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Agent skill

Feature Spec

by natnew in natnew/awesome-physical-ai

Kicks off a new feature by finding the next incomplete phase in specs/roadmap.md, creating a git branch, interviewing the user about scope/decisions/context, and writing a dated spec directory under…

MITAuto-check passedProduct & Project Management

Install Feature Spec

skills CLI
$ npx skills add natnew/awesome-physical-ai --skill feature-spec -a claude-code

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

GitHub CLI
$ gh skill install natnew/awesome-physical-ai feature-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/natnew/awesome-physical-ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/feature-spec .claude/skills/feature-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
feature-spec
GitHub stars
157
Token cost
~592 tokens
SKILL.md length
241 words
Files
1
Skills in repo
2
Repo updated
First seen
Licence
MIT

At a glance

Kicks off a new feature by finding the next incomplete phase in specs/roadmap.md, creating a git branch, interviewing the user about scope/decisions/context, and writing a dated spec directory under…

  • Works in 5 steps: Find the next phase → Create the branch → Interview the user — BEFORE writing any… → …
  • The user says feature spec
  • SKILL.md covers Workflow and Constraints
  • Calls git

What it does

Feature Spec is an agent skill from natnew/awesome-physical-ai. Kicks off a new feature by finding the next incomplete phase in specs/roadmap.md, creating a git branch, interviewing the user about scope/decisions/context, and writing a dated spec directory under specs/ containing plan.md, requirements.md, and validation.md. Trigger when the user says "feature spec", "next phase", "start the next feature", or invokes /feature-spec.

Its SKILL.md is about 590 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 and Git workflow. The repository describes itself as: A curated list of Robotics + AI resources to learn, build, deploy, and stay current in Physical AI / Embodied AI. 🌟 Star if you like it! The licence is MIT.

When your agent uses it

  • The user says feature spec
  • Start the next feature
  • Invokes /feature-spec

Example prompts

  • “feature spec”
  • “next phase”
  • “start the next feature”
  • “/feature-spec”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Find the next phase
  2. Create the branch
  3. Interview the user — BEFORE writing any files
  4. Read guidance files
  5. Create the spec directory

What it can do on your machine

Read from SKILL.md and the folder at commit 5ba1e94. 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

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Feature Spec loads about 592 tokens when it runs. Until then it costs about 96 tokens; SKILL.md has 241 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~96
When it runs · the whole SKILL.md, loaded when a task matches
~592

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 natnew/awesome-physical-ai at commit 5ba1e94, republished under its MIT licence (© natnew). 241 words, ~592 tokens.

Download SKILL.mdSave it as .claude/skills/feature-spec/SKILL.md (or your agent's skills folder).
name
feature-spec
description
Kicks off a new feature by finding the next incomplete phase in specs/roadmap.md, creating a git branch, interviewing the user about scope/decisions/context, and writing a dated spec directory under specs/ containing plan.md, requirements.md, and validation.md. Trigger when the user says "feature spec", "next phase", "start the next feature", or invokes /feature-spec.

Feature Spec

Workflow

1. Find the next phase

Read specs/roadmap.md. The next phase is the first section whose items are all [ ]. Note its name to derive the branch and directory name.

2. Create the branch
git checkout -b phase-N-<kebab-name>
3. Interview the user — BEFORE writing any files

Use AskUserQuestion with exactly 3 questions in one call:

HeaderQuestion focus
ScopeWhat the feature collects, exposes, or does — fields, behaviour, data shape
DecisionsKey implementation choices — storage, visibility, validation, UX pattern
ContextTone, constraints, or anything shaping the spec — copy style, stack limits, open questions

Do not write any files until the user has answered all three questions.

4. Read guidance files

Read specs/mission.md and specs/tech-stack.md before drafting.

5. Create the spec directory

Name: specs/YYYY-MM-DD-<feature-name>/ using today's date.

requirements.md
  • Scope section: what is and is not included; field/data table if applicable
  • Decisions section: choices made and why (draw from user answers)
  • Context section: tone rules, stack pointers, existing patterns to follow
plan.md
  • Numbered task groups appropriate to the feature (for example: Data → Components → Page & Route → Navigation → Tests)
  • Each group has numbered sub-tasks; groups should be independently implementable
validation.md
  • Automated: project test and typecheck commands pass; specific assertions required
  • Manual: walkthrough, behaviour, edge cases
  • Tone check if the feature has user-facing copy
  • Definition of done

Constraints

  • Respect the existing tech stack defined in specs/tech-stack.md — no new dependencies without user approval
  • Follow existing conventions and patterns already established in the codebase
  • Keep feature scope focused and independently shippable

© natnew, 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 skills/feature-spec of natnew/awesome-physical-ai.

Open the folder on GitHubat commit 5ba1e94

Compare with similar skills

Feature 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.

Feature Spec compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Feature Spec this skillnatnew/awesome-physical-ai157—~592Automated safety check: PassMIT
Schematicblader/schematic240—~2.2kAutomated safety check: PassMIT
Build MvpBuildGreatProducts/builder-os228—~1.2kAutomated safety check: NotesMIT
Spec LiteChorus-AIDLC/Chorus1.2k—~2.2kAutomated safety check: PassAGPL-3.0
Spec LiteChorus-AIDLC/Chorus1.2k—~2.1kAutomated safety check: PassAGPL-3.0
Spec LiteChorus-AIDLC/Chorus1.2k—~2.4kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Feature Spec

What does Feature Spec do?

Kicks off a new feature by finding the next incomplete phase in specs/roadmap.md, creating a git branch, interviewing the user about scope/decisions/context, and writing a dated spec directory under…. Feature Spec is an agent skill from natnew/awesome-physical-ai.md.

When should I use Feature Spec?

Feature Spec fits situations like: the user says feature spec; start the next feature; invokes /feature-spec.

How do I install Feature Spec in Claude Code?

Run `npx skills add natnew/awesome-physical-ai --skill feature-spec -a claude-code`. Or copy the skill folder (skills/feature-spec in natnew/awesome-physical-ai) into .claude/skills/feature-spec in your project. Claude Code loads it when a task matches its description.

How do I install Feature Spec in Codex?

Run `npx skills add natnew/awesome-physical-ai --skill feature-spec -a codex`. Or copy the skill folder (skills/feature-spec in natnew/awesome-physical-ai) into .agents/skills/feature-spec in your project. Codex loads it when a task matches its description.

Can I use Feature 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 natnew/awesome-physical-ai --skill feature-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/feature-spec, .gemini/skills/feature-spec, .github/skills/feature-spec and .opencode/skills/feature-spec in your project.

What does Feature Spec need to run?

Going by SKILL.md and its folder, Feature Spec needs the command-line tools its instructions call (git).

Does Feature Spec access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Feature 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 Feature Spec use?

Feature 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 Feature Spec use?

About 592 tokens (SKILL.md is roughly 2.4k 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 Feature Spec?

Skills that share tags, products or a category with Feature Spec: Schematic (blader/schematic, 240 stars), Build Mvp (BuildGreatProducts/builder-os, 228 stars), Spec Lite (Chorus-AIDLC/Chorus, 1.2k stars) and Spec Lite (Chorus-AIDLC/Chorus, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Feature Spec?

natnew (a GitHub user) maintains it in natnew/awesome-physical-ai, which has 157 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 7, 2026.

Source: natnew/awesome-physical-ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.