text. The graph of what calls what, what breaks what, and how the team writes is real, knowable, and worth holding onto. Sense keeps it on your machine and hands it to your AI.
The rest follows from that. Local, because your code is yours. Read-only, because understanding shouldn't require permission to change things. Four tools, because your AI needs a few that work, not a hundred to pick from.
These aren't features. They're the reason Sense looks the way it does.
Four tools for your AI. A full CLI for you. No sprawl.
| Tool | Capability |
|---|---|
sense_graph | Symbol relationships, callers, callees, inheritance, tests, dead code |
sense_search | Hybrid bi-encoder + cross-encoder semantic code search with keyword and text fallback |
sense_blast | Blast radius, affected code, affected tests, risk score |
sense_conventions | Detected project conventions from source |
Your AI stops reading 30 files to answer "who calls this?" It stops hallucinating dependencies. It stops writing code that's correct but doesn't match how your team writes code.
Of the four tools, convention detection is the one nobody else does well. AI tools don't just struggle with structure. They struggle with style. They write correct code that doesn't follow how YOUR codebase writes code.
Sense detects patterns from your actual source code: key types and their declarations, framework idioms (Rails associations, Go interfaces, Django models), architectural layers, and naming conventions. Your AI follows these patterns because it sees them, not because it was told about them. Convention detection isn't a feature. It's the thing that makes AI-written code feel like it belongs.
Measured across 7 real-world codebases (Discourse, Flask, Next.js, Axum, Gin, Javalin, and a private e-commerce repo).
Full methodology and raw data: bench/. Head-to-head leaderboard against Serena, Probe, GitNexus, code-search MCPs: docs/bench-leaderboard.svg.
| Metric | Claude Code (Opus 4.6) | Claude Code (Opus 4.6) + Sense | Change |
|---|---|---|---|
| Tool calls per task | 19 | 10 | -47% |
| Tokens per task | 228K | 156K | -32% |
| Cost per task | $0.42 | $0.31 | -26% |
| Session time | 91s | 73s | -19% |
| Score per 100K tokens | 0.19 | 0.30 | +64% |
| Score per minute | 0.28 | 0.38 | +37% |
Same correctness, dramatically less work. Sense doesn't make the model smarter. It gives the model structural understanding so it stops wasting effort.
Tasks that require understanding code relationships, not just reading text, are where Sense pulls ahead.
| Task type | Baseline | + Sense | Why |
|---|---|---|---|
| Blast radius | 0.17 | 0.25 | Pre-computed dependency graph vs. manual grep chains |
| Find callers | 0.27 | 0.33 | Graph lookup vs. reading dozens of files |
| Dead code | 0.00 | 0.05 | Baseline can't do this at all |
| Semantic search | 0.36 | 0.38 | Two-stage retrieval (bi-encoder + cross-encoder) with text fallback |
Sense is structural understanding, not a general search engine. For tasks that are fundamentally text-grep (find a log message, locate a string literal), plain grep is the right tool and Sense adds nothing. Search text fallback (ripgrep) bridges some of this gap, but it's a fallback, not a replacement.
curl -fsSL https://luuuc.github.io/sense/install.sh | sh
Or download the binary for your OS from the latest release, unzip, and move sense somewhere on your PATH.
This is the beginning of the README. Read the rest at the original. Read the full README ↗