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CLAUDE.md

Agent guidance for codellm-devkit/codeanalyzer-typescript (cants).

What this project is

cants = TypeScript/JavaScript static analyzer built on TypeScript compiler (via ts-morph). CLDK TypeScript backend: emits canonical schema v2 — one additive Code Property Graph — in two projections, analysis.json and Neo4j property graph. Mirrors Python and Java sibling analyzers, so output-shape parity with them first-class concern.

Schema v2 — the additive CPG (read this before touching output)

Output = one scale-free structure: containment tree of nodes (id / kind / span / children) with typed edge overlays (CPG). Every classic artifact — symbol table, call graph, CFG, PDG, SDG — is projection of that one structure. Analysis levels = how deep it populated (each level only adds, never rewrites):

  • L1 (-a 1): tree to callable depth — application → symbol_table{module} → types{}/functions{}/fields{} → callables{} — plus call nodes in each callable's body{} (callee unresolved). source stored once per module; every node's text slices off it via span.bytes.
  • L2 (-a 2): call_graph edge list (callable→callable) at application scope, and callee slot on each call node refined null → id (only sanctioned mutation).
  • L3 (-a 3): rest of body{} (statements + @entry/@exit) and intra-callable edge lists cfg/cdg/ddg (reaching-definitions, prov:["reaching-defs"]) hung on each callable.
  • L4 (-a 4): synthetic @formal_in:N/@formal_out/<L>/actual_in:N/<L>/actual_out vertices, intra-caller summary edges, and application-scope param_in/param_out lists (interprocedural SDG).

Identity two-tier: durable can://<lang>/<app>/<file>/<type>/<sig> ids at callable depth and above; ordinal <callable-id>@<line>:<col> (or @<tag>) below. Intra-callable edge lists use bare local ids; cross-callable lists use fully-qualified can://…@local ids. L1 ⊆ L2 ⊆ L3 ⊆ L4 = CI-checkable monotonicity gate (test/schema-v2.test.ts). Model + every decision live in .claude/SCHEMA_DECISIONS.md (§ "Schema v2 migration") and skillset's canonical-schema.md.

Provider/client boundary: analyzer = pure graph provider — emits graph substrate (CFG/PDG/SDG + summary edges) and stops. Slicing and taint = reachability queries over it, belong to frontend SDK; never add taint_flows section here.

Schema v2 = native model (#96): stages build v2 tree directly (src/schema/schema.ts, one model family — no v1 model, no emit-time reshape). Per-run passes stamp derived layers (python parity): assignIds (can:// ids — per-run because ids embed --app-name while cache round-trips tree), l1Body (call_sites → body{}), heritage, homing + l2Callees (L2), dataflow/attach (L3/L4). finalizeAnalysis (src/schema/emit.ts) runs them + assembles envelope + strips INTERNAL fields (call_sites, abs_path, cache trio).

Call graph = tsc resolver + defuse linker (#98): deterministic per-callable pass over resolver leftovers — alias chains, decorator edges, library-callback edges, bounded interprocedural votes, CHA-by-name fallback. No whole-program fixpoint, no backend flag, one code path. Module-scope calls attributed to MODULE (python #131 parity). prov tags: tsc / defuse / import. Joern superset ledger: docs/design/specs/defuse-linker-joern-ledger.md.

Architecture — follow the pipeline

Whole analyzer = one orchestration function: analyze() in src/core.ts. Read it first; everything else is stage it calls, in order:

