vigil-intervene

name	vigil-intervene
description	Phase I (Intervene) — falsifies hypotheses via counterfactual replay. Designs minimal interventions for each root candidate; runs in sandbox; root cause is the candidate whose intervention flips failure→success. Hard cap 5 interventions. Use after `idg.md` identifies ≥1 ROOT_CANDIDATE, before emitting the root-cause report.
allowed-tools	apply_patch_sandboxed, revert_patch, run_test, inject_oracle, statistical_replay
metadata	{"methodology":"VIGIL","phase":"I-intervene"}

SKILL: Intervene — falsify hypotheses via counterfactual replay

When to use

Load when idg.md schema-valid with ≥1 ROOT_CANDIDATE. Unload when one hypothesis survives (→ Learn) or budget exhausted (→ escalate).

---

Contract

Field	Value
LLM calls permitted	For designing minimal interventions and interpreting results
Tool budget	≤30% of mission budget (hot phase)
Intervention cap	5 hard maximum. No exceptions.
Output	intervention-log.md — schema-valid per schemas/intervention-log.v1.json
Authority enforcement	Interventions run only in declared authority scope

---

The Counterfactual Principle

A candidate is the root cause if and only if replacing its output with a correct value causes the failure to disappear. This is the strongest causal proof available short of formal verification.

Research basis:

• AgenTracer (arXiv:2509.03312) — counterfactual replay as the minimum-cost causal proof; standalone LLM attribution accuracy is sub-10% without it
• Lifecycle of Failures (arXiv:2509.23735) — attribution accuracy 46.3% → 65.8% with counterfactual replay vs log-only
• Delta Debugging (Zeller, classical) — minimal interventions are the ground-truth technique for causality in deterministic systems

---

Hypothesis Generation

Before any intervention runs, generate ≥3 competing hypotheses. The harness refuses to apply interventions with <3 active hypotheses.

For each ROOT_CANDIDATE from idg.md, generate at least one hypothesis. If you have <3 root candidates, generate multiple hypotheses per candidate (different mechanisms by which that candidate could cause the observed failure).

hypotheses:
  - id: H-001
    candidate: N-003
    mechanism: |
      The UUID generation in RecordVote#build_ballot_token returns nil
      when the user's session lacks a secret key, producing the observed
      ballot.token = nil at assertion time.
    falsification_criterion: |
      If H-001 is true, supplying a valid UUID at N-003's output boundary
      will flip the failure to success.
    confidence_prior: medium
  - id: H-002
    candidate: N-004
    mechanism: |
      Session lookup at N-004 returns an incomplete session object missing
      the secret_key attribute; downstream UUID generation produces nil.
    falsification_criterion: |
      If H-002 is true, injecting a complete session object at N-004's
      output will flip the failure.
    confidence_prior: medium
  - id: H-003
    candidate: N-003
    mechanism: |
      The UUID library is misconfigured — not user session related; a
      recent dep upgrade changed the generation contract.
    falsification_criterion: |
      If H-003 is true, pinning the UUID library to the previous version
      will flip the failure.
    confidence_prior: low

Plurality rule: The hypotheses must be mechanistically distinct. "The UUID is nil" and "The token field is nil" are not distinct — they're different descriptions of the same observation. Distinct hypotheses predict different interventions.

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Intervention Design

For each hypothesis, design the smallest possible change that would falsify it. Each intervention fits one of four types:

Type 1 — Code-change intervention

Apply a minimal diff at the candidate's location. The diff should address only the proposed mechanism, not improve surrounding code.

intervention_type: code_change
target: app/flows/record_vote.rb:56
diff: |
  -    token = generate_uuid(session)
  +    token = generate_uuid(session) || SecureRandom.uuid   # temporary oracle
scope: 1 file, 1 line

Type 2 — Oracle injection

Replace a node's runtime output with a known-correct value, without changing code. Use this when the hypothesis concerns upstream correctness.

intervention_type: oracle_injection
target: N-004 (session lookup return)
oracle_value: <complete session object with secret_key populated>
mechanism: "Test helper monkeypatches SessionStore.find for this run"

Type 3 — Input/state correction

Supply corrected input or initialize corrected state before running the failing invocation.

intervention_type: state_correction
target: "before-test state: ballot.token column"
correction: "Pre-populate ballot.token with valid UUID via DB fixture"

Type 4 — Timing/concurrency fix

For HEISENBUG category: force ordering, add sync primitives, or freeze timing.

intervention_type: timing_fix
target: "ballot save + token generation"
correction: "Wrap in explicit transaction with serializable isolation"

Minimum-intervention rule. Across all types:

• Interventions affecting >3 files require explicit justification; harness flags for review
• Interventions that improve unrelated code fail the scope check
• Each intervention must be hypothesis-specific — you cannot apply one intervention and claim it falsifies two hypotheses, unless the hypotheses were mechanistically identical (in which case they shouldn't have been separate hypotheses)

