legacy-code-intel

name: legacy-code-intel description: "Use when you want a PERSISTENT, queryable, SCIP-inspired code-intelligence library over legacy artifacts — COBOL, DataStage .dsx (XML), ETL scripts (shell/SQL/Python), and Pick/MultiValue BASIC (UniVerse/UniData/D3/jBASE/OpenQM) — built ONCE into a content-addressed store and exposed via a deterministic graph-query layer + a self-contained HTML navigator. An LLM-as-parser framework (mirrors lineage-extract-static): model-neutral prompts the in-session AI CLI uses to extract symbols/occurrences/relationships, NOT per-format AST parsers. Triggerable as a single skill (ingest one artifact/dir) and via agent-teams (batch, one worker per artifact — NO new agent). Static analysis only (v1). Also trigger on "index this COBOL", "symbol graph for DataStage jobs", "where is this paragraph called", "impact of changing this copybook", "index this Pick/MultiValue BASIC", "code intelligence for legacy", "build a navigator for this mainframe code"."

legacy-code-intel — persistent SCIP-inspired code-intelligence library

S-cycle design at docs/plans/2026-06-09-legacy-code-intel-skill-design.md.

What it does

Ingests legacy artifacts into a SCIP-inspired custom code-intelligence index (symbols + occurrences + relationships — SCIP's package/type hierarchy dropped), stores it once in a content-addressed library (~/.codelib), and exposes:

• a deterministic query layer (query.py) — find_symbol, defs, refs, impact, list_artifacts, subgraph_for_llm — byte-identical stdout across runs;
• a self-contained HTML navigator (render_navigator.py) — Cytoscape relationship DAG + sortable symbol/occurrence/relationship tables + client-side search + CSV / ndjson exports, XSS-safe and air-gap-safe.

It is a framework: the in-session AI CLI (Claude Code / Codex CLI / Gemini CLI / Copilot CLI) is the parser via model-neutral prompts; scripts/ has NO per-format AST parsers (the lineage-extract-static precedent). Scope: COBOL + DataStage DSX + ETL + Pick/MultiValue BASIC. Both triggers: skill (one artifact/dir) and agent-teams (batch). No new agent.

When to use it

• "build a code-intelligence index / navigator over this COBOL/DataStage/ETL tree"
• "where is paragraph X called from", "what does this copybook touch"
• "impact of changing this paragraph/stage/table" (advisory until gold-cleared)
• "give me a symbol graph for these legacy artifacts"

Skip when:

• The code is in a language with a native LSP/SCIP indexer (use that — this is for legacy formats without one).
• You want RUNTIME traces (this is static analysis only, v1).
• You want data/process LINEAGE (datasets → jobs) — that's lineage-extract-static (cross-linked here via the shared content_sha256).
• You want a generated PROJECT.md front-door for a modern repo — that's code-comprehension.

Components (one skill; mirror lineage-extract-static)

prompts/     analyze-symbols.md (+ cobol.md / dsx.md / etl.md / pick.md addenda),
             merge-chunks-within-file.md, resolve-symbol-id.md, redact-secrets.md   (model-neutral)
scripts/     chunk_file.py, accumulate.py, redact.py (fail-closed), emit_index.py,
             store.py (SINGLE WRITER), query.py (deterministic), render_navigator.py,
             fingerprint.py, goldcheck.py
schemas/     code-index.v1.json, code-finding.v1.json, library-catalog.v1.json   (VALID draft-07, lowercase)
templates/   navigator.html.j2 (XSS-safe, air-gap, Cytoscape)
references/  symbol-vocabulary.md, confidence-classifier.md, store-layout.md, anti-patterns.md
gold/        cobol/sample.gold.json   (the accuracy oracle)
tests/       unit + 4 anti-requirement tests + gold harness + round-trip

Ingest flow (single artifact)

When a user issues legacy-code-intel ingest <artifact|dir> (or equivalent NL):

1. Agent reads this SKILL.md (the orchestration playbook).
2. Compute content_sha256 (fingerprint.py content) + the pipeline_fingerprint
   (fingerprint.py pipeline --prompt-hash <prompt-hash> --model-id <this CLI>).
3. STORE PROBE (dedup): store.py probe --content-sha256 H --pipeline-fingerprint F.
   - exit 0 (HIT) -> already indexed with this exact pipeline; SKIP the LLM pass
     entirely (zero LLM calls) and go straight to promote/render. "Process once."
   - exit 3 (MISS) -> continue.
4. chunk_file.py <artifact> <run_id>  -> per-file manifest + chunk placeholders
   at ~/.cache/legacy-code-intel/runs/<run_id>/files/<sha>/ (0700, NEVER /tmp).
5. For each chunk: read it, apply prompts/analyze-symbols.md + the format addendum
   (auto-detected: cobol/dsx/etl/pick — Pick BASIC often has no extension, so it is
   detected by content: SUBROUTINE / READNEXT / <a,v,s> / OCONV / CRT), emit ONE code-finding.v1 JSON object into
   chunk_NNNN.jsonl. Classify confidence per the bright-line rule. Use
   prompts/resolve-symbol-id.md for path-INDEPENDENT IDs.
   DSX: the agent loads the .dsx XML via defusedxml (XXE HARD-RULE) before analysis.
6. accumulate.py <chunk_dir> <run_id> <sha>  -> deterministic boundary-merge ->
   summary.json (NOT an LLM judgment).
7. emit_index.py summary.json --output index.json --content-sha256 H --format <fmt>
   --source-path <path> --line-count N --model-id <CLI> --prompt-hash H
   --pipeline-fingerprint F   -> assembles + schema-validates code-index.v1
   (closed kind-enum + bright-line classifier re-check).
8. redact.py index.json --output index.redacted.json  (FAIL-CLOSED — abort on error,
   NEVER partial-store). Legacy is credential-dense.
9. store.py persist index.redacted.json  -> writes the immutable object derivation +
   path ref, then promotes catalog/latest.json under the flock (single writer).
10. render_navigator.py --output-dir <dir>  -> navigator.html + CSV/ndjson exports.
11. (optional) goldcheck.py index.redacted.json gold/cobol/sample.gold.json --record
    -> records call-edge precision; keeps impact() advisory below 0.85 (design §8).
12. Print the navigator location + the accuracy number to the user.

