Agent skill

Autospec Clarify

by ariel-frischer in ariel-frischer/autospec

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

MITAuto-check passedDevelopment

Install Autospec Clarify

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

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

GitHub CLI
$ gh skill install ariel-frischer/autospec autospec-clarify --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-clarify .claude/skills/autospec-clarify && 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-clarify
GitHub stars
144
Token cost
~2.2k tokens
SKILL.md length
985 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 6 steps: Load and analyze the spec file at… → Generate candidate questions (maximum… → Sequential questioning loop (interactive) → …
  • Development work in your project
  • SKILL.md covers User Input, Outline, Pre-computed Context and Key Rules
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Autospec Clarify is an agent skill from ariel-frischer/autospec. Identify underspecified areas in YAML spec and encode clarifications back into the spec.

Its SKILL.md is about 2.2k 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 Development. The repository describes itself as: CLI for streamlined spec-driven development. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/autospec-clarify”

Workflow steps

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

  1. Load and analyze the spec file at {{.FeatureSpec}}. Perform a structured ambiguity & coverage scan using this taxonomy. For each category…
  2. Generate candidate questions (maximum 5). Apply these constraints
  3. Sequential questioning loop (interactive)
  4. Integration after EACH accepted answer (incremental update approach)
  5. Validate the artifact after each write
  6. Report completion (after questioning loop ends)

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 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 Clarify loads about 2.2k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 985 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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). 985 words, ~2,166 tokens.

Download SKILL.mdSave it as .claude/skills/autospec-clarify/SKILL.md (or your agent's skills folder).
name
autospec-clarify
description
Identify underspecified areas in YAML spec and encode clarifications back into the spec.

autospec-clarify

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

Outline

Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec.yaml file.

Note: This clarification workflow should run BEFORE $autospec-plan. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.

Pre-computed Context

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

  • FEATURE_DIR: {{.FeatureDir}}
  • FEATURE_SPEC: {{.FeatureSpec}}
  1. Load and analyze the spec file at {{.FeatureSpec}}. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing.

    Functional Scope & Behavior:

    • Core user goals & success criteria
    • Explicit out-of-scope declarations
    • User roles / personas differentiation

    Domain & Data Model:

    • Entities, attributes, relationships
    • Identity & uniqueness rules
    • Lifecycle/state transitions
    • Data volume / scale assumptions

    Interaction & UX Flow:

    • Critical user journeys / sequences
    • Error/empty/loading states
    • Accessibility or localization notes

    Non-Functional Quality Attributes:

    • Performance (latency, throughput targets)
    • Scalability (horizontal/vertical, limits)
    • Reliability & availability (uptime, recovery expectations)
    • Observability (logging, metrics, tracing signals)
    • Security & privacy (authN/Z, data protection, threat assumptions)
    • Compliance / regulatory constraints (if any)

    Integration & External Dependencies:

    • External services/APIs and failure modes
    • Data import/export formats
    • Protocol/versioning assumptions

    Edge Cases & Failure Handling:

    • Negative scenarios
    • Rate limiting / throttling
    • Conflict resolution (e.g., concurrent edits)

    Constraints & Tradeoffs:

    • Technical constraints (language, storage, hosting)
    • Explicit tradeoffs or rejected alternatives

    Terminology & Consistency:

    • Canonical glossary terms
    • Avoided synonyms / deprecated terms

    Completion Signals:

    • Acceptance criteria testability
    • Measurable Definition of Done style indicators

    Misc / Placeholders:

    • TODO markers / unresolved decisions
    • Ambiguous adjectives ("robust", "intuitive") lacking quantification
  2. Generate candidate questions (maximum 5). Apply these constraints:

    • Maximum of 10 total questions across the whole session
    • Each question must be answerable with EITHER:
      • A short multiple-choice selection (2-5 distinct, mutually exclusive options), OR
      • A one-word / short-phrase answer (explicitly constrain: "Answer in <=5 words")
    • Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation
    • Ensure category coverage balance: attempt to cover the highest impact unresolved categories first
    • Exclude questions already answered, trivial stylistic preferences, or plan-level execution details
    • Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests
  3. Sequential questioning loop (interactive):

    • Present EXACTLY ONE question at a time

    • For multiple-choice questions:

      • Analyze all options and determine the most suitable option based on best practices, common patterns, risk reduction, and alignment with project goals
      • Present your recommended option prominently at the top with clear reasoning (1-2 sentences)
      • Format as: **Recommended:** Option [X] - <reasoning>
      • Then render all options as a Markdown table:
      OptionDescription
      A<Option A description>
      B<Option B description>
      C<Option C description>
      ShortProvide a different short answer (<=5 words)
      • After the table: You can reply with the option letter (e.g., "A"), accept the recommendation by saying "yes" or "recommended", or provide your own short answer.
    • For short-answer style (no meaningful discrete options):

