Agent skill

Autospec Plan

by ariel-frischer in ariel-frischer/autospec

Generate YAML implementation plan from feature specification.

MITAuto-check passedAgent Workflows

Install Autospec Plan

skills CLI
$ npx skills add ariel-frischer/autospec --skill autospec-plan -a claude-code

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

GitHub CLI
$ gh skill install ariel-frischer/autospec autospec-plan --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/ariel-frischer/autospec.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/autospec-plan .claude/skills/autospec-plan && 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
autospec-plan
GitHub stars
144
Token cost
~1.7k tokens
SKILL.md length
370 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Generate YAML implementation plan from feature specification.

  • Works in 6 steps: Load context → Execute plan workflow → Generate plan.yaml: Create the YAML plan… → …
  • Tasks that involve Planning
  • SKILL.md covers User Input, Pre-computed Context, Outline and Key Rules
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Autospec Plan is an agent skill from ariel-frischer/autospec. Generate YAML implementation plan from feature specification.

Its SKILL.md is about 1.7k 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. The repository describes itself as: CLI for streamlined spec-driven development. The licence is MIT.

When your agent uses it

  • Tasks that involve Planning

Example prompts

  • “/autospec-plan”

Workflow steps

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

  1. Load context
  2. Execute plan workflow
  3. Generate plan.yaml: Create the YAML plan file with this structure
  4. Write the plan to {{.FeatureDir}}/plan.yaml
  5. Validate the artifact
  6. Report: Output

What it can do on your machine

Read from SKILL.md and the folder at commit 3381f26. 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 (its code samples are yaml and bash).

    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

Autospec Plan loads about 1.7k tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 370 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~19
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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 ariel-frischer/autospec at commit 3381f26, republished under its MIT licence (© ariel-frischer). 370 words, ~1,671 tokens.

Download SKILL.mdSave it as .claude/skills/autospec-plan/SKILL.md (or your agent's skills folder).
name
autospec-plan
description
Generate YAML implementation plan from feature specification.

autospec-plan

This Agent Skill is generated from autospec.plan. When the user invokes "$autospec-plan" or "/autospec.plan", load and follow these instructions directly. Treat the text after the skill or command name as "$ARGUMENTS". Do not route back through "autospec plan"; this skill is the prompt for the stage.

Project specs directory: ./specs

User Input

text
$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Pre-computed Context

The following paths have been pre-computed and are available for use:

  • FEATURE_DIR: {{.FeatureDir}}
  • FEATURE_SPEC: {{.FeatureSpec}}
  • AUTOSPEC_VERSION: {{.AutospecVersion}}
  • CREATED_DATE: {{.CreatedDate}}

Outline

  1. Load context:

    • Read the spec file at {{.FeatureSpec}}
    • Read project constitution if exists (.autospec/constitution.yaml or AGENTS.md, falling back to agent-specific file like CLAUDE.md)
    • Extract: feature description, user stories, requirements, constraints
  2. Execute plan workflow:

    Phase 0: Outline & Research

    a. Identify technical unknowns from the spec:

    • For each unclear technology choice → research task
    • For each dependency → best practices research
    • For each integration → patterns research

    b. Resolve unknowns through exploration:

    • Examine existing codebase patterns
    • Consider project constraints
    • Make informed technology decisions

    c. Document research findings for inclusion in plan

    Phase 1: Design & Architecture

    a. Define technical context based on spec and research:

    • Language/framework (detect from existing code or choose)
    • Primary dependencies
    • Storage requirements
    • Testing approach
    • Target platform

    b. Design project structure:

    • Documentation files to create
    • Source code organization
    • Test file locations

    c. Identify data model entities from spec requirements

    d. Design API contracts if applicable

  3. Generate plan.yaml: Create the YAML plan file with this structure:

    yaml
    plan:
      branch: "<current git branch>"
      created: "<today's date YYYY-MM-DD>"
      spec_path: "<relative path to spec file>"
    
    summary: |
      <1-2 paragraph summary of the implementation approach.
      Explain key technical decisions and how they address the spec requirements.>
    
    technical_context:
      language: "<primary language>"
      framework: "<framework if applicable, or 'None'>"
      primary_dependencies:
        - name: "<dependency name>"
          version: "<version constraint>"
          purpose: "<why needed>"
      storage: "<storage technology or 'None'>"
      testing:
        framework: "<test framework>"
        approach: "<unit/integration/e2e strategy>"
      target_platform: "<platform(s)>"
      project_type: "<cli|web|mobile|library|service>"
      performance_goals: "<specific targets from spec>"
      constraints:
        - "<constraint from spec or technical>"
      scale_scope: "<expected scale/scope>"
    
    constitution_check:
      constitution_path: "<path to constitution file or 'Not found'>"
      gates:
        - name: "<principle name from constitution>"
          status: "PASS"  # or "FAIL" or "N/A"
          notes: "<how this plan addresses the principle>"
    
    research_findings:
      decisions:
        - topic: "<what was researched>"
          decision: "<what was chosen>"
          rationale: "<why chosen>"
          alternatives_considered:
            - "<alternative 1>"
            - "<alternative 2>"
    
    data_model:
      entities:
        - name: "<entity name>"
          description: "<what it represents>"
          fields:
            - name: "<field name>"
              type: "<data type>"
              description: "<purpose>"
              constraints: "<validation rules>"
          relationships:
            - target: "<related entity>"
              type: "<one-to-many|many-to-many|etc>"
              description: "<relationship meaning>"
    
    api_contracts:
      endpoints:
        - method: "<HTTP method>"
          path: "<endpoint path>"
          description: "<what it does>"
          request:
            content_type: "<content type>"
            body_schema: "<inline schema or reference>"
          response:
            success_code: 200
            body_schema: "<inline schema or reference>"
          errors:
            - code: 400
              description: "<when this occurs>"
    
    project_structure:
      documentation:
        - path: "<relative path>"
          description: "<purpose of this file>"
      source_code:
        - path: "<relative path or pattern>"
          description: "<what this contains>"
      tests:
        - path: "<relative path or pattern>"
          description: "<what tests live here>"
    
    implementation_phases:
      - phase: 1
        name: "<phase name>"
        goal: "<what this phase accomplishes>"
        deliverables:
          - "<deliverable 1>"
          - "<deliverable 2>"
      - phase: 2
        name: "<phase name>"
        goal: "<what this phase accomplishes>"
        dependencies:
          - "Phase 1"
        deliverables:
          - "<deliverable>"
    
    open_questions:
      - question: "<unresolved question>"
        context: "<why it matters>"
        proposed_resolution: "<suggested approach>"
    
    _meta:
      version: "1.0.0"
      generator: "autospec"
      generator_version: "{{.AutospecVersion}}"
      created: "{{.CreatedDate}}"
      artifact_type: "plan"
  4. Write the plan to {{.FeatureDir}}/plan.yaml

  5. Validate the artifact:

    bash
    autospec artifact {{.FeatureDir}}/plan.yaml
    • If validation fails: fix schema errors (missing required fields, invalid types) and retry
    • If validation passes: proceed to report
  6. Report: Output:

    • Branch name
    • Full path to plan.yaml
    • Summary of technical context
    • Number of implementation phases
    • Any constitution gate failures (CRITICAL if any FAIL)
    • Readiness for $autospec-tasks
Show full SKILL.md (72 more words)Show less

Key Rules

  • Output MUST be valid YAML (use autospec artifact {{.FeatureDir}}/plan.yaml to verify schema compliance)
  • Technical context should reflect actual project setup (detect from existing code)
  • Constitution gates are mandatory if constitution exists
  • Research findings should document all significant technical decisions
  • Data model should be derived from spec requirements
  • Project structure should follow existing codebase conventions
  • All YAML arrays use list syntax (not JSON inline)
  • Multi-line strings use | or > block scalar style

© ariel-frischer, 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 .agents/skills/autospec-plan of ariel-frischer/autospec.

