CCPM Project Management
automazeio/ccpm
Runs a spec-driven workflow from PRD to epic to GitHub issues to parallel agents, with status, standup and blocked-work reports from bundled scripts.
Create a product spec from a GitHub issue in this repository by applying the local shared write-product-spec workflow with issue context and output paths.
Install Create Product Spec
$ npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Terry-Mao/AICodingFlow create-product-spec --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/create-product-spec .claude/skills/create-product-spec && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "create-product-spec" agent skill from https://github.com/Terry-Mao/AICodingFlow/tree/main/.github/skills/create-product-spec into .claude/skills/create-product-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-product-spec", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Terry-Mao/AICodingFlow/tree/main/.github/skills/create-product-specType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Terry-Mao/AICodingFlow create-product-spec --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.github/skills/create-product-spec .agents/skills/create-product-spec && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "create-product-spec" agent skill from https://github.com/Terry-Mao/AICodingFlow/tree/main/.github/skills/create-product-spec into .agents/skills/create-product-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-product-spec", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Terry-Mao/AICodingFlow create-product-spec --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.github/skills/create-product-spec .cursor/skills/create-product-spec && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "create-product-spec" agent skill from https://github.com/Terry-Mao/AICodingFlow/tree/main/.github/skills/create-product-spec into .cursor/skills/create-product-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-product-spec", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Terry-Mao/AICodingFlow.git --path .github/skills/create-product-spec--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Terry-Mao/AICodingFlow create-product-spec --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.github/skills/create-product-spec .gemini/skills/create-product-spec && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "create-product-spec" agent skill from https://github.com/Terry-Mao/AICodingFlow/tree/main/.github/skills/create-product-spec into .gemini/skills/create-product-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-product-spec", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Terry-Mao/AICodingFlow create-product-specInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .github/skills && cp -r skills-src/.github/skills/create-product-spec .github/skills/create-product-spec && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "create-product-spec" agent skill from https://github.com/Terry-Mao/AICodingFlow/tree/main/.github/skills/create-product-spec into .github/skills/create-product-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-product-spec", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Terry-Mao/AICodingFlow create-product-spec --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.github/skills/create-product-spec .opencode/skills/create-product-spec && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "create-product-spec" agent skill from https://github.com/Terry-Mao/AICodingFlow/tree/main/.github/skills/create-product-spec into .opencode/skills/create-product-spec/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "create-product-spec", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
create-product-spec- GitHub stars
- 167
- Token cost
- ~1.3k tokens
- SKILL.md length
- 696 words
- Files
- 1
- Skills in repo
- 30
- Repo updated
- First seen
- Licence
- MIT
At a glance
Create a product spec from a GitHub issue in this repository by applying the local shared write-product-spec workflow with issue context and output paths.
- Works in 12 steps: Start from the local shared… → Read the prompt-provided issue context… → Inspect the repository enough to… → …
- Without creating commits
- SKILL.md covers Overview, Inputs, Workflow and Output expectations
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Create Product Spec is an agent skill from Terry-Mao/AICodingFlow. Create a product spec from a GitHub issue in this repository by applying the local shared write-product-spec workflow with issue context and output paths. Use when an issue should be turned into a product spec artifact stored under specs/issue-<issue-number/product.md and the agent should prepare file changes only, without creating commits or pull requests itself.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Product & Project Management, covering PRD writing. It works with GitHub. The repository describes itself as: Setup a AI Coding Flow. The licence is MIT.
When your agent uses it
- Without creating commits
- Pull requests itself
Example prompts
- “/create-product-spec”
Workflow steps
12 steps, taken from the first numbered list in SKILL.md.
- Start from the local shared write-product-spec guidance and follow its structure and writing standards unless this wrapper says otherwise.
- Read the prompt-provided issue context path carefully. If a prompt-provided
- Inspect the repository enough to understand the current user workflow and likely scope before writing the spec.
- Create or update the exact product_spec path from issue_context.json.
- Keep the product spec focused on intended behavior and user-facing requirements. Use the shared skill's sections as the baseline, adapted…
- 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…
- 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.
