extracting-requirement…
Use when starting an iterative-development run on human spec collateral — reads the spec, produces per-epic requirement files with proof obligations and…
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.
$ npx -y skills add prime-radiant-inc/iterative-development --skill auditing-progress --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/auditing-progressContext 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.
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.
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?"
Invoked by `iterative-development` after every `running-an-iteration` call, before picking the next iteration.
Read the per-epic requirement files in `docs/superpowers/iterations/requirements/`, `docs/superpowers/iterations/behavior-scenarios.md`, and `docs/superpowers/iterations/behavior-corpus.md`:
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:
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
Following PAR aggregation rules:
Return the audit result (clean or gaps) to the orchestrator. The orchestrator decides whether to loop or terminate.
| 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) |
A Claude Code plugin that drives an autonomous, audited implementation loop for projects with large, comprehensive, or ambiguous specs. Pairs with superpowers.
Use when starting an iterative-development run on human spec collateral — reads the spec, produces per-epic requirement files with proof obligations and…
Use when executing a batch of TDD-sized tasks inside a running-an-iteration call — dispatches an implementer subagent per task following red-green-refactor…
Use when implementing a project with a large, comprehensive, or ambiguous spec — extracts requirements with proof obligations, defines a walking skeleton with…
Use when executing the next pending iteration from an iterative-development roadmap — picks the iteration, decomposes into code and evidence tasks, runs…
Use when turning extracted requirements into a roadmap — selects the walking skeleton iteration with its first journey scenario, orders remaining work into…