Agent skill

Autospec Checklist

by ariel-frischer in ariel-frischer/autospec

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

MITAuto-check passedTesting & QA

Install Autospec Checklist

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

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

GitHub CLI
$ gh skill install ariel-frischer/autospec autospec-checklist --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-checklist .claude/skills/autospec-checklist && 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-checklist
GitHub stars
144
Token cost
~2.4k tokens
SKILL.md length
728 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 7 steps: Clarify intent (dynamic): Derive up to… → Understand user request: Combine… → Load feature context: Read from the… → …
  • Tasks that involve Unit testing
  • SKILL.md covers Checklist Purpose: "Unit Tests…, User Input, Pre-computed Context and Execution Steps, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Autospec Checklist is an agent skill from ariel-frischer/autospec. Generate YAML checklist for feature quality validation.

Its SKILL.md is about 2.4k 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 Testing & QA, covering Unit testing. The repository describes itself as: CLI for streamlined spec-driven development. The licence is MIT.

When your agent uses it

  • Tasks that involve Unit testing

Example prompts

  • “/autospec-checklist”

Workflow steps

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

  1. Clarify intent (dynamic): Derive up to THREE initial contextual clarifying questions. They MUST
  2. Understand user request: Combine $ARGUMENTS + clarifying answers
  3. Load feature context: Read from the feature directory
  4. Generate checklist.yaml - Create "Unit Tests for Requirements"
  5. Write the checklist to {{.FeatureDir}}/checklists/.yaml
  6. Validate the artifact
  7. 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 Checklist loads about 2.4k tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 728 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
~2.4k

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). 728 words, ~2,353 tokens.

Download SKILL.mdSave it as .claude/skills/autospec-checklist/SKILL.md (or your agent's skills folder).
name
autospec-checklist
description
Generate YAML checklist for feature quality validation.

autospec-checklist

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

Project specs directory: ./specs

Checklist Purpose: "Unit Tests for English"

CRITICAL CONCEPT: Checklists are UNIT TESTS FOR REQUIREMENTS WRITING - they validate the quality, clarity, and completeness of requirements in a given domain.

NOT for verification/testing:

  • NOT "Verify the button clicks correctly"
  • NOT "Test error handling works"
  • NOT "Confirm the API returns 200"
  • NOT checking if code/implementation matches the spec

FOR requirements quality validation:

  • "Are visual hierarchy requirements defined for all card types?" (completeness)
  • "Is 'prominent display' quantified with specific sizing/positioning?" (clarity)
  • "Are hover state requirements consistent across all interactive elements?" (consistency)
  • "Are accessibility requirements defined for keyboard navigation?" (coverage)
  • "Does the spec define what happens when logo image fails to load?" (edge cases)

Metaphor: If your spec is code written in English, the checklist is its unit test suite. You're testing whether the requirements are well-written, complete, unambiguous, and ready for implementation - NOT whether the implementation works.

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}}

Execution Steps

  1. Clarify intent (dynamic): Derive up to THREE initial contextual clarifying questions. They MUST:

    • Be generated from the user's phrasing + extracted signals from spec/plan/tasks
    • Only ask about information that materially changes checklist content
    • Be skipped individually if already unambiguous in $ARGUMENTS
    • Prefer precision over breadth

    Generation algorithm:

    1. Extract signals: feature domain keywords (e.g., auth, latency, UX, API), risk indicators ("critical", "must", "compliance"), stakeholder hints ("QA", "review", "security team")
    2. Cluster signals into candidate focus areas (max 4) ranked by relevance
    3. Identify probable audience & timing (author, reviewer, QA, release) if not explicit
    4. Detect missing dimensions: scope breadth, depth/rigor, risk emphasis, exclusion boundaries
    5. Formulate questions from these archetypes:
      • Scope refinement (e.g., "Should this include integration touchpoints?")
      • Risk prioritization (e.g., "Which risk areas need mandatory gating checks?")
      • Depth calibration (e.g., "Lightweight sanity list or formal release gate?")
      • Audience framing (e.g., "Author-only or peer PR review?")

