auditing-progress

name	auditing-progress
description	Use when an iteration has just finished and you need to verify behavior evidence quality in three tiers — deep evidence for current stories, impacted behavior for touched scenarios, and sentinel corpus for high-value regression detection.

Auditing Progress

Overview

Runs after every iteration as part of the planning cycle. Verifies behavior evidence quality in three tiers using parallel adversarial review (PAR) — two paired auditor subagents evaluate the same work in parallel with competitive framing.

The audit answers: "Does durable, reusable evidence exist at the correct seam for every externally observable behavior this iteration touched?"

When to Use

Invoked by iterative-development after every running-an-iteration call, before picking the next iteration.

Audit Process

1. Partition the audit into three tiers

Read the per-epic requirement files in docs/superpowers/iterations/requirements/, docs/superpowers/iterations/behavior-scenarios.md, and docs/superpowers/iterations/behavior-corpus.md:

• Tier 1 — Deep evidence: stories marked done:ITER-<current> and scenarios added or updated in this iteration. Audit every AC and its proof obligation thoroughly.
• Tier 2 — Impacted behavior: all existing scenarios whose owning stories had code changes in this iteration (even if those stories were completed in earlier iterations). Verify the scenarios still pass.
• Tier 3 — Sentinel corpus: all scenarios with run cadence sentinel in the behavior corpus. Compare against the pre-iteration baseline from running-an-iteration step 3.

2. Dispatch paired auditor subagents (PAR)

Following the PAR methodology in skills/shared/parallel-adversarial-review.md:

1. Build the auditor prompt using auditor-subagent-prompt.md. Include ALL THREE tiers:
   • Tier 1: full story cards with proof obligations + new/changed scenario cards
   • Tier 2: impacted scenario cards + their current test results
   • Tier 3: sentinel scenario IDs + baseline results + current results
2. Wrap in competitive framing from skills/shared/par-reviewer-wrapper.md
3. Dispatch TWO auditor subagents in parallel
4. Wait for both to return

3. Aggregate findings

Following PAR aggregation rules:

• Same finding from both auditors → one finding, high confidence
• Finding from only one auditor → separate finding, still actionable
• Severity disagreement → take the more severe assessment, always fix it

4. Process results

• If gaps found (any AC fails, evidence is too weak, sentinel regression detected):
  • For AC failures: append gap stories to requirements/ (status pending) or flip existing stories back from done to pending
  • For weak evidence: create evidence-improvement stories (add scenario, strengthen seam)
  • For sentinel regressions: create regression-fix stories with CRITICAL priority
  • Revise roadmap.md to add a follow-up iteration for the gaps
• If clean (all tiers pass, evidence is adequate):
  • The iteration is confirmed done
  • Return clean signal to the orchestrator

5. Return control

Return the audit result (clean or gaps) to the orchestrator. The orchestrator decides whether to loop or terminate.

Quick Reference

Tier	What it checks	Failure means
Deep evidence	Every AC + proof obligation for current iteration	Story not done, evidence too weak
Impacted behavior	Scenarios whose surfaces were touched	Stale or broken scenario
Sentinel corpus	High-value journey scenarios	Regression in previously-working behavior

Reads	Writes	Dispatches
requirements/, behavior-scenarios.md, behavior-corpus.md, product code/tests	requirements/ (gaps), roadmap.md (new iteration) if gaps, behavior-scenarios.md (stale flags)	Two auditor subagents in parallel (PAR)

References

• skills/shared/parallel-adversarial-review.md — PAR methodology
• skills/shared/par-reviewer-wrapper.md — competitive framing wrapper
• skills/shared/behavior-evidence-formats.md — scenario and proof obligation formats
• auditor-subagent-prompt.md — auditor-specific prompt template