| name | searching-codebases |
| description | Binding-resolved Python symbol queries via pyright — every true caller (--refs), the real definition (--def), or an inferred signature (--hover) of a .py symbol, excluding the same-named false positives text search cannot tell apart. Use when a task needs ALL callers or users of a named Python symbol and grep would over-match. Python only, and only for the caller/definition question: measured 2026-07-04 on real issue-localization tasks, plain ripgrep tied or beat the semantic and indexed-regex tiers at 4-60x less wall-clock, so those tiers survive below without being a default. |
| metadata | {"version":"2.5.0"} |
Searching Codebases
Find code in any codebase by pattern or concept. One entry point, two
search strategies, automatic routing.
When NOT to use this skill
Python callers and definitions only. Every other code question is cheaper
elsewhere, and the measurement below is why this skill is edge-case-only.
| Situation | Use |
|---|
| What symbols does this file contain? | tree-sitting |
| Where is X defined, so I can read it? | tree-sitting |
| Any structure question, any language | tree-sitting |
| A literal string or regex | plain ripgrep |
| Which files are most about a concept? | bm25 |
| First look at an unfamiliar repo | exploring-codebases |
Non-Python code has no pyright binding resolution to offer, so there is no
version of this skill that applies to it.
Prerequisites
uv tool install ripgrep
tree-sitting installs automatically when needed — for --expand context
expansion and for the binding-resolved --refs/--def/--hover tier, which
uses it to resolve symbol positions. Only the bare tree-sitter package is
fetched; the language grammars ship bundled.
Primary Command
SKILL_DIR=/mnt/skills/user/searching-codebases
python3 $SKILL_DIR/scripts/search.py SOURCE "query1" ["query2" ...] [OPTIONS]
SOURCE is any of:
- Local directory path
- GitHub URL (downloads tarball automatically)
uploads (uses /mnt/user-data/uploads/)project (uses /mnt/project/)- Path to a
.zip or .tar.gz archive
Search Modes
Regex mode (patterns, identifiers, literal text):
python3 $SKILL_DIR/scripts/search.py ./repo "def handle_error"
python3 $SKILL_DIR/scripts/search.py ./repo "class.*Exception" --regex
python3 $SKILL_DIR/scripts/search.py ./repo "TODO|FIXME|HACK"
Semantic mode (concepts, natural language):
python3 $SKILL_DIR/scripts/search.py ./repo "retry logic with backoff" --semantic
python3 $SKILL_DIR/scripts/search.py ./repo "authentication flow"
python3 $SKILL_DIR/scripts/search.py ./repo "error handling strategy"
Auto-detection: short queries and code-like tokens → regex. Multi-word
natural language → semantic. Override with --regex or --semantic.
Binding-resolved mode (Python only — pyright via the python-lsp skill):
python3 $SKILL_DIR/scripts/search.py ./repo --refs SYMBOL
python3 $SKILL_DIR/scripts/search.py ./repo --def SYMBOL
python3 $SKILL_DIR/scripts/search.py ./repo --hover SYMBOL
Regex mode matches text, so a cross-reference for a function false-positives
on shadowed and same-named-but-unrelated symbols. --refs is binding-resolved:
pyright excludes the unrelated same-named symbol and follows imports. Use it when
you need a true "find all callers/users" for a .py symbol, not a text grep.
The tier is engaged lazily — pyright's index cost is paid only when you ask
for --refs/--def/--hover, never on ordinary searches. It is Python-only;
for non-.py sources, or when pyright/node is unavailable, it prints a one-line
degradation note and falls back to the regex text path. Each takes a single bare
symbol name and is mutually exclusive with the other two and with text queries.
Options
--regex / --semantic: Force search mode--refs SYMBOL / --def SYMBOL / --hover SYMBOL: Binding-resolved Python
queries via pyright (see Binding-resolved mode above)--expand: Return full function bodies via tree-sitting AST context--benchmark: Compare indexed regex vs brute-force ripgrep--branch NAME: Git branch for GitHub URLs (default: main)--skip DIRS: Comma-separated directories to skip--json: Machine-readable output-v: Show index stats and query routing decisions
How It Works
Regex search builds a sparse n-gram inverted index over all files.
Queries are decomposed into literal fragments, looked up in the index
to identify candidate files (typically 90-99% reduction), then verified
with ripgrep. Frequency-weighted n-grams make rare character sequences
more selective.
Semantic search builds a TF-IDF index over code chunks (functions,
classes, structural entries). Queries are ranked by cosine similarity.
Context expansion (--expand) uses tree-sitting's AST cache to
identify function/class boundaries, returning complete structural units
rather than line fragments. On first use, tree-sitting scans the repo
(~700ms for 250 files); subsequent expansions are sub-millisecond.
Small codebases (< 20 files) skip indexing entirely — direct ripgrep is
faster when there's nothing to narrow.
Mixed Queries
Multiple queries can use different modes in a single invocation. Each query
is auto-routed independently, and indexes are built once per mode:
python3 $SKILL_DIR/scripts/search.py ./repo \
"class.*Error" \
"error recovery strategy" \
"def retry"
Dependencies
- tree-sitting: Provides AST context expansion for
--expand and the
symbol→position resolution that seeds the binding-resolved tier
(--refs/--def/--hover). Auto-installs the bare tree-sitter package
when either is used (grammars are bundled). Regex and semantic search work
without it.
- ripgrep: Required for regex verification. Install via
uv tool install ripgrep.
- scikit-learn: Required for semantic mode. Installs automatically.
- python-lsp: Provides the binding-resolved tier (
--refs/--def/--hover).
Self-bootstraps pyright on first use and requires system node (v18+). Not
required — without it those flags degrade to the regex text path.
When to Use — narrow, by design
The ONE recommended use: binding-resolved Python symbol queries.
- "find all callers of
X" / "where is X really defined" for a .py
symbol, when same-named-but-unrelated symbols would pollute a text grep.
Empirical basis: rg get on psf/requests returned 232 hits, 224 of them
false; --refs get excluded all 224 (2026-06-15).
When NOT to Use — which is most of the time
Everything else. Measured head-to-head on real issue-localization tasks
(7 scikit-learn issues with merged fix-PRs, gold = PR diff files,
2026-07-04, replicating the file-discovery metric of arXiv:2602.11988):
- Literal tokens / identifiers: naive
rg -l tied or beat the indexed
tier on recall@10 in every instance, at 0.4s vs 25s.
- Concept / natural-language search: the TF-IDF semantic tier never
beat identifier grep — not even on identifier-poor issues, which are
themselves rare (~0.3% of merged-PR traffic in the sample).
- First encounter / "what is this repo": use exploring-codebases.
- Repos under ~20 files: read them.
The self-test before invoking: would plain rg return the same answer?
If yes, use rg. The indexed-regex and semantic tiers are retained for
completeness and for corpora where they may yet earn their cost (very
large repos, non-code document collections), but they carry the burden
of proof.
Files
scripts/search.py — Entry point, query routing, output formattingscripts/resolve.py — Input source resolution (GitHub, uploads, archives)scripts/context.py — tree-sitting-based AST context expansionscripts/ngram_index.py — Sparse n-gram inverted index, regex decompositionscripts/sparse_ngrams.py — Core n-gram algorithms, frequency weightsscripts/code_rag.py — TF-IDF semantic search over code chunksscripts/lsp_refs.py — Binding-resolved Python tier: symbol→position
resolution (tree-sitting), pyright queries (python-lsp), soft fallback