---

Running Interventions

Deterministic mode

1. Apply intervention via sandbox adapter
2. Run reproduction command
3. Record result
4. Revert intervention — sandbox must return to clean state

Each intervention = 1 run. Result categories:

• FLIPPED — failure → success (or failure → different success); hypothesis survives
• NO_CHANGE — failure reproduces identically; hypothesis falsified
• NEW_FAILURE — different failure emerges; hypothesis falsified but reveals compound issue (log for Learn)
• ERROR — intervention itself failed to apply; does not count against budget, retry with fix

Statistical mode

Inherited from Phase V if DETERMINISM_VERDICT = flaky. Each intervention runs 5× with the same setup.

statistical_run:
  intervention_id: I-001
  runs: 5
  outcomes: [PASS, PASS, PASS, FAIL_MATCH, PASS]
  flip_ratio: 4/5
  verdict: FLIPPED   # ≥4/5 required

Verdict: FLIPPED only if ≥4 of 5 runs pass. 3/5 is not enough — the noise floor in failing-baseline runs must be distinguishable from the signal in intervention runs.

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Authority-Gated Execution

Authority	Intervention Behavior
read-only	Interventions are described but not executed. Output marks each as simulated. Downstream (APIVR-Δ or human) must validate. No [ROOT-CAUSE] emission possible — highest confidence is [HYPOTHESIS-N] with high-priority flag.
sandbox	Interventions execute in the pluggable sandbox adapter only. Working tree untouched. Counterfactual flips are real; [ROOT-CAUSE] emission permitted.
write	Sandbox-first always. If sandbox run flips, the surviving intervention may be emitted as verified-patch.diff for application to working branch. Application itself is still a downstream step — VIGIL emits the patch; it does not auto-apply to the working tree unless explicitly requested.

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Budget Exhaustion — Escalation Protocol

If 5 interventions run without a FLIPPED result:

1. Stop immediately. Do not design a 6th intervention. Do not "one more try."
2. Emit escalation brief — templates/escalation-brief.md — including:
   • All 5 interventions with results
   • The current hypothesis log
   • The full IDG
   • What evidence would resolve the ambiguity
3. Route to FORGE (reasoner) by default — ambiguity in root cause is FORGE's domain. Alternative routing: human if the failure class is safety-critical.

Rationale. Unbounded self-correction degrades output quality (CorrectBench 2025). Five interventions, each minimal and each testing a distinct hypothesis, is enough to resolve most attributable failures. Missions that exceed this budget are genuinely ambiguous and need either broader reasoning (FORGE) or more evidence (re-scope and re-run VIGIL).

---

Writing intervention-log.md

mission_id: VIGIL-YYYYMMDD-NNN
upstream_idg: <ref>
hypotheses: [<as above>]
interventions:
  - id: I-001
    hypothesis_id: H-001
    intervention_type: oracle_injection
    target: "N-003 output"
    description: "Inject valid UUID directly"
    authority_used: sandbox
    runs:
      - mode: deterministic | statistical
        outcome: FLIPPED | NO_CHANGE | NEW_FAILURE | ERROR
        evidence: "<log excerpt ref>"
    verdict: FLIPPED
  - id: I-002
    ...
survivor:
  hypothesis_id: H-001
  intervention_id: I-001
  confidence: H                # H requires deterministic flip or ≥4/5 statistical; M one tier down
  verified_patch: <null | diff block>   # populated if authority ≥ sandbox
multi_flip_handling: null      # populated if >1 intervention flipped; records tie-break
compound_findings: []          # NEW_FAILURE results that revealed additional issues
budget:
  interventions_used: 2
  interventions_cap: 5
  exhausted: false

---

Pitfalls

• Confirmation-optimal interventions. Designing an intervention that must succeed regardless of hypothesis truth. Guard: each intervention should have a plausible NO_CHANGE outcome; if not, re-design.
• "Just one more hypothesis" after 5 interventions. No. Escalate.
• Applying an intervention that accidentally fixes a different issue. If FLIPPED but the intervention scope exceeds the hypothesis, the flip is suspect — mark [DISPUTED] and run a narrower intervention.
• Skipping revert between interventions. Sandbox contamination destroys causality. Always revert to clean state.
• Accepting 3/5 flip in statistical mode. Not enough signal. Raise to 4/5 or decline the attribution.

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ECL Escalation-Brief Envelope

On budget exhaustion (5 interventions, no FLIPPED result), the escalation-brief payload is wrapped per templates/escalation-brief.envelope.json with performative: "ESCALATE" and to.eidolon: "forge". The assumptions[] array MUST include "trigger: budget-exhausted-no-flip" (ECL §2.2.3). The edge_origin is "roster" — the vigil → forge lateral edge is declared in roster/index.yaml.

See skills/learn.md § Envelope Emission for the full envelope construction procedure (SHA-256 computation, trace-event append, thread_id resolution).

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VIGIL Phase I (Intervene) — 5 counterfactuals, minimal each, falsification-optimal