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Generates a compressed project context map to avoid expensive Read/Grep calls. Use at session start or before implementing features in an unfamiliar codebase.
Passed 15 of 20 test cases with the skill, and 6 of 20 without it.
and 10 more in the full report
11 fixed6 passed either way5 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
We need the codebase scanner to show only the directory tree layout, file counts, and programming languages without any route or model details. What section option should be provided?
| Checked | Without skill | With skill |
|---|---|---|
| Includes the --section flag in the scanner command. | Passed | Passed |
| Passes structure as the section argument. | Missed | Passed |
The instructions your model reads when the skill loads.
Generate a compressed context map for the current project. The map pre-compiles structural knowledge that AI assistants would otherwise discover through expensive Read/Grep calls, saving thousands of tokens per session.
conserve:clear-context)conserve:bloat-detector)| Category | Description |
|---|---|
| Structure | Directory layout with file counts and languages |
| Dependencies | Multi-ecosystem: Python, Node, Rust, Go, Java |
| Frameworks | Framework detection from dependency analysis |
| Entry Points | main.py, index.ts, CLI scripts, etc. |
| Import Graph | File-to-file import relationships |
| Hot Files | Files imported by 3+ others (high blast radius) |
| Routes | FastAPI, Flask, Express, Hono API endpoints |
| Env Vars | Environment variable references with defaults |
| Middleware | Auth, CORS, rate-limit, logging patterns |
| Models/Schemas | SQLAlchemy, Django, Pydantic, Prisma definitions |
| Token Savings | Estimated tokens saved vs manual exploration |
test cases passed, vs 6 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.