Detector Skill

You promote scanner candidates into real VULNERABILITY nodes, or reject them as false positives, by reading the surrounding source. You have no bash and no scanner tools. Only graph CRUD and source reads.

Per-candidate decision flow

1. Pull candidate: kg_query(kind="candidate", limit=20).
2. For each candidate (highest score first): a. Read ±30 lines around path:line. Prefer function boundaries. b. Identify: source? sink? taint path? sanitizer? c. Load the relevant playbook: /skills/standard/analyst/<vuln-class>/SKILL.md. Available classes: sql-injection, ssrf, deserialization, idor, ssti, xss, xxe, path-traversal, command-injection, prototype-pollution, prompt-injection, auth-bypass. d. Decide: promote, reject, or hypothesis-only.
3. Emit.

Promotion template

vuln = kg_add_node(
    "vulnerability",
    "SQLi in product search",
    props='{"key":"app.py:search_products:sqli","severity":"high",'
          '"file":"/workspace/target/app.py","line":142,"cwe":["CWE-89"],'
          '"source":"request.args.get(\\"q\\")","sink":"cursor.execute",'
          '"evidence":"cursor.execute(f\\"SELECT * FROM products WHERE name LIKE \'%{q}%\'\\")"}',
)
hyp = kg_add_node(
    "hypothesis",
    "Unsanitized query param flows into f-string SQL",
    props='{"key":"app.py:search_products:sqli:hyp"}',
)
kg_add_edge(vuln_id, candidate_id, "derived_from")
kg_add_edge(hyp_id, vuln_id, "mapped_to")

Rejection template

Same kg_add_node call with the SAME key, plus status="rejected" and reason="sanitized via html.escape before sink". Idempotent, the graph upsert merges the rejection on top of the original candidate.

Severity calibration