Batch flow (agent-teams, NO agent)

1. discover + classify artifacts under the target tree (auto-detect format).
2. dedup pre-filter: for each, fingerprint + store.py probe; drop the HITs.
3. agent-teams fan-out: one worker per remaining artifact, each runs steps 2-9 of the
   ingest flow with a bob-issued claim token (workers write DISJOINT objects/<sha>/
   dirs — safe; only the catalog promote serializes on the flock).
4. ONE serialized promote (store.py promote) after all workers finish.
5. render_navigator.py once over the promoted catalog.

No new agent: code-comprehension proves a claimless skill + agent-teams does batch with no agent, and a new agent would hit bob_subagent_depth_restriction (loses the Task tool when spawned as a subagent → batch silently serializes).

CLI

legacy-code-intel ingest <artifact|dir> [--store PATH] [--output-dir DIR] [--no-vendor]
legacy-code-intel query <op> [args] [--store PATH]
    ops: find_symbol <q> | defs <q> | refs <q> | impact <q> | list_artifacts
         | subgraph_for_llm --anchors A B …
legacy-code-intel render --output-dir DIR [--store PATH] [--no-vendor]
legacy-code-intel goldcheck <index.json> <gold.json> [--record] [--store PATH]

The scripts are independently runnable (e.g. python3 scripts/query.py impact PAYROLL). --store defaults to ~/.codelib (or $LCI_STORE); pass --store .codelib for a project-local store.

HARD-RULEs (enforced by tests)

1. Fail-closed redaction before any store write (legacy = DSN/credential-dense); a redaction error aborts the artifact — NEVER partial-store.
2. Bright-line confidence classifier (grounded/inferred/speculative); interpolation / dynamic CALL / COPY REPLACING / DSX RCP → forced speculative (emit_index._looks_dynamic, defense-in-depth over the prompt).
3. Deterministic output — store records + query stdout + navigator are byte-canonical (sort_keys, SOURCE_DATE_EPOCH sentinel, atomic .tmp.<pid> + os.replace).
4. XSS-safe navigator — html.escape server-side (Jinja |e) + textContent client-side; embedded JSON has < > & neutralised. test_html_escape_hostile_symbols.
5. Single-writer store + flock'd promote — producers NEVER write catalog/latest.json (CB4); the catalog is promoted only under fcntl.flock(.promote.lock) + atomic os.replace. test_concurrent_promote.
6. Model-neutral prompts (Claude Code / Codex CLI / Gemini CLI / Copilot CLI — no vendor anchors, no instruction XML tags).
7. DoS caps — 50 MB/file skip-with-warn, 2000-line chunks, 0700 cache (reused from lineage).
8. 0700 store, NEVER /tmp; defusedxml for DSX (XXE); path-INDEPENDENT symbol IDs; pipeline-fingerprint dedup key.

Anti-requirements (the discarded agy build's bugs — MUST NOT recur)

The design was built CLEAN after a rogue agy analyst auto-committed a broken version (reverted/deleted). Its four confirmed bugs are regression-tested:

• schema must validate under Draft7Validator.check_schema (lowercase) → test_schema_valid
• catalog writes must be flock'd single-writer → test_concurrent_promote
• query must be deterministic (adjacency index, edge sort, real budget) → test_query_determinism
• pipeline_fingerprint computed in the ingest path + round-trips → test_dedup_cache_hit

See references/anti-patterns.md for the full list (11 items).

Accuracy gate (design §8)

The real failure mode: the LLM call-graph can be wrong, there is no native oracle for COBOL/DSX, and the confidence tag flags uncertainty but not incorrectness — so without measurement the store becomes a write-once never-trusted graveyard. Mitigation:

• Gold-file harness: gold/cobol/sample.gold.json (hand-labeled call-edges) + goldcheck.py computes call-edge precision/recall; the navigator header + the catalog report it.
• impact() is advisory-by-default: until a format's gold precision clears 0.85, impact() carries the speculative framing + advisory: true. COBOL ships its gold file in v1; DSX + ETL + Pick extraction prompts ship too but their gold files are a tracked fast-follow — until then DSX/ETL/Pick impact() stays advisory (same gate).

Composition with other skills

• lineage-extract-static — data/process lineage over the SAME artifacts; cross-linked via the shared content_sha256 (advisory join in the navigator).
• code-comprehension — generated PROJECT.md front-door for modern repos (claimless, agent-teams batch precedent reused here).
• wiring-query — the graph_ops.py adjacency-BFS shape ported into query.py.
• wiring-reconcile / project-state — the flock + atomic-os.replace promote pattern ported into store.py.
• cobol-developer / datastage-developer / pick-developer — the per-format symbol vocabulary.

Security — XML / XHTML parsing

For the navigator's HTML output, all user-controlled strings are html.escape-d server-side and inserted via textContent client-side — never raw innerHTML. See llm-security for context-appropriate escaping rules.