Add a [harnessx] config block so the agentic plane can be steered from
openultrasast.toml, and make the LLM provider selectable (the hunter and verifier
previously hardcoded AnthropicProvider).
- config.HarnessxConfig: provider ("anthropic"|"openai"|"litellm", default
anthropic), max_cost_usd (2.0), token_threshold (120000); parsed from [harnessx].
- harness_ext.build_provider: single lazy, capability-guarded provider factory.
The provider name is validated before any import, so an unknown choice fails with
a clear ValueError even when the extra is absent; each provider reads its own
standard key env var (openai -> OPENAI_API_KEY, anthropic -> ANTHROPIC_API_KEY).
- HxScanOrchestrator and verify_findings_dispatch take a provider and route through
build_provider; cli threads config.harnessx.{provider,max_cost_usd,token_threshold}
into both the hunter and the verifier.
- README: "Configuring the HarnessX agentic plane" section (install, models,
provider, budgets, fallback/degradation).
Test isolation: convert the "harnessx not in sys.modules" import-guards to a
hermetic subprocess fixture (tests/conftest.py assert_cold_of_harnessx) and
simulate extra-absent in the CLI dispatch tests, so the suite is deterministic
whether or not the optional extra is installed (it now is, locally). Verified the
OpenAI path constructs end-to-end (build_provider -> OpenAIProvider; orchestrator
-> Harness) with the budget threaded through.
137 passed, 3 skipped; ruff + mypy clean; detection gate PASS.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Externalize detection rules to versioned TOML and remove rule-local severity, so
rules detect and the central CWE policy decides severity — fixing "rules too
influential / not centrally managed". Zero-dependency; detection is byte-identical
(benchmark recall 44/47 = 93.6%, FP 0% unchanged) and the 90/10 gate holds.
- RulesetStore (ruleset/store.py + ruleset/<lang>/rules.toml): PatternRule as data
(rule_id, title, languages, cwe, tags, pattern, status, min_evidence_level,
precision_estimate, version) with NO severity; load_ruleset (sorted, with
optional rule_policy.json ledger overlay) and a stdlib TOML write_ruleset. The
32 rules were generated from the prior PATTERN_RULES with a verified exact
round-trip (pattern/cwe/languages/tags/title).
- findings.py: drop the hardcoded PatternRule class + PATTERN_RULES tuple; load the
default ruleset from TOML; quick_scan_findings(root, targets, rankings, ruleset,
policy) resolves each finding's severity from policy via resolve_severity
(SEVERITY_LABEL maps 0-5 -> string) and tags findings with their rule status.
- Three-status volume control: StaticFinding gains status (default "enabled");
disabled rules never fire; shadow findings are partitioned out of the report and
score in the CLI (written to shadow_findings.json) — no shadow rules by default.
- Emission floors (RulesetConfig.min_emit_priority / min_emit_precision, default
0.0 = emit all) replace "every match is a finding".
- CLI loads the ruleset as a stage and uses it for the fail-loud policy check,
detection, hunter pool, and the score CWE map. Wheel ships the ruleset TOML.
85 tests pass; ruff + mypy clean. Spec tasks 3.1-3.4 checked off.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
