create-product-spec

create-product-spec is a skill for Claude Code, Codex from Terry-Mao/AICodingFlow. It costs 78 tokens per session (1,091 once invoked), scanned A, original, MIT.

A workflow for turning a GitHub issue into a product specification, a document defining user-facing behavior and acceptance criteria.

In plain words
What is it for?
It prepares a product specification under the repository's specs directory and can include pull-request metadata without changing the related Linear workspace.
Why use it?
It turns an issue into a clearer, testable description of what should be built, including edge cases and unresolved questions.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/terry-mao/aicodingflow/create-product-spec
Any agent
npx skills add Terry-Mao/AICodingFlow --skill create-product-spec
Clone the repo
git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for create-product-spec

README.md
[![agentmods](https://agentmods.dev/badge/skills/terry-mao/aicodingflow/create-product-spec.svg)](https://agentmods.dev/skills/terry-mao/aicodingflow/create-product-spec)
Your own site
<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>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,091 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash 30fd08847048, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

.github/skills/create-product-spec/SKILL.md · 86 lines

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

  1. Start from the local shared write-product-spec guidance and follow its structure and writing standards unless this wrapper says otherwise.
  2. 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.
  3. Inspect the repository enough to understand the current user workflow and likely scope before writing the spec.
  4. Create or update the exact product_spec path from issue_context.json.
  5. 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
  6. 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.
  7. Do not include implementation details, file-level changes, or technical design. Those belong in the tech spec.
  8. 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.
  9. Do not include issue number references (e.g. (#N), Refs #N) in commit messages. The issue is already linked in the PR.
  10. 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.json in the current workflow worktree. The file must contain a JSON object with the fields branch_name, pr_title, and pr_summary. The pr_summary should 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 as Refs #<issue-number> rather than closing keywords like Closes or Fixes.
  11. Default behavior: do not stage files, create commits, push branches, open pull requests, or use the GitHub CLI.
  12. 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.

Read the full file on GitHub · 86 lines

Changes

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.

  1. 4d ago First seen · 86 lines · 78 tokens per session scan A 30fd08847048

Subscribe to this mod's changes

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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