    Defaults when interaction impossible:

    • Depth: Standard
    • Audience: Reviewer (PR) if code-related; Author otherwise
    • Focus: Top 2 relevance clusters
  2. Understand user request: Combine $ARGUMENTS + clarifying answers:

    • Derive checklist theme (e.g., security, review, deploy, ux)
    • Consolidate explicit must-have items mentioned by user
    • Map focus selections to category scaffolding
  3. Load feature context: Read from the feature directory:

    • spec.yaml: Feature requirements and scope
    • plan.yaml if exists: Technical details, dependencies, data model, API contracts
    • tasks.yaml if exists: Implementation tasks
  4. Generate checklist.yaml - Create "Unit Tests for Requirements":

    yaml
    checklist:
      feature: "<feature name from spec>"
      branch: "<current git branch>"
      spec_path: "<relative path to spec file>"
      domain: "<checklist domain: ux, api, security, performance, etc.>"
      audience: "<author|reviewer|qa|release>"
      depth: "<lightweight|standard|comprehensive>"
    
    categories:
      - name: "Requirement Completeness"
        description: "Are all necessary requirements documented?"
        items:
          - id: "CHK001"
            description: "Are all functional requirements specified for the primary user flow?"
            quality_dimension: "completeness"
            spec_reference: "FR-001"  # or null if checking for gap
            status: "pending"  # pending | pass | fail
            notes: ""
    
          - id: "CHK002"
            description: "Are error handling requirements defined for all API failure modes?"
            quality_dimension: "completeness"
            spec_reference: null
            status: "pending"
            notes: ""
    
      - name: "Requirement Clarity"
        description: "Are requirements specific and unambiguous?"
        items:
          - id: "CHK003"
            description: "Is 'fast loading' quantified with specific timing thresholds?"
            quality_dimension: "clarity"
            spec_reference: "NFR-001"
            status: "pending"
            notes: ""
    
      - name: "Requirement Consistency"
        description: "Do requirements align without conflicts?"
        items:
          - id: "CHK004"
            description: "Are navigation requirements consistent across all pages?"
            quality_dimension: "consistency"
            spec_reference: "FR-010"
            status: "pending"
            notes: ""
    
      - name: "Acceptance Criteria Quality"
        description: "Are success criteria measurable?"
        items:
          - id: "CHK005"
            description: "Can all success criteria be objectively verified?"
            quality_dimension: "measurability"
            spec_reference: "SC-001"
            status: "pending"
            notes: ""
    
      - name: "Scenario Coverage"
        description: "Are all flows and cases addressed?"
        items:
          - id: "CHK006"
            description: "Are requirements defined for zero-state scenarios?"
            quality_dimension: "coverage"
            spec_reference: null
            status: "pending"
            notes: ""
    
      - name: "Edge Case Coverage"
        description: "Are boundary conditions defined?"
        items:
          - id: "CHK007"
            description: "Is fallback behavior specified when external services fail?"
            quality_dimension: "edge_cases"
            spec_reference: null
            status: "pending"
            notes: ""
    
    summary:
      total_items: <number>
      passed: <number>
      failed: <number>
      pending: <number>
      pass_rate: "<percentage>"
    
    _meta:
      version: "1.0.0"
      generator: "autospec"
      generator_version: "<AUTOSPEC_VERSION from step 1>"
      created: "<CREATED_DATE from step 1>"
      artifact_type: "checklist"
  5. Write the checklist to {{.FeatureDir}}/checklists/<domain>.yaml

    • Create {{.FeatureDir}}/checklists/ directory if it doesn't exist
    • Use domain-based filename: ux.yaml, api.yaml, security.yaml, etc.
  6. Validate the artifact:

    bash
    autospec artifact checklist {{.FeatureDir}}/checklists/<domain>.yaml
    • If validation fails: fix schema errors (missing required fields, invalid types/enums) and retry
    • If validation passes: proceed to report
  7. Report: Output:

