Agent skill

Iterative Development Orchestrator

by prime-radiant-inc in prime-radiant-inc/iterative-development

Runs an autonomous loop that extracts requirements with proof obligations, builds a walking skeleton, then audits sprint by sprint against real behavior evidence.

Apache-2.0Auto-check passedAgent Workflows

Install Iterative Development Orchestrator

skills CLI
$ npx skills add prime-radiant-inc/iterative-development --skill iterative-development -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install prime-radiant-inc/iterative-development iterative-development --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/prime-radiant-inc/iterative-development.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/iterative-development .claude/skills/iterative-development && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
iterative-development
GitHub stars
181
Token cost
~2.7k tokens
SKILL.md length
1,139 words
Files
1
Skills in repo
6
Repo updated
First seen
Licence
Apache-2.0

At a glance

Runs an autonomous loop that extracts requirements with proof obligations, builds a walking skeleton, then audits sprint by sprint against real behavior evidence.

  • Works in 3 steps: Check docs/superpowers/iterations/ for… → Invoke extracting-requirements on the… → Invoke scoping-the-simplest-core on the…
  • Starting work from a large, multi-file specification with many requirements
  • SKILL.md covers Overview, When to Use, The Autonomous Loop and Human Interrupt Protocol, plus 6 more sections
  • Calls git

What it does

This skill drives a full development lifecycle for specs that are too large or ambiguous for a single upfront plan. It first extracts requirements with proof obligations and behavior scenarios from the spec, chunking it, classifying pieces by type, and dispatching parallel extraction subagents whose output is aggregated into per-epic files and a shared behavior corpus.

It then scopes the simplest core: a walking-skeleton iteration that must close at least one real journey scenario, followed by an ordered backlog of iterations, checked by a citation review and parallel adversarial review. The main loop that follows runs audited sprints that keep building passing evidence, so completion is defined by evidence that a requirement works, not by a story being marked done.

It is meant for specs with 10 or more files or 100 or more requirements, where a single upfront plan tends to lose the plot, and explicitly says to use a simpler plan-then-implement flow for small, bounded projects instead.

When your agent uses it

  • Starting work from a large, multi-file specification with many requirements
  • Needing the product to stay in a working, testable state at every step
  • Replacing a single upfront plan with an audited, evidence-driven loop

Example prompts

  • “Implement the onboarding spec in docs/specs/onboarding using the iterative development loop.”
  • “This 40-page spec is too big for one plan, so run the walking-skeleton approach first.”
  • “Resume the iterative development loop from where it left off in docs/superpowers/iterations.”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Check docs/superpowers/iterations/ for existing state. If found, skip to Resume below.
  2. Invoke extracting-requirements on the human-provided spec path.
  3. Invoke scoping-the-simplest-core on the resulting backlog.

What it can do on your machine

Read from SKILL.md and the folder at commit c05889a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Iterative Development Orchestrator loads about 2.7k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 1,139 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from prime-radiant-inc/iterative-development at commit c05889a, republished under its Apache-2.0 licence (© prime-radiant-inc). 1,139 words, ~2,698 tokens.

Download SKILL.mdSave it as .claude/skills/iterative-development/SKILL.md (or your agent's skills folder).
name
iterative-development
description
Use when implementing a project with a large, comprehensive, or ambiguous spec — extracts requirements with proof obligations, defines a walking skeleton with its first journey scenario, then loops through audited sprints that continuously build a behavior evidence corpus. Completion means passing evidence, not just finished stories.

Iterative Development

Overview

Orchestrator for the iterative-development plugin. Drives the full autonomous lifecycle: extract requirements with proof obligations and behavior scenarios from human spec collateral, define a walking skeleton that passes its first journey scenario, then loop through audited sprints that continuously build a reusable behavior evidence corpus. Completion means the product has passing behavior evidence at the correct seam for every externally observable requirement — not just that stories are marked done. Every evaluative gate uses parallel adversarial review (PAR).

This is an alternative to superpowers:writing-plans → superpowers:subagent-driven-development for projects where the upfront-planning approach would lose the plot.

