oaustegard/exploring-codebases

Exploring Codebases

Exploratory code analysis for unfamiliar repositories. Orchestrates tree-sitting (structural) and featuring (semantic) over a local copy.

Workflow

Five numbered steps, in order. Do not skip step 0.

0. Setup (once per session)

uv venv /home/claude/.venv 2>/dev/null
uv pip install tree-sitter --python /home/claude/.venv/bin/python
export PYTHON=/home/claude/.venv/bin/python
export TREESIT=/mnt/skills/user/tree-sitting/scripts/treesit.py
export GATHER=/mnt/skills/user/featuring/scripts/gather.py

If step 2's --stats later reports Scanned 0 files ... Errors: 1, the tree-sitter core package isn't installed — come back here and install it (the engine bundles its own grammars and does NOT use tree-sitter-language-pack). Treesit fails silently on missing deps; it does not raise a useful error.

1. Get the repo (tarball, not per-file)

OWNER=...
REPO=...
REF=main                    # branch name, tag, or SHA. For a PR: pull/N/head
curl -sL -H "Authorization: Bearer $GH_TOKEN" \
  "https://api.github.com/repos/$OWNER/$REPO/tarball/$REF" -o /tmp/$REPO.tar.gz
mkdir -p /tmp/$REPO && tar -xzf /tmp/$REPO.tar.gz -C /tmp/$REPO --strip-components=1
ls /tmp/$REPO | head        # sanity check — did extraction land?

One HTTP call gets the whole repo. Do NOT curl README, cat files, or fetch via contents/PATH first — they're in the tarball. The Authorization header is only needed for private repos; public repos work without it.

Ref selection matters. If exploring a feature branch, PR, or tag, set REF accordingly. The default main will silently give you stale code if the question is about an unmerged branch.

2. Structural scan

$PYTHON $TREESIT /tmp/$REPO --stats

Read the output. It gives file counts, symbol counts, languages, and per-directory symbol density. This IS the orienting artifact — treat it as the product of this step, not warm-up.

Drill only if you have a specific question. For pure "what is this repo" exploration, skip drilling and go to step 3 — featuring surfaces the interesting paths for you. Drill when a user asked about a specific subsystem, or when step 3's output raises a question that needs source.

When you do drill, batch queries in one invocation. Every treesit call pays the full scan cost. Multiple queries added to the same command share that scan and each additional query adds ~0ms. If you're about to make a second treesit call on the same path, fold it into the first.

# GOOD — one scan, three answers
$PYTHON $TREESIT /tmp/$REPO --path=SUBDIR --detail=full \
  'find:*Handler*:function' 'source:main' 'refs:Config'

# BAD — three scans, three answers (3× the cost for the same information)
$PYTHON $TREESIT /tmp/$REPO --path=SUBDIR --detail=full
$PYTHON $TREESIT /tmp/$REPO 'find:*Handler*:function'
$PYTHON $TREESIT /tmp/$REPO 'refs:Config'

3. Feature synthesis

$PYTHON $GATHER /tmp/$REPO \
  --skip tests,.github,node_modules --source-budget 8000

Output includes a "Candidate areas for sub-files (by symbol density)" list near the top — that's your drill-target picker, ranked.

4. Reason about the combined output

Synthesize 2+3: capabilities, feature groups, architecture, entry points, anomalies. Produce _FEATURES.md when warranted. This is the LLM step; everything before was mechanical.

When to Use This vs Other Skills

| Situation | Use | |-----------|-----| | "I just cloned this, what is it?" | exploring-codebases (this skill) | | "Where is the retry logic?" | searching-codebases | | "Find all files matching class.*Error" | searching-codebases | | "Show me the symbols in auth.py" | tree-sitting directly | | "Which files are most about CSRF / sessions / queryset filtering?" | bm25 | | "Rank these docs by relevance to a multi-word concept" | bm25 | | "Document what this codebase does" | featuring directly |

Exploring is the divergent skill — you don't know what you're looking for yet. Searching is the convergent skill — you know what you want.

Pairing bm25 with this workflow

Once steps 2–3 have surfaced the rough shape of the repo, bm25 is the natural complement when you want ranked content search beyond grep and beyond exact-symbol lookup. It ranks files by lexical relevance to a multi-word query, which is useful for "what's this codebase actually about when I search for X?" — particularly when you don't yet know the symbol name to feed to tree-sitting.

BM25=/mnt/skills/user/bm25/scripts/bm25.py

# Pass multiple queries — index builds once, all queries reuse it
python3 $BM25 /tmp/$REPO 'auth flow' 'session backend' 'middleware pipeline' \
  --exclude 'tests/*' --exclude '*/tests/*' --top-k 5

Two patterns that pair especially well:

1. bm25 → tree-sitting. Use bm25 to find the top-ranked files for a concept; then tree-sitting source:Symbol:path/to/file.py to read the actual implementation.
2. bm25 with --exclude 'tests/*'. Test directories tend to dominate keyword queries because test names redundantly mention domain terms. Excluding them up front lands you on implementation files.

bm25 is corpus-agnostic — it'll also work on project knowledge stores or uploads/ if your exploration spans docs, transcripts, or PDFs.

Delegating to subagents (large repos only, and only if subagents exist)

Gate first: does this environment expose a subagent tool (Agent/Task in Claude Code and CCotw)? Claude.ai chat and bare-skill runs have none — run steps 2–4 inline and skip this section entirely. Never simulate fan-out by other means when the tool is absent.

When the tool exists and the repo is large (>1000 files or several distinct subsystems), keep steps 2–3 inline — they're mechanical and cheap — and fan out only step 4's judgment work, one agent per subsystem.

Subagents inherit nothing. Not your conversation, not this SKILL.md, not the knowledge that scan artifacts exist on disk. An agent prompted only with "explore crates/foo" will re-derive structure by ls/glob crawling at full tier cost. (Observed 2026-07-16: four Sonnet agents launched onto a 2,300-file repo without the handoff spent their opening turns running ls, with the full symbol index already on disk.)

Every subagent prompt must therefore carry:

1. Its structure slice, pre-computed. Partition the gather output's ## Public API section by subsystem path prefix, write each slice to a file, and point the agent at its file: "grep/Read this instead of listing directories." Small slices (<50KB) can be pasted inline instead.
2. The treesit recipe verbatim — the full command including the venv python path, plus the batch rule (one invocation, many queries; each invocation pays the scan, extra queries are free).
3. Anti-crawl instructions — no ls/Glob for discovery; Read only to confirm or expand a line range the slice or treesit already located.
4. An output spec — what to report, a line budget, and "file paths + line refs" so results are verifiable.

Routing (see the agent-routing skill): multi-turn exploration is outside Haiku's calibrated zone — use sonnet for subsystem agents and keep the final cross-cluster synthesis in the orchestrator.

Notes

• Large repos (>100 files): use --skip tests,vendored,docs,... in step 2 to focus the scan.
• Monorepos: treat each package/service as a separate exploration. Generate per-subsystem _FEATURES.md files linked from a root index.
• Drill heuristics (if step 2 drilling is warranted): directories with high symbol-to-file ratio (dense logic), entry-point names (main, cli, app, server, routes), files with many imports (integration points).