- 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
- 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…
What it can do on your machine
Read from SKILL.md and the folder at commit 7703e16. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Create Product Spec loads about 1.3k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 696 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from Terry-Mao/AICodingFlow at commit 7703e16, republished under its MIT licence (© Terry-Mao). 696 words, ~1,283 tokens.
Download SKILL.mdSave it as
.claude/skills/create-product-spec/SKILL.md (or your agent's skills folder).- name
- create-product-spec
- description
- Create a product spec from a GitHub issue in this repository by applying the local shared `write-product-spec` workflow with issue context and output paths. Use when an issue should be turned into a product spec artifact stored under `specs/issue-<issue-number>/product.md` and the agent should prepare file changes only, without creating commits or pull requests itself.
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.
Show full SKILL.md (66 more words)Show less
Output expectations
- Leave the repository with the new or updated product spec file ready to be committed by the workflow.
- When requested by the prompt, leave a ready-to-use PR metadata file at the
prompt-provided path with
branch_name,pr_title, andpr_summary. - If the issue is underspecified, still produce the best possible product spec and clearly capture assumptions or open questions in the spec file and final response.
© Terry-Mao, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .github/skills/create-product-spec of Terry-Mao/AICodingFlow.
Open the folder on GitHubat commit 7703e16
Compare with similar skills
Create Product Spec next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Create Product Spec this skillTerry-Mao/AICodingFlow | 167 | — | ~1.3k | Automated safety check: Pass | MIT | |
| CCPM Project Managementautomazeio/ccpm | 8.4k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Write Update Tidb Docspingcap/docs | 615 | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| Create Product Specwarpdotdev/oz-for-oss | 313 | — | ~1.2k | Automated safety check: Pass | MIT | |
| RalphTheCraigHewitt/skills | 157 | — | ~1k | Automated safety check: Pass | MIT | |
| Ouroboros PM InterviewQ00/ouroboros | 6.2k | — | ~5.7k | Automated safety check: Pass | MIT |
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Create Product Spec
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Create a product spec from a GitHub issue in this repository by applying the shared write-product-spec workflow with Oz-specific issue context and output paths.
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Categories
Questions about Create Product Spec
What does Create Product Spec do?
Create a product spec from a GitHub issue in this repository by applying the local shared write-product-spec workflow with issue context and output paths. Create Product Spec is an agent skill from Terry-Mao/AICodingFlow. Create a product spec from a GitHub issue in this repository by applying the local shared write-product-spec workflow with issue context and output paths.
When should I use Create Product Spec?
Create Product Spec fits situations like: without creating commits; pull requests itself.
How do I install Create Product Spec in Claude Code?
Run `npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a claude-code`. Or copy the skill folder (.github/skills/create-product-spec in Terry-Mao/AICodingFlow) into .claude/skills/create-product-spec in your project. Claude Code loads it when a task matches its description.
How do I install Create Product Spec in Codex?
Run `npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a codex`. Or copy the skill folder (.github/skills/create-product-spec in Terry-Mao/AICodingFlow) into .agents/skills/create-product-spec in your project. Codex loads it when a task matches its description.
Can I use Create Product Spec in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Terry-Mao/AICodingFlow --skill create-product-spec -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-product-spec, .gemini/skills/create-product-spec, .github/skills/create-product-spec and .opencode/skills/create-product-spec in your project.
What does Create Product Spec need to run?
SKILL.md names no scripts, command-line tools or credentials: Create Product Spec is instructions for the agent only.
Does Create Product Spec access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Create Product Spec safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Create Product Spec use?
Create Product Spec is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Create Product Spec use?
About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Create Product Spec?
Skills that share tags, products or a category with Create Product Spec: CCPM Project Management (automazeio/ccpm, 8.4k stars), Write Update Tidb Docs (pingcap/docs, 615 stars), Create Product Spec (warpdotdev/oz-for-oss, 313 stars) and Ralph (TheCraigHewitt/skills, 157 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Create Product Spec?
Terry-Mao (a GitHub user) maintains it in Terry-Mao/AICodingFlow, which has 167 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 3, 2026.
Source: Terry-Mao/AICodingFlow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.