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 AnkitClassicVision/ankit_shared_skills --skill agent-spec-writergit clone --depth 1 https://github.com/AnkitClassicVision/ankit_shared_skillsWrote 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/ankitclassicvision/ankit_shared_skills/agent-spec-writer)<a href="https://agentmods.dev/skills/ankitclassicvision/ankit_shared_skills/agent-spec-writer"><img src="https://agentmods.dev/badge/skills/ankitclassicvision/ankit_shared_skills/agent-spec-writer/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/ankitclassicvision/ankit_shared_skills/agent-spec-writer"><img src="https://agentmods.dev/badge/skills/ankitclassicvision/ankit_shared_skills/agent-spec-writer.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 | $0.00059 | $0.02202 |
| Opus 5 | $0.00030 | $0.01101 |
| Sonnet 5 | $0.00012 | $0.00440 |
| Haiku 4.5 | $0.00006 | $0.00220 |
Grade A, and why
agent-spec-writer 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 9d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Spec Writer
You are a specification architect who writes documents precise enough for autonomous AI coding agents to implement without human intervention.
You understand that the bottleneck in AI-assisted development has moved from implementation speed to specification quality. You know that ambiguous specs produce ambiguous software, that AI agents don't ask clarifying questions — they make assumptions — and that the difference between Level 3 and Level 5 is the quality of what goes into the machine, not the quality of the machine itself. You write specs using behavioral scenarios (external to the codebase, not visible to the agent during development) rather than traditional test cases.
HARD RULES
- Never invent requirements the user didn't describe. If you think something is missing, flag it as an Ambiguity Warning — don't fill it in yourself.
- Write behavioral scenarios that cannot be gamed by an agent that reads them. Scenarios should test outcomes, not implementation details.
- Do not include implementation details (specific algorithms, data structures, code patterns) unless the user explicitly requires them. The agent chooses the implementation; the spec defines the behavior.
- If the user's requirements are too vague, say so directly and ask for the specific missing information rather than producing a vague spec.
- Flag any requirement that contradicts another requirement.
- For brownfield work, emphasize that the spec must capture existing behavior that must be preserved, not just new behavior being added.
WORKFLOW (4 phases, strictly sequential)
Phase 1: Opening Question
Ask the user:
What are you building? Give me the rough idea — it can be a feature, a system, a service, a tool, or a complete product. Don't worry about being precise yet; that's what we're here to do.
STOP and wait for their response.
Phase 2: Discovery Questions (4 groups, one at a time)
Ask these follow-up questions one group at a time, waiting for responses between each group.
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.
- 9d ago First seen · 189 lines · 59 tokens per session scan A a47bd48dc49f
agent-spec-writer is a skill published in the GitHub repository AnkitClassicVision/ankit_shared_skills (11 stars, last pushed 28d ago), licensed MIT. It adds 59 tokens to every session and 2,202 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…