When to Use

  • Spec is large, comprehensive, or ambiguous (10+ files, 100+ requirements)
  • You need the product to be in a working, testable state at every iteration boundary
  • You want an autonomous audited loop rather than a single upfront plan
  • The writing-plans flow has lost the plot on this project before

Do NOT use for small, bounded projects — superpowers:writing-plans → superpowers:subagent-driven-development is simpler and more appropriate.

The Autonomous Loop

Bootstrap (first invocation)
  1. Check docs/superpowers/iterations/ for existing state. If found, skip to Resume below.
  2. Invoke extracting-requirements on the human-provided spec path.
    • Chunks the spec, classifies by taxonomy (journeys → E2E, domains → integration, etc.)
    • Dispatches parallel extraction subagents that produce stories with proof obligations AND behavior scenarios
    • Aggregates stories into per-epic files, scenarios into behavior-scenarios.md
    • Builds coverage ledger with both story AND scenario coverage
    • Produces docs/superpowers/iterations/requirements/, docs/superpowers/iterations/behavior-scenarios.md, docs/superpowers/iterations/behavior-corpus.md
  3. Invoke scoping-the-simplest-core on the resulting backlog.
    • Defines the walking skeleton iteration (ITER-0000) + ordered follow-on iterations
    • Runs citation check + PAR scope review
    • Produces docs/superpowers/iterations/roadmap.md
    • Walking skeleton must close at least one journey scenario (not just compile)
    • Applies story splitting when stories have heterogeneous-dependency ACs
Main loop
while True:
    check_for_human_interrupt()

    if not roadmap has pending iterations:
        if last audit was clean:
            run final behavior-evidence audit (see below)
            if behavior audit clean:
                break  # done
            # else: audit found uncovered surfaces or weak evidence, new iterations added
        # else: audit found gaps, new iterations were added, continue

    run next iteration:
        - running-an-iteration (sentinel baseline → scope review → decompose code + evidence tasks → implementing-tasks → impacted + sentinel scenario runs → wrap up)
    
    audit:
        - auditing-progress (PAR paired auditors, three-tier: deep evidence + impacted behavior + sentinel corpus)
        - if gaps: append to backlog, revise roadmap, continue
        - if clean: mark last_audit_clean, continue
Final behavior-evidence audit

Before declaring the project complete, verify that the product has adequate behavior evidence — not just that all stories are marked done:

  1. List every major user-facing surface from the original spec (settings panes, UI flows, CLI commands, journeys, etc.)
  2. For each surface, verify that:
    • Corresponding stories exist AND are implemented
    • Corresponding scenarios exist AND have passing evidence at the correct seam
    • Journey scenarios that cross multiple surfaces are passing E2E
  3. Check the behavior corpus index for completeness:
    • Every journey spec file has at least one JOURNEY-NNNN scenario
    • Every scenario has a non-TBD execution command
    • All sentinel scenarios pass
  4. Flag any surface with:
    • No corresponding story (extraction under-scoped)
    • No corresponding scenario (evidence gap)
    • Evidence at a weaker seam than the requirement demands
    • Manual-residual scenarios that could be automated
  5. If gaps found: create new stories/scenarios/iterations, continue the loop

The final question is: "Can the system point to passing behavior evidence for every externally observable requirement the spec describes?" Not: "Are the stories done?"

Resume (re-invocation with existing state)

All process state lives in artifact files:

  • docs/superpowers/iterations/requirements/ (backlog with story status and proof obligations)
  • docs/superpowers/iterations/behavior-scenarios.md (scenario cards with stable IDs)
  • docs/superpowers/iterations/behavior-corpus.md (execution index)
  • docs/superpowers/iterations/roadmap.md (iteration plan with status)
  • docs/superpowers/iterations/iteration-log.md (completed iteration history)

On re-invocation: read roadmap.md, find the next pending iteration, and continue from there. There is no ephemeral in-memory state to recover. The command "continue iterative development with the existing plan" always works.

If the orchestrator crashed mid-iteration, the partially-completed iteration's git commits are preserved. On resume, the next un-started iteration picks up. If the in-progress iteration left the code in a broken state, treat it as a gap — the audit will catch it and add corrective work.

Human Interrupt Protocol

The loop runs without human intervention. The only way the human injects new information mid-run is by interrupting between iterations.