Open the folder on GitHubat commit 3381f26

Compare with similar skills

Autospec Plan 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.

Autospec Plan compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Autospec Plan this skillariel-frischer/autospec144—~1.7kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers297k2 repos~5.1kAutomated safety check: PassMIT
Interview Meaddyosmani/agent-skills105k6 repos~3.8kAutomated safety check: PassMIT
OpenSpec Guided OnboardingFission-AI/OpenSpec72k1 repos~4.5kAutomated safety check: PassMIT
Writing Plansgeeksblabla/stateofdev.ma16358 repos~661Automated safety check: PassNone
Subagent Driven DevelopmentAsvarox/allkaraoke26138 repos~1.2kAutomated safety check: PassNone

Similar skills

  • Executing Plans Inline

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  • Interview Me

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  • OpenSpec Guided Onboarding

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    72k GitHub starsUsed in 1 repo~4.5k tokens
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  • Writing Plans

    geeksblabla/stateofdev.ma

    A skill your agent uses when design is complete and you need detailed implementation tasks for engineers with zero codebase context - creates comprehensive implementation plans with exact file…

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  • Subagent Driven Development

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More from ariel-frischer/autospec

All 9 skills in this repo
  • Autospec Analyze

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    Analyze cross-artifact consistency and quality in YAML format.

    144 GitHub stars~2.3k tokensUpdated 13 days ago
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  • Autospec Checklist

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    Generate YAML checklist for feature quality validation. An agent skill from ariel-frischer/autospec.

    144 GitHub stars~2.4k tokensUpdated 13 days ago
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  • Autospec Clarify

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    Identify underspecified areas in YAML spec and encode clarifications back into the spec.

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  • Autospec Constitution

    ariel-frischer/autospec

    Generate or update project constitution in YAML format. An agent skill from ariel-frischer/autospec.

    144 GitHub stars~2.1k tokensUpdated 13 days ago
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  • Autospec Specify

    ariel-frischer/autospec

    Generate YAML feature specification from natural language description.

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  • Autospec Tasks

    ariel-frischer/autospec

    Generate YAML task breakdown from implementation plan. An agent skill from ariel-frischer/autospec.

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Categories

Questions about Autospec Plan

What does Autospec Plan do?

Generate YAML implementation plan from feature specification. Autospec Plan is an agent skill from ariel-frischer/autospec. Generate YAML implementation plan from feature specification.

When should I use Autospec Plan?

Autospec Plan fits situations like: tasks that involve Planning.

How do I install Autospec Plan in Claude Code?

Run `npx skills add ariel-frischer/autospec --skill autospec-plan -a claude-code`. Or copy the skill folder (.agents/skills/autospec-plan in ariel-frischer/autospec) into .claude/skills/autospec-plan in your project. Claude Code loads it when a task matches its description.

How do I install Autospec Plan in Codex?

Run `npx skills add ariel-frischer/autospec --skill autospec-plan -a codex`. Or copy the skill folder (.agents/skills/autospec-plan in ariel-frischer/autospec) into .agents/skills/autospec-plan in your project. Codex loads it when a task matches its description.

Can I use Autospec Plan 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 ariel-frischer/autospec --skill autospec-plan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/autospec-plan, .gemini/skills/autospec-plan, .github/skills/autospec-plan and .opencode/skills/autospec-plan in your project.

What does Autospec Plan need to run?

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

Does Autospec Plan 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 Autospec Plan 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 Autospec Plan use?

Autospec Plan 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 Autospec Plan use?

About 1.7k tokens (SKILL.md is roughly 6.7k 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 Autospec Plan?

Skills that share tags, products or a category with Autospec Plan: Executing Plans Inline (obra/superpowers, 297k stars), Interview Me (addyosmani/agent-skills, 105k stars), OpenSpec Guided Onboarding (Fission-AI/OpenSpec, 72k stars) and Writing Plans (geeksblabla/stateofdev.ma, 163 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Autospec Plan?

ariel-frischer (a GitHub user) maintains it in ariel-frischer/autospec, which has 144 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on September 28, 2026.

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