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 lttr/claude-marketplace --skill to-specgit clone --depth 1 https://github.com/lttr/claude-marketplaceWrote 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/lttr/claude-marketplace/to-spec)<a href="https://agentmods.dev/skills/lttr/claude-marketplace/to-spec"><img src="https://agentmods.dev/badge/skills/lttr/claude-marketplace/to-spec/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/lttr/claude-marketplace/to-spec"><img src="https://agentmods.dev/badge/skills/lttr/claude-marketplace/to-spec.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 | $0.00030 | $0.00884 |
| Opus 5 | $0.00015 | $0.00442 |
| Sonnet 5 | $0.00006 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
to-spec 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 3d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill takes the current conversation context and codebase understanding and produces a spec (you may know this document as a PRD). Do NOT interview the user, just synthesize what you already know.
Process
-
If the task folder has an
intent.md, read it first. The spec answers it in the originator's terms. Where the intent conflicts with an ADR, the glossary, or the codebase, raise it with the user and record what stays unresolved under Open Concerns. Don't edit the intent. -
Explore the repo to understand the current state of the codebase, if you haven't already. Use the project's domain glossary vocabulary throughout the spec, and respect any ADRs in the area you're touching.
-
Sketch out the seams at which you're going to test the feature. Existing seams should be preferred to new ones. Use the highest seam possible. If new seams are needed, propose them at the highest point you can. The fewer seams across the codebase, the better - the ideal number is one.
Check with the user that these seams match their expectations.
-
Audit the technical decisions the feature depends on: what handles each solved problem (auth, validation, jobs, …), where the code lives, what schema/API contracts change. Decisions already settled in the conversation go into the spec as-is. For any that were never settled, check the project's existing dependencies and stack conventions — usually those already cover it and no new dependency is needed; record what will be used. Only when nothing in the project covers a genuinely solved problem, pick the idiomatic candidate for the stack and confirm it with the user alongside the seams. Either way, don't leave the choice implicit in the spec.
-
Write the spec using the template below, then save it per the
aiwork-protocolskill.
Problem Statement
The problem that the user is facing, from the user's perspective.
Solution
The solution to the problem, from the user's perspective.
User Stories
A LONG, numbered list of user stories. Each user story should be in the format of:
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.
- 3d ago Changed · +8 lines fe2a5ca4c66e
- 11d ago First seen · 77 lines · 30 tokens per session scan A 4fbb606c757b
to-spec is a skill published in the GitHub repository lttr/claude-marketplace (2 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 884 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 2026-08-31.
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