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 agentmods add skills/terry-mao/aicodingflow/create-product-specnpx skills add Terry-Mao/AICodingFlow --skill create-product-specgit clone --depth 1 https://github.com/Terry-Mao/AICodingFlowWrote 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/terry-mao/aicodingflow/create-product-spec)<a href="https://agentmods.dev/skills/terry-mao/aicodingflow/create-product-spec"><img src="https://agentmods.dev/badge/skills/terry-mao/aicodingflow/create-product-spec.svg" alt="Measured on agentmods" 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 | $0.00078 | $0.01091 |
| Opus 5 | $0.00039 | $0.00545 |
| Sonnet 5 | $0.00016 | $0.00218 |
| Haiku 4.5 | $0.00008 | $0.00109 |
Grade A, and why
create-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 4d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
create-product-spec
Create a product spec from a GitHub issue for this repository.
Overview
This skill is a wrapper around the local shared product-spec workflow:
.agents/skills/write-product-spec/SKILL.md
Use that shared local skill as the base behavior and structure unless this wrapper overrides it. Keep the same emphasis on precise user-facing behavior, invariants, edge cases, validation, and open questions.
The differences are:
- the primary input is a GitHub issue, not a Linear issue
- the output path is
specs/issue-<issue-number>/product.md - the workflow or prompt provides the issue context path; in CI this is often
issue_context.json, while local wrappers should provide a path in a system temporary directory - the workflow or prompt may provide an issue comments path; in CI this is
often
issue_comments.txt - a workflow may also request a structured PR metadata output path; in CI this
is often
pr-metadata.json - do not create or edit Linear issues as part of this workflow
Inputs
Expect issue details in the issue context file named by the prompt, including
the issue number, title, description, labels, assignees, triggering comment
when present, and exact product_spec path. If the prompt does not provide an
explicit path, use issue_context.json in the current workflow worktree.
Use the issue comments file named by the prompt as prior discussion context
when present. If no explicit path is provided, use issue_comments.txt in the
current workflow worktree when it exists. Treat comments as additional context,
not as a silent override of the issue body. Resolved decisions from comments
can refine the spec; unresolved disagreements should remain explicit open
questions.
Workflow
- Start from the local shared
write-product-specguidance and follow its structure and writing standards unless this wrapper says otherwise. - Read the prompt-provided issue context path carefully. If a prompt-provided issue comments path exists, review it for clarifications, prior decisions, and issue-comment nuance that should influence the spec.
- Inspect the repository enough to understand the current user workflow and likely scope before writing the spec.
- Create or update the exact
product_specpath fromissue_context.json. - Keep the product spec focused on intended behavior and user-facing requirements. Use the shared skill's sections as the baseline, adapted to this repository and issue format. At minimum, cover:
- summary
- problem
- goals
- non-goals or scope boundaries
- concrete user experience and behavior requirements
- success criteria
- validation
- open product questions
- If design context such as a Figma link is present in the issue description or comments, include it. If no design context exists, make that absence explicit rather than silently omitting it.
- Do not include implementation details, file-level changes, or technical design. Those belong in the tech spec.
- Do not implement the feature or modify production code as part of this task. Limit changes to the product spec artifact. Treat temporary context and comments files as scratch input only and do not commit them.
- Do not include issue number references (e.g.
(#N),Refs #N) in commit messages. The issue is already linked in the PR. - If the prompt asks for PR metadata, write it to the exact metadata output
path named by the prompt. If no explicit path is provided, use
pr-metadata.jsonin the current workflow worktree. The file must contain a JSON object with the fieldsbranch_name,pr_title, andpr_summary. Thepr_summaryshould summarize the product and technical planning clearly enough that reviewers can use it directly as the PR body. For spec-only PRs, include a non-closing reference to the source issue such asRefs #<issue-number>rather than closing keywords likeClosesorFixes. - Default behavior: do not stage files, create commits, push branches, open pull requests, or use the GitHub CLI.
- In your final response, provide a brief summary of the product spec and call out any assumptions or open questions so the workflow can reuse that summary when creating the PR.
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.
- 4d ago First seen · 86 lines · 78 tokens per session scan A 30fd08847048
create-product-spec is a skill published in the GitHub repository Terry-Mao/AICodingFlow (165 stars, last pushed 6d ago), licensed MIT. It adds 78 tokens to every session and 1,091 once invoked, about $0.0004 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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