  1. materialize (src/build) — resolve/prepare target project deps.
  2. buildSymbolTable (src/syntactic_analysis) — modules, classes, interfaces, enums, type aliases, namespaces, functions, methods, variables, decorators, JSDoc, with precise source spans.
  3. call graph (src/semantic_analysis) — tsc resolver (callGraph.ts, incl. module-scope sweep + RTA + phantoms) then defuseLinker.ts tiers T1–T5; merged with provenance union.
  4. program graphs (src/dataflow) — levels 3–4 (-a 3/-a 4): CFG → post-dominance/CDG → access-path def-use → PDG → SCC-condensed bottom-up summaries → SDG. This is compute (IR in src/schema/graphs.ts); src/dataflow/attach.ts writes it onto tree (body{} + cfg/cdg/ddg/summary per callable + param_in/param_out). Decisions: .claude/SCHEMA_DECISIONS.md; contract + staged follow-ups: issue #2.
  5. cache (src/utils/cache.ts) — content-hash cache under .codeanalyzer/; stores id-free builder tree only (ids/body/heritage = per-run layers; levels 3–4 also record summaries + dependency edges in graphs_summaries.json).
  6. finalize + output — finalizeAnalysis (src/schema/emit.ts, called by analyze()) runs pass spine, returns AnalysisResult {application (wire TSAnalysis envelope), internal, program_graphs, gates}; src/utils/serialize.ts writes envelope verbatim; src/build/neo4j projects same envelope into graph.cypher snapshot or incremental Bolt push. --emit neo4j always full-depth (levels gate JSON path only; combining -a/--graphs with it = error).

Output shape = schema v2 (src/schema/schema.ts: TSAnalysis envelope → TSApplication root → TSModule/TSType/TSCallable/TSField/TSBodyNode). Same types = the model stages build; INTERNAL fields never reach wire. Neo4j schema (src/build/neo4j/schema.ts) versioned and enforced by conformance test — treat both as contracts, keep in lockstep with JSON.

Directory map

Path Responsibility
src/main.ts, src/cli.ts Entry point + Commander CLI
src/core.ts analyze() orchestrator — the spine
src/options Parsed CLI options / AnalysisOptions
src/syntactic_analysis Symbol table (ts-morph traversal)
src/semantic_analysis Call graph: tsc resolver + defuse linker (T1–T5), phantoms
src/dataflow L3/L4 program-graph compute (CFG, dominance/CDG, def-use, summaries, SDG) + attach.ts (IR → tree)
src/schema the native v2 model (schema.ts) + per-run passes (assignIds/l1Body/heritage/homing/l2Callees) + emit.ts (finalizeAnalysis) + signatureOf + graphs IR
src/build Dep materialization; build/neo4j = the v2 graph projection (project/rows/cypher/bolt/schema)
src/utils fs, caching, logging, serialization (serialize.ts writes the envelope), version
test Bun tests + fixtures/sample-app + fixtures/dataflow-app; schema-v2.test.ts = the L1–L4 gates

Repository-artifact layer (#101, python v1.3.0 parity): three level-free sections — application.artifacts{} (never-drop inventory, LANGUAGE-NEUTRAL can://artifact/<app>/<path> ids, roles[], text-capture policy: --no-artifact-text/--artifact-text-max-bytes, sha256/ size_bytes always full-file even when source is a truncated prefix), dependencies[] (npm kinds incl. coined peer, direct:false lockfile-only transitives), unresolved_imports[] (@types type-only rule; --resolve-installed opt-in probe). Each artifact also carries config_keys[] (env/JSONC/YAML/TOML/INI/dockerfile namespaces, @key/-suffixed ids, arg./env. internal id disambiguation). config_uses/config_reads join a config_access L1 body-node read (or a detector-table call) to a declared key through a level-graded literal→dataflow-intra→dataflow-interproc tier (src/semantic_analysis/configUse.ts, src/dataflow/configUse.ts); config_reads deliberately SHRINKS as -a rises — the layer's one non-monotonic section. src/artifacts/. Neo4j contract 2.1.0 (SCHEMA_VERSION unmoved — every analyzer re-baselines together later): NEUTRAL :Artifact/:Package/:ConfigKey (purl) — sanctioned prefix exception — plus TS_PROVIDES/TS_UNRESOLVED_IMPORT into :TSExternal ghosts and TS_USES_CONFIG into :ConfigKey. Consumer query skill: docs/skills/analyzing-cants-graphs/.