    • Full path to checklist.yaml
    • Item count by category
    • Gap markers count (requirements needing attention)
    • Checklist domain and audience
Show full SKILL.md (208 more words)Show less

HOW TO WRITE CHECKLIST ITEMS - "Unit Tests for English"

WRONG (Testing implementation):

  • "Verify landing page displays 3 episode cards"
  • "Test hover states work on desktop"
  • "Confirm logo click navigates home"

CORRECT (Testing requirements quality):

  • "Are the exact number and layout of featured episodes specified?" [Completeness]
  • "Is 'prominent display' quantified with specific sizing/positioning?" [Clarity]
  • "Are hover state requirements consistent across all interactive elements?" [Consistency]
  • "Are keyboard navigation requirements defined for all interactive UI?" [Coverage]
  • "Is the fallback behavior specified when logo image fails to load?" [Edge Cases]
Quality Dimensions
  • completeness: Are all necessary requirements present?
  • clarity: Are requirements unambiguous and specific?
  • consistency: Do requirements align with each other?
  • measurability: Can requirements be objectively verified?
  • coverage: Are all scenarios/edge cases addressed?
  • edge_cases: Are boundary conditions defined?
ABSOLUTELY PROHIBITED
  • Any item starting with "Verify", "Test", "Confirm", "Check" + implementation behavior
  • References to code execution, user actions, system behavior
  • "Displays correctly", "works properly", "functions as expected"
  • "Click", "navigate", "render", "load", "execute"
  • Test cases, test plans, QA procedures
  • Implementation details (frameworks, APIs, algorithms)
REQUIRED PATTERNS
  • "Are [requirement type] defined/specified/documented for [scenario]?"
  • "Is [vague term] quantified/clarified with specific criteria?"
  • "Are requirements consistent between [section A] and [section B]?"
  • "Can [requirement] be objectively measured/verified?"
  • "Are [edge cases/scenarios] addressed in requirements?"
  • "Does the spec define [missing aspect]?"

© 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-checklist of ariel-frischer/autospec.

Open the folder on GitHubat commit 3381f26

Compare with similar skills

Autospec Checklist 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 Checklist compared with similar skills
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RTK Filter TDD in Rustrtk-ai/rtk83k—~1.9kAutomated safety check: NotesApache-2.0
Test GuardamElnagdy/guard-skills1.3k2 repos~2.1kAutomated safety check: PassMIT
Creating A Packagec15t/c15t1.9k—~913Automated safety check: PassApache-2.0
Run Pre Gen ChecksGoogleCloudPlatform/magic-modules974—~492Automated safety check: PassCustom licence

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Questions about Autospec Checklist

What does Autospec Checklist do?

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

When should I use Autospec Checklist?

Autospec Checklist fits situations like: tasks that involve Unit testing.

How do I install Autospec Checklist in Claude Code?

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

How do I install Autospec Checklist in Codex?

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

Can I use Autospec Checklist 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-checklist -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-checklist, .gemini/skills/autospec-checklist, .github/skills/autospec-checklist and .opencode/skills/autospec-checklist in your project.

What does Autospec Checklist need to run?

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

Does Autospec Checklist 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 Checklist 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 Checklist use?

Autospec Checklist 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 Checklist use?

About 2.4k tokens (SKILL.md is roughly 9.4k 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 Autospec Checklist?

Skills that share tags, products or a category with Autospec Checklist: Rust TDD Workflow (rtk-ai/rtk, 83k stars), RTK Filter TDD in Rust (rtk-ai/rtk, 83k stars), Test Guard (amElnagdy/guard-skills, 1.3k stars) and Creating A Package (c15t/c15t, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Autospec Checklist?

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.