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 Avyayalaya/pm-skills-arsenal --skill specification-writinggit clone --depth 1 https://github.com/Avyayalaya/pm-skills-arsenalWrote 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/avyayalaya/pm-skills-arsenal/specification-writing)<a href="https://agentmods.dev/skills/avyayalaya/pm-skills-arsenal/specification-writing"><img src="https://agentmods.dev/badge/skills/avyayalaya/pm-skills-arsenal/specification-writing/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/avyayalaya/pm-skills-arsenal/specification-writing"><img src="https://agentmods.dev/badge/skills/avyayalaya/pm-skills-arsenal/specification-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00056 | $0.27033 |
| Opus 5 | $0.00028 | $0.13517 |
| Sonnet 5 | $0.00011 | $0.05407 |
| Haiku 4.5 | $0.00006 | $0.02703 |
Grade A, and why
specification-writing 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 12d 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.
This is a copy
86% identical to specification-writing — 80 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,700 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Produce a Zero-Question Specification — a spec so complete that any competent executor (AI agent, engineer, contractor, cross-functional team) can begin work immediately without asking a single clarifying question.
A Zero-Question Spec is not the same as a long spec. Length is a symptom, not a goal. The goal is that every assumption is surfaced, every ambiguity is resolved or explicitly marked TBD with an owner and deadline, every acceptance criterion is binary-testable, and every scope boundary names the adjacent capability it excludes.
This codex encodes six interlocking frameworks that systematize the transition from "rough feature intent" to "executable specification." Each framework targets a specific class of spec failure. Together, they eliminate the five most common causes of execution failure: unclear outcomes, untestable criteria, invisible scope creep, missing executor context, and silent assumptions.
When to Use / When NOT to Use
When to Use
- Writing a product or feature spec for a new user-facing capability
- Writing an API contract spec that multiple teams or services will implement against
- Writing an agent task spec for an AI agent that will execute autonomously
- Writing a process or workflow spec that crosses team boundaries
- Writing an infrastructure or migration spec where failure conditions are critical
- Writing a research or discovery spec where "done" is ambiguous without explicit criteria
- Upgrading a draft spec that has already generated clarifying questions from executors
- Reviewing an existing spec for completeness before handing off to execution
When NOT to Use
- Brainstorming or ideation — this skill assumes you already know WHAT to build; if you don't, use Problem Framing or Discovery first
- One-line tickets for well-understood changes — if the executor already has full context and the change is trivial (e.g., "change button color from blue to green"), a zero-question spec is overhead
- Exploratory prototypes with no success criteria — if the goal is "try things and see what works," a spec constrains prematurely. Use a time-boxed spike instead.
- Post-hoc documentation — this skill is for BEFORE execution, not for documenting what was already built
- Specs where the audience is exclusively yourself — the frameworks are calibrated for context transfer to another executor. If you are the sole executor and have full context, a lighter checklist may suffice.
What ships with it
2 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.
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.
- 12d ago First seen · 1,700 lines · 56 tokens per session scan A 17b5479a33d4
specification-writing is a skill published in the GitHub repository Avyayalaya/pm-skills-arsenal (6 stars, last pushed 3mo ago), licensed MIT. It adds 56 tokens to every session and 27,033 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to specification-writing, differing in 80 lines, and is treated as a copy.
Other skills, from other repositories
specification-writing
Use when writing a product spec, feature spec, API contract, agent task spec, or any other specification where a zero-question document is required. Encodes outcome-first methodology, acceptance criteria taxonomy, scope boundary protocol, executor context model, and ambiguity resolution framework.
dummy-dataset
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
outcome-roadmap
Transform an output-focused roadmap into an outcome-focused one that communicates strategic intent. Rewrites initiatives as outcome statements reflecting user and business impacts. Use when shifting to outcome roadmaps, making a roadmap more strategic, or rewriting feature lists as outcomes.
retro
Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.
release-notes
Generate user-facing release notes from tickets, PRDs, or changelogs. Creates clear, engaging summaries organized by category (new features, improvements, fixes). Use when writing release notes, creating changelogs, announcing product updates, or summarizing what shipped.
flow-next-impl-review
Carmack-level implementation review of changes via the configured backend. Use when asked to review code or a diff in a flow-next repo.