Commands

  • bun run start -- --input /path/to/project — run analyzer from source.
  • bun run build — compile standalone dist/cants binary.
  • bun test — run tests. Container tests: bun run test:container (needs Docker).
  • bun run typecheck — tsc --noEmit.
  • bun run gen:schema — regenerate schema.neo4j.json.
  • bun run gen:readme — regenerate README's cants --help block.

I implement features myself — you assist

For feature work, I write the implementation to stay fluent in my own analyzer. Act as helper, not author:

  • Don't write feature code or apply edits to implement it unless I explicitly ask ("write this", "implement X", "apply it"). Default to guiding, not doing.
  • Do move me fast: explain relevant stage, point at prior art (e.g. existing call-graph provider in src/semantic_analysis as template for new one), sketch signatures/types, outline approach, answer questions about codebase.
  • Review on request: when I share diff or push, critique it — correctness, parity with Python/Java backends, schema conformance, missing tests, edge cases — and suggest concrete improvements.
  • Scaffolding like tests or boilerplate fine when I ask; otherwise leave keyboard to me.
  • If you think I'm about to go wrong, say so briefly and let me decide — don't pre-empt by implementing the fix.

Rules

  1. Think before coding. State assumptions explicitly; ask rather than guess. Push back when simpler approach exists. Stop when confused.
  2. Simplicity first. Guide me toward minimum idiomatic code that solves the problem. Nothing speculative; no abstractions for single-use code.
  3. Issue → branch → work → PR. Every change starts as issue, on branch named feat/issue-XXX, fix/issue-XXX, chore/issue-XXX, lands via PR.
  4. Guard the contract. Changes to src/schema or Neo4j output must keep parity with sibling analyzers and pass schema conformance test.

Goal-driven execution, as a teaching loop

Success measured by sole fact that I understand it. Success criterion: I can point to exact line of code where any feature lives, however remote or obscure, and explain why it's there and how it behaves.

To that end, be my teacher and Socratic one — not answer key:

  • Lead with questions that make me derive answer; don't hand me solution.
  • Verify understanding, not just behavior — have me locate and explain relevant LOC, walk edge cases, predict what change would do before running it.
  • Teach, help improve, strengthen weak spots you surface; circle back to them.
  • Loop closes when I can teach it back and place every feature on a line, not merely when tests pass.
  • Over session, frequently — but not so much that I am stymied — ask spaced repetition questions so concepts internalized.

Learning progress tracked globally, not per-repo: see SRS deck and "continual learning" defaults in ~/.claude/CLAUDE.md.

Auxiliary support tasks

Tidy up the release announcement

Every vX.Y.Z tag makes release workflow (.github/workflows/release.yml) auto-post announcement to Announcements discussion (this repo, and org mirror in codellm-devkit/.github). That body machine-generated from release notes / PR titles — mis-categorizes changes, includes chore(release) noise, buries breaking changes.

When working in this repo, check whether latest release's announcement still needs cleanup, and if so, fix it:

  1. Find it. gh release view --json tagName,publishedAt; then list recent discussions via gh api graphql (repository → discussions), match category Announcements and title vX.Y.Z. Keep discussion node id, read its body.
  2. Skip if already done. If body starts with <!-- cleaned-up --> (or already reads as clear, human-written announcement), do nothing.
  3. Otherwise rewrite it into clear, user-facing announcement, grounded in CHANGELOG.md and referenced PRs/diff (not auto-grouping — verify each change; never invent anything):
    • breaking changes first, each with one-line migration step;
    • plain-language highlights (what it does, not PR title);
    • upgrade lines — pip install -U "codeanalyzer-typescript==X.Y.Z", or brew upgrade codellm-devkit/homebrew-tap/codeanalyzer-typescript, or shell installer one-liner;
    • links to GitHub release and CHANGELOG.md.
  4. Update in place. Edit discussion body with GraphQL updateDiscussion mutation (don't open new one), prepend <!-- cleaned-up -->, mirror same body to org discussion. This task only reads code and edits Discussions — makes no commits.