autospec.plan

A command that turns a feature specification into a YAML implementation plan. A feature specification describes the desired behavior, user needs, requirements, and constraints.

In plain words
What is it for?
Use it to examine an existing specification and produce a plan for how the feature should be designed and built in the project.
Why use it?
It organizes technical decisions and design work before coding starts. The plan records items such as the technology context, data model, API contracts, research, and project structure.

Command for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/ariel-frischer/autospec/autospec.plan
Clone the repo
git clone --depth 1 https://github.com/ariel-frischer/autospec

Made for: Claude Code.

Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,405 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00008 $0.01405
Opus 5 $0.00004 $0.00702
Sonnet 5 $0.00002 $0.00281
Haiku 4.5 $0.00001 $0.00140

Measured yesterday against content hash 60df35831d25, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

autospec.plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

internal/cli/admin/.claude/commands/autospec.plan.md · 205 lines

How it starts

The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.

User Input

$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:

    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"
    

Read the full file on GitHub · 205 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 205 lines · 8 tokens per session scan A 60df35831d25

Subscribe to this mod's changes

autospec.plan is a command published in the GitHub repository ariel-frischer/autospec (141 stars, last pushed 23d ago), licensed MIT. It adds 8 tokens to every session and 1,405 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.