tsa-landing
tsa-landing is a Codebase mapping & onboarding skill for AI agents, published by aimasteracc in the tree-sitter-analyzer repository on GitHub. Skillspace rates tsa-landing D (62/100) and it passed the safety scan; the rating is based on static analysis only.
Land in a new (or familiar) codebase using the tree-sitter-analyzer MCP server. One workflow → 6 decision surfaces (project_card / entry_points / recent_signals / health / top_files / agent_next_step) → ≤2k tokens, ≤3 MCP calls. Use when: - First time entering an unfamiliar repository - Returning to a repo after >1 week - User asks "what is this project?" / "where do I start?" Workflow: parallel-fan-out 3-4 MCP tools, fold output, return decision_surface. Replaces the typical 15k-token bootstrap (read README + ls -R + git log + AGENTS).
- Grade
- D
- Quality
- 62 / 100
- Safety
- Passed
- GitHub stars
- 43
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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