tree-sitter-analyzer: Skill for Claude Code

.claude/skills/tsa-landing/SKILL.md

tsa-landing is a skill for Claude Code from aimasteracc/tree-sitter-analyzer. It costs 142 tokens per session (1,422 once invoked), scanned A, original, MIT.

A repository-orientation workflow that uses a tree-sitter analyzer to summarize an unfamiliar codebase through project structure, entry points, recent changes, health, important files and a next step. A repository is the project folder containing the code and its history.

In plain words
What is it for?
Use it to learn where a project starts, assess its health, see recent activity, identify important files and choose the next investigation step.
Why use it?
It gives an agent a compact starting view of a project without requiring many separate inspection calls. It is intended for first visits or when returning after a long break.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths; mentions CLAUDE.md; mentions AGENTS.md.

This is aimasteracc/tree-sitter-analyzer's own configuration. It tells Claude Code how to work on tree-sitter-analyzer itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything tree-sitter-analyzer configures →

Reuse

Borrowing it

Nothing to install: this file belongs to aimasteracc/tree-sitter-analyzer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/aimasteracc/tree-sitter-analyzer/main/.claude/skills/tsa-landing/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/aimasteracc/tree-sitter-analyzer

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for tsa-landing

README.md
[![agentmods](https://agentmods.dev/badge/skills/aimasteracc/tree-sitter-analyzer/tsa-landing.svg)](https://agentmods.dev/skills/aimasteracc/tree-sitter-analyzer/tsa-landing)
Your own site
<a href="https://agentmods.dev/skills/aimasteracc/tree-sitter-analyzer/tsa-landing"><img src="https://agentmods.dev/badge/skills/aimasteracc/tree-sitter-analyzer/tsa-landing.svg" alt="Measured on agentmods" height="20"></a>
Per session 142 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,422 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00142 $0.01422
Opus 5 $0.00071 $0.00711
Sonnet 5 $0.00028 $0.00284
Haiku 4.5 $0.00014 $0.00142

Measured 8d ago against content hash ded7e072cd7e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

tsa-landing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/tsa-landing/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

tsa-landing — Land in any repo in <3s

First action on entering a new repo. Replaces 6 separate bootstrap calls (~15k tokens) with 3-4 parallel calls (~2k tokens). 92% token saved.

When to use

  • You just entered an unfamiliar project (no Claude.md / no skim yet)
  • You return after long gap and need "what's the state?"
  • User asks any of: "what is this project / where do I start / what's the entry / recent changes / health"

Don't use when:

  • You already have full context (just continue working)
  • User wants to read source — use structure action=read directly

Procedure

Step 1 — Verify tools available

uv run python -m tree_sitter_analyzer --check-tools --format json | head -5

If fd or rg missing, stop and tell user how to install.

Step 2 — Fan-out 4 MCP calls (in single message, parallel)

Call these 4 tools in ONE message (parallel tool use):

  1. project action=overview (no args) — project_card + entry_points
  2. health action=project with max_files: 5 — grade distribution + weakest dimension
  3. edit action=impact with mode: "branch" — recent_signals (last commit, ahead-of-main)
  4. project action=workflow (no args) — current_phase + recommended_commands

Step 3 — Fold and emit decision_surface

Combine into single Decision Surface:

{
  "project_card": {
    "name": <from project action=overview project_root basename>,
    "primary_language": <key with the highest count in project action=overview summary.by_language — it is a {language: file_count} dict sorted descending, so the first key>,
    "language_mix": <from project action=overview summary.by_language — first 3 keys of the {language: file_count} dict>,
    "size": {
      "files": <from project action=overview summary.total_files>,
      "loc": <from project action=overview summary.total_lines>
    }
  },
  "entry_points": <from project action=overview entry_points>,
  "recent_signals": {
    "last_commit": <git log -1 --oneline via Bash>,
    "ahead_of_origin": <git rev-list --count via Bash>,
    "uncommitted_files": <git status --short | wc -l via Bash>,
    "branch": <git branch --show-current via Bash>
  },
  "health": {
    "verdict": <from health action=project verdict>,
    "risk": <from health action=project agent_summary.risk>,
    "grade_distribution": <from health action=project grade_distribution>,
    "weakest_dimension": <from health action=project weakest_dimension>
  },
  "top_files_to_know": [
    "AGENTS.md",
    "CLAUDE.md",
    <from project action=overview entry_points>,
    <top 3 from health action=project top_refactoring_targets>
  ],
  "agent_next_step": {
    "if_asked_what_is_this":
      "Read AGENTS.md (canonical contracts) + docs/CODEMAPS/architecture.md (topology). Stop after 2k tokens.",
    "if_asked_to_add_feature":
      "Call project action=workflow → follow phase_order. Use TDD (write test first).",
    "if_asked_to_fix_bug":
      "Call health action=patterns file_path=<file> → edit action=refactor file_path=<file>. Cross-ref nav action=lineage if symbol-level.",
    "if_asked_about_test_status":
      "Run: uv run pytest -q (5-min cap). Project enforces xdist parallel, ~5min for 15k tests."
  },
  "summary_line": "<project> files=<N> py=<X%> grade=<G> recent=<commit_subj>",
  "verdict": "INFO"
}

Read the full file on GitHub · 138 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 138 lines · 142 tokens per session scan A ded7e072cd7e

Subscribe to this mod's changes

tsa-landing is a skill published in the GitHub repository aimasteracc/tree-sitter-analyzer (49 stars, last pushed today), licensed MIT. It adds 142 tokens to every session and 1,422 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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