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Agent skill

Feature Planning

by HybridAIOne in HybridAIOne/hybridclaw

Break features into implementation plans, acceptance criteria, and sequenced tasks.

MITAuto-check passedAgent Workflows

Install Feature Planning

skills CLI
$ npx skills add HybridAIOne/hybridclaw --skill feature-planning -a claude-code

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

GitHub CLI
$ gh skill install HybridAIOne/hybridclaw feature-planning --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/HybridAIOne/hybridclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/feature-planning .claude/skills/feature-planning && 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-planning
GitHub stars
159
Token cost
~720 tokens
SKILL.md length
352 words
Files
1
Skills in repo
72
Repo updated
First seen
Licence
MIT

At a glance

Break features into implementation plans, acceptance criteria, and sequenced tasks.

  • Works in 6 steps: Confirm the goal, constraints, and… → Inspect the current code paths, types,… → Identify the files and system boundaries… → …
  • Tasks that involve Planning
  • SKILL.md covers Planning Workflow, Default Output, Task Rules and Codebase Exploration, plus 3 more sections
  • Calls npm

What it does

Feature Planning is an agent skill from HybridAIOne/hybridclaw. Break features into implementation plans, acceptance criteria, and sequenced tasks.

Its SKILL.md is about 720 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 Agent Workflows, covering Planning and User stories. The repository describes itself as: Enterprise-ready self-hosted AI assistant runtime with sandboxed execution, secure credentials, approvals, and memory. The licence is MIT.

When your agent uses it

  • Tasks that involve Planning
  • Tasks that involve User stories

Example prompts

  • “/feature-planning”

Workflow steps

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

  1. Confirm the goal, constraints, and non-goals.
  2. Inspect the current code paths, types, tests, and similar features.
  3. Identify the files and system boundaries likely to change.
  4. Break the work into small sequenced tasks.
  5. Define validation for each stage and for the final change.
  6. Capture risks, dependencies, and unanswered questions.

What it can do on your machine

Read from SKILL.md and the folder at commit 8162701. 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:

    • npm

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

  • Network

    No URLs in SKILL.md. Its commands use npm, 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 Planning loads about 720 tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 352 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~720

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 HybridAIOne/hybridclaw at commit 8162701, republished under its MIT licence (© HybridAIOne). 352 words, ~720 tokens.

Download SKILL.mdSave it as .claude/skills/feature-planning/SKILL.md (or your agent's skills folder).
name
feature-planning
description
Break features into implementation plans, acceptance criteria, and sequenced tasks.
user-invocable
true

Feature Planning

Use this skill to turn a feature request into an implementation plan that is specific enough to execute without rediscovering the codebase.

Planning Workflow

  1. Confirm the goal, constraints, and non-goals.
  2. Inspect the current code paths, types, tests, and similar features.
  3. Identify the files and system boundaries likely to change.
  4. Break the work into small sequenced tasks.
  5. Define validation for each stage and for the final change.
  6. Capture risks, dependencies, and unanswered questions.

Default Output

When the user asks for a plan and does not specify a format, use:

  1. Goal
  2. Current state
  3. Proposed approach
  4. Task breakdown
  5. Validation plan
  6. Risks and unknowns
  7. Recommended next action

Task Rules

Each task should have one concrete outcome. Prefer:

  • exact file paths instead of vague module names
  • explicit commands instead of "run the tests"
  • acceptance criteria that can be verified
  • clear notes on migrations, docs, config, or rollout work when relevant

If the scope is large, group tasks into milestones, but keep each task small enough that an implementer can finish it without further decomposition.

Codebase Exploration

Before finalizing a plan, inspect the repo for:

  • existing patterns that should be preserved
  • nearby tests and fixtures
  • configuration or schema touchpoints
  • user-facing docs or CLI/help text that may need updates

Use the existing codebase to anchor the plan instead of inventing new patterns.

Show full SKILL.md (122 more words)Show less

Validation Expectations

Every plan should name the checks needed to prove the change works. Prefer the smallest useful set, for example:

bash
npm run typecheck
npm run lint
npm run test:unit

Replace generic commands with repo-specific ones after inspecting the project.

Working Rules

  • Separate required work from optional polish.
  • State assumptions when dates, estimates, or dependencies are uncertain.
  • Sequence risky or high-uncertainty work before cleanup and polish.
  • Name what will not change so scope stays bounded.
  • Flag decisions that need user input instead of hiding them in the plan.

Common Outputs

Use whichever artifact best fits the request:

  • implementation plan
  • milestone breakdown
  • acceptance criteria
  • rollout checklist
  • dependency map
  • risk register

For plans that will be handed to another implementer, optimize for clarity over brevity: exact files, concrete commands, and explicit success criteria.

© HybridAIOne, 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-planning of HybridAIOne/hybridclaw.

Open the folder on GitHubat commit 8162701

Compare with similar skills

Feature Planning 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 Planning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Feature Planning this skillHybridAIOne/hybridclaw159—~720Automated safety check: PassMIT
Gen Planalibaba/atrex-kernel-agent167—~3.4kAutomated safety check: PassMIT
Code Task Generatormikeyobrien/ralph-orchestrator3.2k—~1.6kAutomated safety check: PassMIT
Autospec Tasksariel-frischer/autospec144—~2.5kAutomated safety check: PassMIT
Notion Spec To Implementationrongxinzy/RongxinAI1542 repos~2.2kAutomated safety check: PassAGPL-3.0
Factory Queuetikalk/adlc-team-skills141—~1.3kAutomated safety check: PassMIT

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    alibaba/atrex-kernel-agent

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Categories

Questions about Feature Planning

What does Feature Planning do?

Break features into implementation plans, acceptance criteria, and sequenced tasks. Feature Planning is an agent skill from HybridAIOne/hybridclaw. Break features into implementation plans, acceptance criteria, and sequenced tasks.

When should I use Feature Planning?

Feature Planning fits situations like: tasks that involve Planning; tasks that involve User stories.

How do I install Feature Planning in Claude Code?

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

How do I install Feature Planning in Codex?

Run `npx skills add HybridAIOne/hybridclaw --skill feature-planning -a codex`. Or copy the skill folder (skills/feature-planning in HybridAIOne/hybridclaw) into .agents/skills/feature-planning in your project. Codex loads it when a task matches its description.

Can I use Feature Planning 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 HybridAIOne/hybridclaw --skill feature-planning -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-planning, .gemini/skills/feature-planning, .github/skills/feature-planning and .opencode/skills/feature-planning in your project.

What does Feature Planning need to run?

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

Does Feature Planning access the network?

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

Is Feature Planning 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 Planning use?

Feature Planning 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 Planning use?

About 720 tokens (SKILL.md is roughly 2.9k 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 Planning?

Skills that share tags, products or a category with Feature Planning: Gen Plan (alibaba/atrex-kernel-agent, 167 stars), Code Task Generator (mikeyobrien/ralph-orchestrator, 3.2k stars), Autospec Tasks (ariel-frischer/autospec, 144 stars) and Notion Spec To Implementation (rongxinzy/RongxinAI, 154 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Feature Planning?

HybridAIOne (a GitHub organization) maintains it in HybridAIOne/hybridclaw, which has 159 GitHub stars. The repository holds 72 skills in this directory. The repository was last updated on October 9, 2026.

Source: HybridAIOne/hybridclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.