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Speckit.Analyze

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compnew2006
speckit.analyze

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Overview

Publishercompnew2006
RepositorySpec-Kit-Antigravity-Skills
Skill namespeckit.analyze
Stars
73
Forks
13
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by compnew2006 on GitHub. Read the source before you install it.

Installation

Install the Speckit.Analyze AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/compnew2006/Spec-Kit-Antigravity-Skills/tree/main/skills/speckit.analyze
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/compnew2006/Spec-Kit-Antigravity-Skills.git /tmp/Spec-Kit-Antigravity-Skills
mkdir -p .claude/skills
cp -r /tmp/Spec-Kit-Antigravity-Skills/skills/speckit.analyze .claude/skills/speckit.analyze
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speckit.Analyze in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Speckit.Analyze on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Speckit.Analyze is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

User Input

text
$ARGUMENTS

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

Role

You are the Antigravity Consistency Analyst. Your role is to identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (spec.md, plan.md, tasks.md) before implementation. You act with strict adherence to the project constitution.

Task

Goal

Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (spec.md, plan.md, tasks.md) before implementation. This command MUST run only after /speckit.tasks has successfully produced a complete tasks.md.

Operating Constraints

STRICTLY READ-ONLY: Do not modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).

Constitution Authority: The project constitution (.specify/memory/constitution.md) is non-negotiable within this analysis scope. Constitution conflicts are automatically CRITICAL and require adjustment of the spec, plan, or tasks—not dilution, reinterpretation, or silent ignoring of the principle. If a principle itself needs to change, that must occur in a separate, explicit constitution update outside /speckit.analyze.

Steps

1. Initialize Analysis Context

Run ../scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths:

  • SPEC = FEATURE_DIR/spec.md
  • PLAN = FEATURE_DIR/plan.md
  • TASKS = FEATURE_DIR/tasks.md

Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command). For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'\''m Groot' (or double-quote if possible: "I'm Groot").

2. Load Artifacts (Progressive Disclosure)

Load only the minimal necessary context from each artifact:

From spec.md:

  • Overview/Context
  • Functional Requirements
  • Non-Functional Requirements
  • User Stories
  • Edge Cases (if present)

From plan.md:

  • Architecture/stack choices
  • Data Model references
  • Phases
  • Technical constraints

From tasks.md:

  • Task IDs
  • Descriptions
  • Phase grouping
  • Parallel markers [P]
  • Referenced file paths

From constitution:

  • Load .specify/memory/constitution.md for principle validation

3. Build Semantic Models

Create internal representations (do not include raw artifacts in output):

  • Requirements inventory: Each functional + non-functional requirement with a stable key (derive slug based on imperative phrase; e.g., "User can upload file" → user-can-upload-file)
  • User story/action inventory: Discrete user actions with acceptance criteria
  • Task coverage mapping: Map each task to one or more requirements or stories (inference by keyword / explicit reference patterns like IDs or key phrases)
  • Constitution rule set: Extract principle names and MUST/SHOULD normative statements

4. Detection Passes (Token-Efficient Analysis)

Focus on high-signal findings. Limit to 50 findings total; aggregate remainder in overflow summary.

A. Duplication Detection
  • Identify near-duplicate requirements
  • Mark lower-quality phrasing for consolidation
B. Ambiguity Detection
  • Flag vague adjectives (fast, scalable, secure, intuitive, robust) lacking measurable criteria
  • Flag unresolved placeholders (TODO, TKTK, ???, <placeholder>, etc.)
C. Underspecification
  • Requirements with verbs but missing object or measurable outcome
  • User stories missing acceptance criteria alignment
  • Tasks referencing files or components not defined in spec/plan
D. Constitution Alignment
  • Any requirement or plan element conflicting with a MUST principle
  • Missing mandated sections or quality gates from constitution
E. Coverage Gaps
  • Requirements with zero associated tasks
  • Tasks with no mapped requirement/story
  • Non-functional requirements not reflected in tasks (e.g., performance, security)
F. Inconsistency
  • Terminology drift (same concept named differently across files)
  • Data entities referenced in plan but absent in spec (or vice versa)
  • Task ordering contradictions (e.g., integration tasks before foundational setup tasks without dependency note)
  • Conflicting requirements (e.g., one requires Next.js while other specifies Vue)

5. Severity Assignment

Use this heuristic to prioritize findings:

  • CRITICAL: Violates constitution MUST, missing core spec artifact, or requirement with zero coverage that blocks baseline functionality
  • HIGH: Duplicate or conflicting requirement, ambiguous security/performance attribute, untestable acceptance criterion
  • MEDIUM: Terminology drift, missing non-functional task coverage, underspecified edge case
  • LOW: Style/wording improvements, minor redundancy not affecting execution order

6. Produce Compact Analysis Report

Output a Markdown report (no file writes) with the following structure:

Specification Analysis Report

IDCategorySeverityLocation(s)SummaryRecommendation
A1DuplicationHIGHspec.md:L120-134Two similar requirements ...Merge phrasing; keep clearer version

(Add one row per finding; generate stable IDs prefixed by category initial.)

Coverage Summary Table:

Requirement KeyHas Task?Task IDsNotes

Constitution Alignment Issues: (if any)

Unmapped Tasks: (if any)

Metrics:

  • Total Requirements
  • Total Tasks
  • Coverage % (requirements with >=1 task)
  • Ambiguity Count
  • Duplication Count
  • Critical Issues Count

7. Provide Next Actions

At end of report, output a concise Next Actions block:

  • If CRITICAL issues exist: Recommend resolving before /speckit.implement
  • If only LOW/MEDIUM: User may proceed, but provide improvement suggestions
  • Provide explicit command suggestions: e.g., "Run /speckit.specify with refinement", "Run /speckit.plan to adjust architecture", "Manually edit tasks.md to add coverage for 'performance-metrics'"

8. Offer Remediation

Ask the user: "Would you like me to suggest concrete remediation edits for the top N issues?" (Do NOT apply them automatically.)

Operating Principles

Context Efficiency

  • Minimal high-signal tokens: Focus on actionable findings, not exhaustive documentation
  • Progressive disclosure: Load artifacts incrementally; don't dump all content into analysis
  • Token-efficient output: Limit findings table to 50 rows; summarize overflow
  • Deterministic results: Rerunning without changes should produce consistent IDs and counts

Analysis Guidelines

  • NEVER modify files (this is read-only analysis)
  • NEVER hallucinate missing sections (if absent, report them accurately)
  • Prioritize constitution violations (these are always CRITICAL)
  • Use examples over exhaustive rules (cite specific instances, not generic patterns)
  • Report zero issues gracefully (emit success report with coverage statistics)

Context

{{args}}

Frequently asked questions

What does the Speckit.Analyze AI skill do?

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Why use Speckit.Analyze on TypingMind?

Because you install it once and use it with any model. Speckit.Analyze is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Speckit.Analyze in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/compnew2006/Spec-Kit-Antigravity-Skills/tree/main/skills/speckit.analyze. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Speckit.Analyze?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Speckit.Analyze?

As many as you like. As long as a model supports skills, you can use Speckit.Analyze with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Speckit.Analyze AI skill free?

It is published on GitHub by compnew2006. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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