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 PyModel/pythinker-cli --skill write-product-specgit clone --depth 1 https://github.com/PyModel/pythinker-cliWrote 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/pymodel/pythinker-cli/write-product-spec)<a href="https://agentmods.dev/skills/pymodel/pythinker-cli/write-product-spec"><img src="https://agentmods.dev/badge/skills/pymodel/pythinker-cli/write-product-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/pymodel/pythinker-cli/write-product-spec"><img src="https://agentmods.dev/badge/skills/pymodel/pythinker-cli/write-product-spec.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 | $0.00031 | $0.00218 |
| Opus 5.5 | $0.00012 | $0.00087 |
| Sonnet 5.5 | $0.00006 | $0.00044 |
| Haiku 4.5 | $0.00003 | $0.00022 |
Grade A, and why
write-product-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 1mo 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.
What it actually says
Write Product Spec
Use when the user asks for a product spec, feature brief, issue-to-spec conversion, or acceptance criteria.
Workflow
- Restate the user problem and target users.
- Define goals and non-goals.
- Specify user-facing behavior and edge cases.
- Add acceptance criteria that can be verified.
- Note dependencies, rollout concerns, telemetry/observability needs, and open questions.
Rules
- Do not prescribe implementation details unless they affect product behavior.
- Separate confirmed requirements from assumptions.
- Keep acceptance criteria testable and specific.
- Ask the parent/user for missing product decisions rather than inventing them.
Output
# Product Spec: <title>
## Problem
## Goals
## Non-goals
## User Stories
## Requirements
## Acceptance Criteria
## Rollout / Risks
## Open Questions
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.
- 1mo ago First seen · 39 lines · 31 tokens per session scan A 5a9026fc588a
write-product-spec is a skill published in the GitHub repository PyModel/pythinker-cli (21 stars, last pushed 3d ago), licensed Apache-2.0. It adds 31 tokens to every session and 218 once invoked, about $0.0001 per session on Opus 5.5. 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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