Agent skill

Create Tech Spec

by Terry-Mao in Terry-Mao/AICodingFlow

Create a technical spec from a GitHub issue in this repository by applying the local shared write-tech-spec workflow with issue context and output paths.

MITAuto-check passedDevelopment

Install Create Tech Spec

skills CLI
$ npx skills add Terry-Mao/AICodingFlow --skill create-tech-spec -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Terry-Mao/AICodingFlow create-tech-spec --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Terry-Mao/AICodingFlow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/create-tech-spec .claude/skills/create-tech-spec && rm -rf skills-src

Use ~/.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/

Facts

Skill name
create-tech-spec
GitHub stars
167
Token cost
~1.2k tokens
SKILL.md length
677 words
Files
1
Skills in repo
30
Repo updated
First seen
Licence
MIT

At a glance

Create a technical spec from a GitHub issue in this repository by applying the local shared write-tech-spec workflow with issue context and output paths.

  • Works in 11 steps: Start from the local shared… → Read the prompt-provided issue context… → Inspect the repository to understand the… → …
  • 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 Tech Spec is an agent skill from Terry-Mao/AICodingFlow. Create a technical spec from a GitHub issue in this repository by applying the local shared write-tech-spec workflow with issue context and output paths. Use when an issue should be turned into a tech spec artifact stored under specs/issue-<issue-number/tech.md and the agent should prepare file changes only, without creating commits or pull requests itself.

Its SKILL.md is about 1.2k 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 Development, covering Pull requests. 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-tech-spec”

Workflow steps

11 steps, taken from the first numbered list in SKILL.md.

  1. Start from the local shared write-tech-spec guidance and follow its structure and writing standards unless this wrapper says otherwise.
  2. Read the prompt-provided issue context path carefully. Read the product spec
  3. Inspect the repository to understand the current implementation and the likely scope of the requested work before writing the spec. Do not…
  4. Create or update the exact tech_spec path from issue_context.json.
  5. Use the shared skill's structure as the baseline, adapted to this repository and issue format. At minimum, cover
  6. Keep the tech spec concise, actionable, and grounded in actual code paths and ownership boundaries in this repository.
  7. Do not implement the feature or modify production code as part of this task.
  8. Do not include issue number references (e.g. (#N), Refs #N) in commit messages. The issue is already linked in the PR.
  9. If the prompt asks for PR metadata, write it to the exact metadata output
  10. Default behavior: do not stage files, create commits, push branches, open pull requests, or use the GitHub CLI.
  11. In your final response, provide a brief summary of the tech spec and call out any assumptions or open questions so the workflow can reuse…

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 Tech Spec loads about 1.2k tokens when it runs. Until then it costs about 95 tokens; SKILL.md has 677 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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). 677 words, ~1,249 tokens.

Download SKILL.mdSave it as .claude/skills/create-tech-spec/SKILL.md (or your agent's skills folder).
name
create-tech-spec
description
Create a technical spec from a GitHub issue in this repository by applying the local shared `write-tech-spec` workflow with issue context and output paths. Use when an issue should be turned into a tech spec artifact stored under `specs/issue-<issue-number>/tech.md` and the agent should prepare file changes only, without creating commits or pull requests itself.

create-tech-spec

Create a tech spec from a GitHub issue for this repository.

Overview

This skill is a wrapper around the local shared tech-spec workflow:

  • .agents/skills/write-tech-spec/SKILL.md

Use that shared local skill as the base behavior and structure unless this wrapper overrides it. Keep the same emphasis on grounding the plan in current code, documenting relevant files and data flow, explaining tradeoffs, and defining validation.

The differences are:

  • the primary input is a GitHub issue, not a Linear issue
  • the output path is specs/issue-<issue-number>/tech.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, exact product_spec path, and exact tech_spec path. If the prompt does not provide an explicit path, use issue_context.json in the current workflow worktree.

When available, the product spec at the product_spec path from issue_context.json should be treated as the primary input for understanding the intended behavior. The tech spec translates that product intent into an implementation approach.