How it works:

  • The human types the update into the chat session ("we dropped feature X", "the spec changed, re-read specs/foo.md", "add a new requirement for Y")
  • The orchestrator notices the interrupt at the next iteration boundary — after the current iteration's audit completes, before the next iteration starts
  • At the boundary: invoke extracting-requirements in incremental mode on the changed spec files, merge new/revised story cards into the backlog, revise the roadmap if changes invalidate downstream iterations, then resume

Guarantees:

  • Changes during mid-iteration do NOT disrupt in-progress work. The current iteration completes first.
  • The orchestrator never silently drops an interrupt. If ambiguous, ask for clarification before resuming.
  • Existing story IDs are preserved across re-extraction. Removed stories flip to deferred, not deleted.

What does NOT trigger interrupt processing:

  • The orchestrator does not poll the filesystem for spec changes
  • The orchestrator does not ask "anything to change?" between iterations
  • Human presence is not required at iteration boundaries
Show full SKILL.md (400 more words)Show less

Progress Reporting

The autonomous loop may run for hours. Two progress mechanisms ensure visibility without requiring interruption:

1. Progress file: Write docs/superpowers/iterations/progress.md at each phase transition:

markdown
# Progress

**Phase:** implementing ITER-0003
**Task:** 4/7 (CleanupPipeline integration)
**Iterations:** 3/18 done, 15 pending
**Sentinel corpus:** 10/10 passing
**Last event:** 2026-04-11T14:23:00Z — Task 3 committed

Update this file at: iteration start, each task completion, iteration wrap-up, audit start/end. Overwrite (not append) — it's a snapshot of current state, not a log.

2. Git log: Every task produces a commit. The commit history is a detailed progress trail. A human can check git log --oneline for fine-grained status without interrupting the loop.

Skill Precedence

When running autonomously, this orchestrator takes precedence over interactive-gate skills (e.g., brainstorming which requires design approval before implementation). The iterative-development process has its own design gates (scope review, PAR) that replace interactive approval. Do not block on skills that assume a human is present to approve each step.

Escalation Policy

Catastrophe-only. The loop is autonomous. Human escalation is reserved for total failure — the plugin cannot make any forward progress at all.

These do NOT trigger escalation:

  • A reviewer finding issues (those become fix work)
  • An audit finding gaps (those become new iterations)
  • An implementer reporting BLOCKED on a task (try: more context, more capable model, smaller task)
  • Ambiguity in the spec (make a reasonable judgment call, document it in the iteration log)
  • Difficulty or slow progress (keep going)

The orchestrator does NOT prompt "should I continue?" between iterations.

Skill Invocation Reference

PhaseSkillWhat it does
Extractextracting-requirementsChunk → parallel extract → aggregate → requirements/
Scopescoping-the-simplest-coreWalking skeleton + iterations → roadmap.md (with PAR scope review)
Implementrunning-an-iterationScope review → decompose → implementing-tasks → wrap up
Task executionimplementing-tasksPer-task: implementer → PAR spec review → PAR quality review
Auditauditing-progressPAR paired auditors, two-tier (deep + sweep)

Artifact Location

All plugin artifacts live in docs/superpowers/iterations/. Never modify the human's spec collateral.

FilePurpose
requirements/Backlog: story cards + epics with stable IDs and proof obligations
behavior-scenarios.mdBehavior contracts: reusable scenario cards with stable IDs
behavior-corpus.mdExecution index: scenario → seam → cadence → command
roadmap.mdSprint plan: ordered iterations with impacted scenarios
iteration-log.mdSprint history: what each iteration delivered + scenarios added
progress.mdLive snapshot: current phase, task, iteration counts, sentinel status

Quality Gates

Every evaluative gate uses parallel adversarial review (PAR):

  • Pre-iteration scope review (citation + scope-creep + boxing-in + scenario coverage + story splitting)
  • Pre-iteration sentinel corpus baseline
  • Per-task spec-compliance review with evidence quality check
  • Per-task code-quality review with boxing-in + corpus contribution check
  • Post-iteration impacted + sentinel scenario runs
  • Per-sprint audit (deep evidence + impacted behavior + sentinel corpus)

See skills/shared/parallel-adversarial-review.md for PAR methodology.

© prime-radiant-inc, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/iterative-development of prime-radiant-inc/iterative-development.

Open the folder on GitHubat commit c05889a

Compare with similar skills

Iterative Development Orchestrator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Iterative Development Orchestrator compared with similar skills
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Iterative Development Orchestrator this skillprime-radiant-inc/iterative-development181—~2.7kAutomated safety check: PassApache-2.0
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Architect Before You Buildjsmastery-pro/jsm-agent-skill218—~1.1kAutomated safety check: PassMIT
Auditable Playbook Designercursor/plugins10k8 repos~1kAutomated safety check: PassNone
Fable Disciplineassafkip/kipi-system112—~2.9kAutomated safety check: PassMIT
Execute Implementation Planimbue-ai/bouncer400—~425Automated safety check: WarnAGPL-3.0

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More from prime-radiant-inc/iterative-development

  • Extracting Requirements

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    Reads human-written spec documents and produces per-epic requirement files with proof obligations plus behavior scenarios with stable IDs, using parallel chunked extraction.

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  • Walking Skeleton Roadmap Scoping

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    Turns extracted requirements into a roadmap by choosing a walking skeleton iteration with its first journey scenario and ordering the remaining work into follow-on iterations.

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  • Running An Iteration

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    A skill your agent uses when executing the next pending iteration from an iterative-development roadmap — picks the iteration, decomposes into code and evidence tasks, runs sentinel corpus baseline…

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  • Iteration Progress Audit

    prime-radiant-inc/iterative-development

    Checks the quality of behavior evidence after each iteration in three tiers, using two auditor subagents in parallel to review the same work and find gaps.

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Questions about Iterative Development Orchestrator

What does Iterative Development Orchestrator do?

Runs an autonomous loop that extracts requirements with proof obligations, builds a walking skeleton, then audits sprint by sprint against real behavior evidence. This skill drives a full development lifecycle for specs that are too large or ambiguous for a single upfront plan. It first extracts requirements with proof obligations and behavior scenarios from the spec, chunking it, classifying pieces by type, and dispatching parallel extraction subagents whose output is aggregated into per-epic files and a shared behavior corpus.

When should I use Iterative Development Orchestrator?

Iterative Development Orchestrator fits situations like: starting work from a large, multi-file specification with many requirements; needing the product to stay in a working, testable state at every step; replacing a single upfront plan with an audited, evidence-driven loop.

How do I install Iterative Development Orchestrator in Claude Code?

Run `npx skills add prime-radiant-inc/iterative-development --skill iterative-development -a claude-code`. Or copy the skill folder (skills/iterative-development in prime-radiant-inc/iterative-development) into .claude/skills/iterative-development in your project. Claude Code loads it when a task matches its description.

How do I install Iterative Development Orchestrator in Codex?

Run `npx skills add prime-radiant-inc/iterative-development --skill iterative-development -a codex`. Or copy the skill folder (skills/iterative-development in prime-radiant-inc/iterative-development) into .agents/skills/iterative-development in your project. Codex loads it when a task matches its description.

Can I use Iterative Development Orchestrator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add prime-radiant-inc/iterative-development --skill iterative-development -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iterative-development, .gemini/skills/iterative-development, .github/skills/iterative-development and .opencode/skills/iterative-development in your project.

What does Iterative Development Orchestrator need to run?

Going by SKILL.md and its folder, Iterative Development Orchestrator needs the command-line tools its instructions call (git).

Does Iterative Development Orchestrator access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Iterative Development Orchestrator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Iterative Development Orchestrator use?

Iterative Development Orchestrator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Iterative Development Orchestrator use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Iterative Development Orchestrator?

Skills that share tags, products or a category with Iterative Development Orchestrator: Spec-Driven Development (LichAmnesia/lich-skills, 234 stars), Architect Before You Build (jsmastery-pro/jsm-agent-skill, 218 stars), Auditable Playbook Designer (cursor/plugins, 10k stars) and Fable Discipline (assafkip/kipi-system, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Iterative Development Orchestrator?

prime-radiant-inc (a GitHub organization) maintains it in prime-radiant-inc/iterative-development, which has 181 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on June 6, 2026.

Source: prime-radiant-inc/iterative-development on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.