Explore AI Agent Skills & Claude Prompts

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).

schedule Updated 15 days ago

tsa-pr-review

AST-grounded PR / diff review. One workflow → per-file risk ranking, blast radius per changed symbol, the exact pytest command to gate merge, any architecture-constraint violations, and a final BLOCK / REVIEW / APPROVE verdict — in ~1–2k tokens and 4–6 MCP calls. Goes beyond a generic LLM diff-read because only TSA's `analyze_change_impact` returns a deterministic `verification_command` + `queue_ledger`, and only the persisted call graph can enumerate true callers/callees of changed symbols. (Per `docs/internal/COMPETITOR_HEAD_TO_HEAD_2026-05-23.md`: CodeGraphContext silently indexes 0 files, grep-ast crashes on Python 3.14, wrale miscounts imports. A pure-LLM diff reader has none of these guard-rails — it cannot reach the AST cache to enumerate callers, cannot consult `architectural-constraints.yml`, and invents a pytest command instead of reading the one TSA computed.) Use when: - Reviewing a local diff (`git diff`, staged, branch-vs-main) - Reviewing a GitHub PR URL - User asks "is this PR safe to merge?

tsa-graph

Code archaeology via call graph + symbol resolution. Answer "who calls X", "what does Y call", "where is Z defined", "what's the path from A to B" in one MCP call instead of multi-step grep + read. Uses persisted cross-file resolution (Synapse) so cross-module edges are precise, not regex-guessed. Use when: - User asks "what calls this function" / "what does this function call" - Tracing a bug through layers (impact → caller chain) - Planning refactor of a function/class (need full fanout) - "Where is this symbol defined" / "find all references to X" - "Show me the path from handler → DB" - "Draw a UML class/package/component/sequence diagram" Replaces: grep + read + manual chain-following (~10-30k tokens) with 2-4 MCP calls (~1-3k tokens).

schedule Updated 15 days ago

tsa-temporal

Find "hot zones" — symbols modified often in recent git history that need extra review attention. Adds temporal context (mod_count_30d / 90d / all) to call-graph queries. Like Hebbian "fire-together-wire-together" but for code: functions that change together often deserve scrutiny together. Use when: - User asks "what's churning the most" / "any hot zones?" - Pre-refactor: "what's the history of this function?" - Code review: "is this file getting hammered?" - You see a verdict=CAUTION with "hot zone" in risk_factors Replaces: `git log --follow --stat` + manual counting per-symbol (~10k tokens for non-trivial files) with 1 MCP call (~500 tokens).

tsa-structure

Structural analysis of one file — classes, methods, exports, common patterns, semantic classification of a diff. Returns the file's *shape* without reading the file body. Use when: - "What classes and methods does X file have" - "Show me a table / outline of <file>" - "Run query: find all decorated functions in file X" - "Is this diff a refactor or a behavior change" (semantic classification) - "Find patterns like singleton / factory in this code" Replaces: reading the whole file (~20k tokens for big files) with structural views (200-1000 tokens).

tsa-refactor-queue

Build a top-N prioritized refactoring queue by intersecting three signals: health grade (which files are F/D), temporal churn (which files change most often), and dead-code density (which files carry the most unreachable symbols). For each candidate the queue surfaces (a) the dimension that dragged the grade down, (b) the target symbol, (c) blast radius from `codegraph_callers`, and (d) a concrete action — `split`, `delete dead`, or `extract`. Motivated by `docs/agent-tooling-gap-report.md` "Next High-Value Work" §5: use `check_project_health` output to open focused refactoring slices for the current F-grade files, starting with low-coverage Language Plugin extractors and `api.py`. Use when: - "Where should I refactor next?" / "Build me a refactor queue" - Post-feature cleanup pass - Engineering planning: "what would 2 weeks of cleanup buy us?" - Re-grading after a sprint: "did the queue shrink?" Replaces: per-file `check_file_health` loops + spreadsheet ranking (~25k tokens) with 3 parallel MCP calls + a

tsa-health-watch

Project & file health grading + dead-code detection + watch-daemon for grade drops. Answer "how healthy is this codebase", "what's rotting", "which files need attention", "alert me when something degrades" in one workflow. Use when: - Triaging a codebase: "what should we clean up first?" - Pre-PR check: "did this change make health worse?" - User asks "any dead code?" / "any rotting hot spots?" - Starting long-running session, want auto-alerts on degradation - Project-wide quality reporting Replaces: 5-10 grep/read calls + manual heuristics + spreadsheet (~20k tokens) with 2-3 MCP calls (~2k tokens).

tsa-find

Fast file + content search with code-aware sizing. Replaces Read/Grep/find for routine "where is this file" / "grep for X" / "show me lines 10-20 of Y" questions. Returns file paths + line numbers + a sized chunk, not the whole file. Use when: - "Find files matching <pattern>" / "show me all *.yml under config/" - "Grep for 'TODO' / 'FIXME' / 'TODO\\(perf\\)' / regex anywhere" - "How big is <file>" / "is this file too large to read fully" - "Show me lines 50-80 of <file>" / "read just the relevant slice" - "Find all files matching name + containing string" Replaces: native find + grep + cat invocations (~3-10k tokens for big repos) with single MCP calls (200-500 tokens).