      • Provide your suggested answer based on best practices and context
      • Format as: **Suggested:** <your proposed answer> - <brief reasoning>
      • Then output: Format: Short answer (<=5 words). You can accept the suggestion by saying "yes" or "suggested", or provide your own answer.
    • After the user answers:

      • If the user replies with "yes", "recommended", or "suggested", use your previously stated recommendation/suggestion as the answer
      • Otherwise, validate the answer maps to one option or fits the <=5 word constraint
      • If ambiguous, ask for a quick disambiguation
    • Stop asking when:

      • All critical ambiguities resolved early, OR
      • User signals completion ("done", "good", "no more"), OR
      • You reach 5 asked questions
    • Never reveal future queued questions in advance

    • If no valid questions exist at start, immediately report no critical ambiguities

  4. Integration after EACH accepted answer (incremental update approach):

    • Maintain in-memory representation of the spec.yaml plus the raw file contents

    • For the first integrated answer in this session, ensure a clarifications: section exists in the YAML

    • Add clarification entry in this format:

      yaml
      clarifications:
        - date: "<YYYY-MM-DD>"
          question: "<the question asked>"
          answer: "<the answer provided>"
          applied_to: "<section(s) updated>"
    • Then immediately apply the clarification to the most appropriate section(s):

      • Functional ambiguity -> Update or add items in requirements.functional
      • User interaction / actor distinction -> Update user_stories section
      • Data shape / entities -> Update key_entities section
      • Non-functional constraint -> Add/modify in requirements.non_functional
      • Edge case / negative flow -> Add to edge_cases section
      • Terminology conflict -> Normalize term across spec
    • If the clarification invalidates an earlier ambiguous statement, replace that statement

    • Save the spec file AFTER each integration (atomic overwrite)

    • Preserve YAML formatting: do not reorder unrelated sections; keep structure intact

    • Keep each inserted clarification minimal and testable

  5. Validate the artifact after each write:

    bash
    autospec artifact {{.FeatureSpec}}
    • If validation fails: fix schema errors (missing required fields, invalid types) and retry
    • If validation passes: proceed
  6. Report completion (after questioning loop ends):

    • Number of questions asked & answered
    • Path to updated spec.yaml
    • Sections touched (list names)
    • Coverage summary table listing each taxonomy category with Status:
      • Resolved (was Partial/Missing and addressed)
      • Deferred (exceeds question quota or better suited for planning)
      • Clear (already sufficient)
      • Outstanding (still Partial/Missing but low impact)
    • If any Outstanding or Deferred remain, recommend whether to proceed to $autospec-plan or run $autospec-clarify again
    • Suggested next command
Show full SKILL.md (93 more words)Show less

Key Rules

  • Output MUST be valid YAML (use autospec artifact {{.FeatureSpec}} to verify schema compliance)
  • If no meaningful ambiguities found, respond: "No critical ambiguities detected worth formal clarification." and suggest proceeding
  • If spec file missing, instruct user to run $autospec-specify first
  • Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions)
  • Avoid speculative tech stack questions unless the absence blocks functional clarity
  • Respect user early termination signals ("stop", "done", "proceed")
  • If quota reached with unresolved high-impact categories remaining, explicitly flag them under Deferred with rationale

© 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-clarify of ariel-frischer/autospec.

Open the folder on GitHubat commit 3381f26

Compare with similar skills

Autospec Clarify 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.

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Herdr Pre-Release Auditherdrdev/herdr43k—~289Automated safety check: PassApache-2.0
A2ui Issue Triagea2ui-project/a2ui17k—~1.5kAutomated safety check: PassApache-2.0
Happier Reviewhappier-dev/happier1.9k—~4.5kAutomated safety check: PassMIT

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Questions about Autospec Clarify

What does Autospec Clarify do?

Identify underspecified areas in YAML spec and encode clarifications back into the spec. Autospec Clarify is an agent skill from ariel-frischer/autospec. Identify underspecified areas in YAML spec and encode clarifications back into the spec.

When should I use Autospec Clarify?

Autospec Clarify fits situations like: development work in your project.

How do I install Autospec Clarify in Claude Code?

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

How do I install Autospec Clarify in Codex?

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

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

What does Autospec Clarify need to run?

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

Does Autospec Clarify 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 Clarify 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 Clarify use?

Autospec Clarify 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 Clarify use?

About 2.2k tokens (SKILL.md is roughly 8.7k 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 Clarify?

Skills that share tags, products or a category with Autospec Clarify: Run Nx Generator (nrwl/nx, 29k stars), LoopX PR Program Manager (loopx-project/loopx, 6.2k stars), Herdr Pre-Release Audit (herdrdev/herdr, 43k stars) and A2ui Issue Triage (a2ui-project/a2ui, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Autospec Clarify?

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.