Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add prime-radiant-inc/iterative-development --skill extracting-requirementsgit clone --depth 1 https://github.com/prime-radiant-inc/iterative-developmentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/prime-radiant-inc/iterative-development/extracting-requirements)<a href="https://agentmods.dev/skills/prime-radiant-inc/iterative-development/extracting-requirements"><img src="https://agentmods.dev/badge/skills/prime-radiant-inc/iterative-development/extracting-requirements/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/prime-radiant-inc/iterative-development/extracting-requirements"><img src="https://agentmods.dev/badge/skills/prime-radiant-inc/iterative-development/extracting-requirements.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.02381 |
| Opus 5 | $0.00020 | $0.01190 |
| Sonnet 5 | $0.00008 | $0.00476 |
| Haiku 4.5 | $0.00004 | $0.00238 |
Grade A, and why
extracting-requirements scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extracting Requirements
Overview
Reads arbitrary human spec collateral and produces two artifact sets:
- Per-epic requirement files in
docs/superpowers/iterations/requirements/— story cards with proof obligations per AC - Behavior scenarios in
docs/superpowers/iterations/behavior-scenarios.md— reusable observable-behavior contracts with stable IDs
Uses a chunking + parallel-dispatch + aggregation pipeline so that no single agent holds the entire spec in context. Handles specs from a single page up to ~100K tokens across dozens of files.
When to Use
Invoked by iterative-development during bootstrap, or standalone when you need to regenerate requirements from human spec collateral.
Script Location
All scripts referenced below live in this skill's scripts/ directory, next to this SKILL.md file.
Key Concept: Spec Taxonomy
The spec directory structure drives proof seam classification. See skills/shared/behavior-evidence-formats.md for the full taxonomy. Summary:
| Spec directory | Default proof seam |
|---|---|
test-vectors/ |
unit |
contracts/ |
integration |
domains/ |
integration or app-level |
journeys/ |
e2e |
Extraction subagents use the appropriate prompt variant based on source file location.
Pipeline
1. Inventory
Enumerate the spec files without reading full contents:
python3 "scripts/chunk_spec.py" <spec-path>
This produces a JSON array of chunks. Each chunk has source_file, heading, start_line, end_line, content, and estimated_tokens. Small files (< 4K tokens) are kept whole. Larger files are split by ## headings, or ### if sections are still too large.
Classify each chunk by spec taxonomy: note whether the source file is under journeys/, contracts/, domains/, or test-vectors/. This determines which extraction prompt variant to use.
2. Dispatch extraction subagents
For each chunk (or batch of small chunks), dispatch an extraction subagent using the appropriate template from extraction-subagent-prompt.md:
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- extraction-subagent-prompt.md 7.2 KB
- scripts/aggregate_scenarios.py 7.4 KB runs code
- scripts/aggregate_stories.py 6.4 KB runs code
- scripts/backlink_scenarios.py 3.6 KB runs code
- scripts/chunk_spec.py 4.9 KB runs code
- scripts/validate_requirements_index.py 2.6 KB runs code
- scripts/validate_scenarios.py 3.9 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 199 lines · 40 tokens per session scan A feec652cec03
extracting-requirements is a skill published in the GitHub repository prime-radiant-inc/iterative-development (179 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 2,381 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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