running-an-iteration

name	running-an-iteration
description	Use when executing the next pending iteration from an iterative-development roadmap — picks the iteration, decomposes into code and evidence tasks, runs sentinel corpus baseline, dispatches implementing-tasks, runs impacted + sentinel scenarios, and updates artifacts.

Running an Iteration

Overview

Drives one iteration: picks the next pending, runs sentinel corpus baseline, runs pre-iteration scope review via PAR, decomposes into code and evidence tasks, dispatches implementing-tasks, runs impacted + sentinel scenarios at wrap-up, and updates the roadmap and iteration log.

When to Use

Invoked by iterative-development inside the main loop. Each invocation runs exactly one iteration. After return, the orchestrator invokes auditing-progress.

Script Location

All scripts referenced below live in this skill's scripts/ directory, next to this SKILL.md file.

Iteration Process

1. Pick next iteration

Read docs/superpowers/iterations/roadmap.md, find the first iteration with status pending.

2. Load scope context

Read the per-epic files in docs/superpowers/iterations/requirements/ to load the full story cards for each committed story ID. Only read the epic files that contain stories for this iteration — not all of them. Also:

• Load the next 3 pending iterations from the roadmap for look-ahead
• Read docs/superpowers/iterations/behavior-scenarios.md to identify impacted scenarios
• Read docs/superpowers/iterations/behavior-corpus.md to identify sentinel scenarios

3. Run sentinel corpus baseline

Before any code changes, run every scenario in the behavior corpus with run cadence sentinel:

• If all sentinels pass: record baseline as clean, proceed
• If any sentinel fails: the failure predates this iteration. Record it, create a gap story for it, but proceed with the iteration (the gap will be addressed in a follow-up)

This establishes whether regressions exist before the current iteration starts.

4. Pre-iteration consistency audit

Before planning any work, verify that artifact state is consistent:

1. Citation check: python3 "scripts/check_citations.py" docs/superpowers/iterations/roadmap.md docs/superpowers/iterations/requirements/ — if citations fail, stop and fix the roadmap.
2. Status reconciliation: For each story in this iteration's scope, verify:
   • Stories listed in the roadmap iteration are not already marked done:ITER-XXXX in the requirements index (unless code/tests actually exist for them)
   • Stories marked done in the requirements index actually have corresponding code and tests
   • No story appears in multiple pending iterations
3. Epic counter validation: Spot-check that epic progress counters match the actual count of done stories.

If any inconsistencies are found, reconcile before proceeding. Do not trust any single artifact blindly — cross-check.

5. Pre-iteration scope review (PAR)

Following skills/shared/parallel-adversarial-review.md:

1. Build the scope reviewer prompt using scope-reviewer-prompt.md
2. Wrap in PAR competitive framing from skills/shared/par-reviewer-wrapper.md
3. Dispatch TWO scope reviewers in parallel
4. Aggregate findings: same issue from both = high confidence, unique = still actionable, severity disagreement = take worst
5. If REVISE recommended: adjust iteration scope and re-review. Loop until APPROVE.

6. Decompose into code tasks AND evidence tasks

Break the iteration scope into TDD-sized tasks. Each task = failing test → implementation → passing test → commit.

Evidence tasks: In addition to code tasks, identify:

• Which existing scenarios are impacted by this iteration's changes
• Which new scenarios must be added (from the story proof obligations)
• Which scenario harnesses need to be extended
• Which behavior corpus entries need updated execution commands

Evidence tasks are first-class — they produce scenario updates, test harness extensions, and corpus index entries. They are NOT afterthoughts. Interleave evidence tasks with code tasks: after implementing a feature, the next task should be extending or adding the scenario that proves it.

Cross-iteration dependencies: Some stories reference subsystems that don't exist yet. For these, implement the thinnest abstraction boundary that satisfies the story's ACs without coupling to the future implementation. Prefer a single clean interface over a decomposed hierarchy — the real implementation will define its own internal structure when it arrives. Document the dependency with a TODO comment citing the future iteration. Do NOT defer the story silently or force premature integration.

7. Dispatch implementing-tasks

Pass the task list (code + evidence tasks) and iteration context to implementing-tasks. Wait for completion.

8. Post-iteration scenario runs

After all tasks complete, run:

1. Impacted scenarios: every scenario in the behavior corpus whose owning stories were touched by this iteration
2. Sentinel scenarios: every scenario with run cadence sentinel

If any impacted or sentinel scenario fails that passed at baseline (step 3), this iteration introduced a regression. Create a fix task and re-dispatch to implementing-tasks.

9. Resolve cross-iteration TODOs

Grep the codebase for TODO(ITER-<current>) markers — these are interface stubs that earlier iterations created expecting THIS iteration to provide the real implementation.

For each marker found:

1. Verify the real implementation now exists (not still a stub/NoOp)
2. If resolved: remove the TODO comment
3. If NOT resolved: the iteration is incomplete — add a fix task and re-dispatch

This step is a hard gate. An iteration that leaves its own TODO markers in the code is not done.

10. Wrap up

• Verify all iteration stories' ACs pass (sanity check before audit)
• Verify all proof obligations for observable ACs have corresponding scenario evidence
• Verify no TODO(ITER-<current>) markers remain in the codebase (step 9)
• Mark stories done:ITER-NNNN in the relevant epic files under requirements/
• Update scenario automation status and execution commands in behavior-scenarios.md
• Update the behavior corpus index in behavior-corpus.md
• Update iteration status in roadmap.md to done
• Append entry to docs/superpowers/iterations/iteration-log.md — include:
  • Stories delivered
  • Scenarios added or updated
  • Sentinel corpus results
• Validate: python3 "scripts/validate_iteration_log.py" docs/superpowers/iterations/iteration-log.md
• Return control to orchestrator (do NOT invoke auditing-progress — that's the orchestrator's job)

Quick Reference

Step	Tool/Skill	Purpose
Sentinel baseline	Run sentinel scenarios	Establish pre-iteration regression state
Citation check	scripts/check_citations.py	Mechanical: cited stories exist
Scope review	PAR + scope-reviewer-prompt.md	Semantic: scope, scenarios, splitting, boxing-in
Task execution	implementing-tasks	TDD code + evidence implementation
Post-iteration runs	Run impacted + sentinel scenarios	Catch regressions
TODO resolution	grep -rn 'TODO(ITER-<current>)'	Cross-iteration stubs resolved
Wrap up	scripts/validate_iteration_log.py	Artifact validation

References

• skills/shared/parallel-adversarial-review.md — PAR methodology
• skills/shared/behavior-evidence-formats.md — scenario and proof obligation formats
• scope-reviewer-prompt.md — scope reviewer prompt template
• scripts/check_citations.py — mechanical citation check