Workflow

  1. Start from the local shared write-tech-spec guidance and follow its structure and writing standards unless this wrapper says otherwise.
  2. Read the prompt-provided issue context path carefully. Read the product spec from the exact product_spec path first to understand the intended behavior. If a prompt-provided issue comments path exists, review it for clarifications, prior decisions, and design nuance that should influence the tech plan.
  3. Inspect the repository to understand the current implementation and the likely scope of the requested work before writing the spec. Do not guess about current architecture when the code can be inspected directly.
  4. Create or update the exact tech_spec path from issue_context.json.
  5. Use the shared skill's structure as the baseline, adapted to this repository and issue format. At minimum, cover:
    • problem
    • relevant code
    • current state
    • proposed changes
    • end-to-end flow when useful
    • risks and mitigations
    • testing and validation
    • follow-ups or open technical questions
  6. Keep the tech spec concise, actionable, and grounded in actual code paths and ownership boundaries in this repository.
  7. Do not implement the feature or modify production code as part of this task. Limit changes to the tech spec artifact and any minimal repository metadata needed to support it. Treat temporary context and comments files as scratch input only and do not commit them.
  8. Do not include issue number references (e.g. (#N), Refs #N) in commit messages. The issue is already linked in the PR.
  9. 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 resulting spec changes, validation, and any reviewer-relevant assumptions or open questions. 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.
  10. Default behavior: do not stage files, create commits, push branches, open pull requests, or use the GitHub CLI.
  11. In your final response, provide a brief summary of the tech 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 tech 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, and pr_summary.
  • If the issue is underspecified, still produce the best possible tech 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-tech-spec of Terry-Mao/AICodingFlow.

Open the folder on GitHubat commit 7703e16

Compare with similar skills

Create Tech 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.

Create Tech Spec compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Create Tech Spec this skillTerry-Mao/AICodingFlow167—~1.2kAutomated safety check: PassMIT
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Check PRonyx-dot-app/onyx32k2 repos~2.3kAutomated safety check: PassMIT
Contributor-First PR MergeHKUDS/OpenHarness16k1 repos~847Automated safety check: PassMIT
Create Pull Requestcline/cline70k1 repos~1.6kAutomated safety check: PassApache-2.0
Pull Request Title and Body Writeropeninterpreter/openinterpreter69k2 repos~1.1kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Create Tech Spec

What does Create Tech Spec do?

Create a technical spec from a GitHub issue in this repository by applying the local shared write-tech-spec workflow with issue context and output paths. Create Tech Spec is an agent skill from Terry-Mao/AICodingFlow. Create a technical spec from a GitHub issue in this repository by applying the local shared write-tech-spec workflow with issue context and output paths.

When should I use Create Tech Spec?

Create Tech Spec fits situations like: without creating commits; pull requests itself.

How do I install Create Tech Spec in Claude Code?

Run `npx skills add Terry-Mao/AICodingFlow --skill create-tech-spec -a claude-code`. Or copy the skill folder (.github/skills/create-tech-spec in Terry-Mao/AICodingFlow) into .claude/skills/create-tech-spec in your project. Claude Code loads it when a task matches its description.

How do I install Create Tech Spec in Codex?

Run `npx skills add Terry-Mao/AICodingFlow --skill create-tech-spec -a codex`. Or copy the skill folder (.github/skills/create-tech-spec in Terry-Mao/AICodingFlow) into .agents/skills/create-tech-spec in your project. Codex loads it when a task matches its description.

Can I use Create Tech 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-tech-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-tech-spec, .gemini/skills/create-tech-spec, .github/skills/create-tech-spec and .opencode/skills/create-tech-spec in your project.

What does Create Tech Spec need to run?

SKILL.md names no scripts, command-line tools or credentials: Create Tech Spec is instructions for the agent only.

Does Create Tech 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 Tech 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 Tech Spec use?

Create Tech 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 Tech Spec use?

About 1.2k tokens (SKILL.md is roughly 5k 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 Tech Spec?

Skills that share tags, products or a category with Create Tech Spec: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Check PR (onyx-dot-app/onyx, 32k stars), Contributor-First PR Merge (HKUDS/OpenHarness, 16k stars) and Create Pull Request (cline/cline, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create Tech 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.