feat(improve): Phase 3 task 7 — bounded, gated self-improvement loop

Stand up the deterministic self-improvement loop over the ruleset/policy
governance data. Zero-dependency and fully tested offline; detection gate intact.

- 7.1/7.2 bounded levers + validator (improve/validator.py): two levers (rule,
  policy); typed edits make pattern-text and 0-5 severity changes structurally
  impossible; EvolveValidator rejects bad status, unknown rule_ids (loop cannot add
  rules), enabled->disabled jumps (shadow staging mandatory), unresolved CWEs, and
  out-of-bounds K/MIN_SCORE, raising StrictValidationError.
- 7.3 novelty + replay + journal (improve/journal.py): a JSON round journal records
  every edit/lever/outcome; reverted edits are blocked from re-proposal (novelty);
  a replay smoke gate boots the candidate ruleset before re-benchmarking.
- 7.4 benchmark deltas -> rule_signals.json (recall side: miss; precision side: fp).
- 7.5 hard acceptance gate + byte-for-byte revert (improve/evolve.py): a round is
  accepted only if recall >= 90% AND FP < 10% AND project score did not regress AND
  no matched-finding regression; otherwise the ledger is never written (byte-for-byte
  revert). run_round/run_improvement drive rounds to convergence.
- 7.6 auto-shadow precision-draggers (enabled rules with FPs -> shadow, never
  delete), nominate coverage gaps via rule_signals, and keep the project score as the
  reward separate from the recall/FP feasibility gate (the loop cannot raise the
  score by shadowing rules that breach the gate — proven by the revert test).

Design note: the loop's safety machinery is deterministic and benchmark-driven
(the proposer is the per-rule signal); the LLM MetaAgent.evolve is the optional
richer proposer that plugs into this same validated/gated machinery when the
HarnessX extra is present (offline-untestable). This mirrors how DocuHarnessX
realizes self-improvement.

9 new tests (validator, proposer/signals, accept, byte-for-byte revert, convergence);
118 pass total, 2 construction smokes skip without the extra; ruff + mypy clean;
detection gate 93.6%/0% intact. Tasks 7.1-7.6 checked off.

Co-Authored-By: Claude Opus 4.8 <[email protected]>

.kiro/specs/harnessx-self-improving-rulesets/spec.json

@@ -1,7 +1,7 @@
 {
   "feature_name": "harnessx-self-improving-rulesets",
   "created_at": "2026-06-22T10:07:18Z",
-  "updated_at": "2026-06-22T15:05:00Z",
+  "updated_at": "2026-06-22T15:35:00Z",
   "language": "en",
   "phase": "implementation",
   "approvals": {

.kiro/specs/harnessx-self-improving-rulesets/tasks.md

@@ -177,15 +177,15 @@ This plan splits the fused regex layer into three governed owners (rules detect,
   - _Boundary: HxScanOrchestrator_
   - _Depends: 6.1, 6.4_
 
-- [ ] 7. Stand up the bounded self-improvement loop
-- [ ] 7.1 Add the two bounded edit levers
+- [x] 7. Stand up the bounded self-improvement loop
+- [x] 7.1 Add the two bounded edit levers
   - Expose exactly two new edit levers: a rule lever covering rule status, evidence floor, and threshold, and a policy lever covering evidence floor, scope, and the score constants.
   - Observable completion: the loop's valid-lever set includes exactly the rule and policy levers and rejects any edit outside their declared fields, verified by a lever test.
   - _Requirements: 3.1_
   - _Boundary: EvolveValidator, improve/evolve.py_
   - _Depends: 5.2_
 
-- [ ] 7.2 Enforce the rule/policy edit safety bounds in the validator
+- [x] 7.2 Enforce the rule/policy edit safety bounds in the validator
   - Reject any loop edit that modifies detection pattern text and any edit that modifies the authoritative 0-5 CWE severity.
   - Reject moving a rule directly from enabled to disabled, requiring shadow as a mandatory intermediate staging state.
   - Assert that each edit parses, that status is one of the three literals, that threshold/evidence-floor changes stay within configured bounds, that an added rule does not duplicate an existing identifier, and that every referenced CWE resolves in policy; raise a rule-change validation error and reject the whole round on any failure.
@@ -194,7 +194,7 @@ This plan splits the fused regex layer into three governed owners (rules detect,
   - _Boundary: EvolveValidator_
   - _Depends: 7.1, 1.1_
 
-- [ ] 7.3 Add the novelty, replay, and journal gates
+- [x] 7.3 Add the novelty, replay, and journal gates
   - Block re-proposing a previously reverted edit that lacks a retry rationale via the novelty gate.
   - Run a replay smoke gate before any benchmark re-run proving the edited ruleset and policy still load and boot.
   - Record each rule and policy edit, its lever, and its outcome in the journal so every change is attributable and reversible.
@@ -203,14 +203,14 @@ This plan splits the fused regex layer into three governed owners (rules detect,
   - _Boundary: EvolveValidator, improve/evolve.py_
   - _Depends: 7.2_
 
-- [ ] 7.4 Convert benchmark deltas into loop signals
+- [x] 7.4 Convert benchmark deltas into loop signals
   - Convert the per-rule recommendation delta and false-positive learnings into a loop signal file, writing recall-side items as miss signals and precision-side items as verifier-reject or false-positive signals.
   - Observable completion: a benchmark run produces a signal file whose recall-side entries are miss signals and precision-side entries are verifier-reject/false-positive signals, verified against the documented signal schema.
   - _Requirements: 4.3_
   - _Boundary: improve/evolve.py_
   - _Depends: 4.2, 7.1_
 
-- [ ] 7.5 Implement the hard acceptance gate with byte-for-byte revert
+- [x] 7.5 Implement the hard acceptance gate with byte-for-byte revert
   - After the meta-agent edits rules or policy, re-run the benchmark and accept the round only if aggregate recall is at least 90% AND false-positive rate is under 10% AND the project score did not regress on the held set AND no unpredicted regression occurred.
   - On any failed acceptance condition, revert the round and restore the rule and policy configuration byte-for-byte.
   - Observable completion: a round that regresses recall is reverted and the configuration is restored byte-for-byte, while a compliant round is accepted and persisted, proven by a self-improvement integration test.
@@ -218,7 +218,7 @@ This plan splits the fused regex layer into three governed owners (rules detect,
   - _Boundary: improve/evolve.py_
   - _Depends: 7.3, 7.4, 4.1_
 
-- [ ] 7.6 Auto-shadow precision-draggers, nominate coverage gaps, and separate the objective
+- [x] 7.6 Auto-shadow precision-draggers, nominate coverage gaps, and separate the objective
   - Auto-shadow a rule that drags aggregate precision below the gate rather than delete it, preserving recall while removing the false-positive cost.
   - Nominate a new or loosened rule through the signal file when a coverage gap surfaces as a persistent miss, leaving pattern authoring to a human pull request.
   - Treat maximizing the project score as the optimization objective and the recall/FP gate as a separate hard feasibility constraint that is never folded into the score reward.