spec-analyzer

spec-analyzer is a skill for Claude Code, Codex from srinidhis05/agentura. It costs 3 tokens per session (1,722 once invoked), scanned A, original, Apache-2.0.

A tool for turning a Lovable prototype—a web app made with a visual app-building service—into a detailed feature specification. It describes the screens, data, actions, and interfaces needed for backend and Android development.

In plain words
What is it for?
Use it to document a prototype's screens, navigation, data fields, API calls, and user interactions for backend and mobile teams.
Why use it?
It gives developers a shared description of what to build before implementation begins, reducing guesswork between a prototype and production code.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: model in frontmatter; positional $N argument.

Good fit Use it to document a prototype's screens, navigation, data fields, API calls, and user interactions for backend and mobile teams.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/srinidhis05/agentura/spec-analyzer
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.

Any agent
npx skills add srinidhis05/agentura --skill spec-analyzer
Clone the repo
git clone --depth 1 https://github.com/srinidhis05/agentura

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for spec-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/srinidhis05/agentura/spec-analyzer.svg)](https://agentmods.dev/skills/srinidhis05/agentura/spec-analyzer)
Your own site
<a href="https://agentmods.dev/skills/srinidhis05/agentura/spec-analyzer"><img src="https://agentmods.dev/badge/skills/srinidhis05/agentura/spec-analyzer.svg" alt="Measured on agentmods" height="20"></a>
Per session 3 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,722 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00003 $0.01722
Opus 5 $0.00002 $0.00861
Sonnet 5 $0.00001 $0.00344
Haiku 4.5 $0.00000 $0.00172

Measured 8d ago against content hash f8bb4c1ae6a3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

spec-analyzer 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 8d ago.

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.

skills/incubator/spec-analyzer/SKILL.md · 214 lines

How it starts

The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Spec Analyzer

Task

You analyze Lovable prototypes (URL + source code + PM description) and produce a structured feature specification for incubation into two production codebases:

  • backend-api — Spring Boot 4 / Java 21 / MongoDB backend. Features live as isolated "pits" under projects/<name>/.
  • mobile-app — Kotlin/Compose Android app. Features live under app/src/main/java/com/example/app/ui/<feature>/.

Your output drives downstream agents (pit-builder, mobile-builder) that clone these repos and implement. You define WHAT to build; the repos' own documentation defines HOW.

Execution Protocol

Phase 1: Parse Input

Identify what you received:

  • lovable_url — URL to the Lovable prototype (for context, not fetched)
  • lovable_code — exported source code from the prototype (React/TypeScript components)
  • description — PM's natural language description of the feature
  • context — optional business context, target users, priority

Extract from the prototype code:

  • UI screens and navigation flow
  • Data entities and their fields
  • API calls the prototype makes (even if mocked)
  • User interactions (forms, buttons, gestures)
  • Visual design elements (colors, layouts, components used)

Gate: Input parsed, feature scope understood.

Phase 2: Decompose Into Backend + Mobile

Map prototype elements to production architecture:

Backend (backend-api pit):

  1. Endpoints — REST APIs needed. Map prototype API calls → Spring @RequestMapping paths under /api/v1/<pit-name>/.
  2. Data model — MongoDB @Document classes. Map prototype entities → documents with proper field types.
  3. Business logic — Service layer operations. Map prototype interactions → service methods.
  4. External integrations — Any third-party APIs the feature needs.

Mobile (mobile-app feature):

  1. Screens — Compose screens needed. Map prototype pages → @Composable functions.
  2. Navigation — Screen flow. Map prototype routing → Compose Navigation destinations.
  3. Data layer — API service + repository + models. Map prototype data → Kotlin data classes + Retrofit endpoints.
  4. State management — ViewModels needed. Map prototype state → StateFlow holders.
  5. Feature flag — Remote Config key for gating.

Read the full file on GitHub · 214 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 8d ago First seen · 214 lines · 3 tokens per session scan A f8bb4c1ae6a3

Subscribe to this mod's changes

spec-analyzer is a skill published in the GitHub repository srinidhis05/agentura (9 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 3 tokens to every session and 1,722 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens