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/auditing-progress

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.

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iterative-development
1796 skills
Install
$ npx -y skills add prime-radiant-inc/iterative-development --skill auditing-progress --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/auditing-progress

Context preview

The summary Claude sees to decide when to auto-load this skill.

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.

SKILL.md

auditing-progress.SKILL.md
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
Read more
Ships withiterative-development

A Claude Code plugin that drives an autonomous, audited implementation loop for projects with large, comprehensive, or ambiguous specs. Pairs with superpowers.

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Python
Language
Apache-2.0
License
3mo ago
Last commit
5mo ago
Created

Repo: prime-radiant-inc/iterative-development

Other skills on iterative-development.