SkillsAdd

smithery/neversight

readiness

Evaluate repository readiness for AI agents. Analyzes 81 criteria across 8 pillars, assigns maturity level 1-5, generates visual report.

Installation

$ npx skills add smithery/neversight --skill readiness
View on skills.sh ↗ Website ↗

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

find-skills Same topic
vercel-labs/skills

Helps users discover and install agent skills when they ask questions like "how do I do X", "fi…

3.3M installs
agent-browser Same topic
vercel-labs/agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, includ…

810.4K installs
vercel-react-best-practices Same topic
vercel-labs/agent-skills 697.8K installs
web-design-guidelines Same topic
vercel-labs/agent-skills

Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "chec…

617.3K installs
microsoft-foundry Same topic
microsoft/azure-skills

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and …

576.5K installs
azure-prepare Same topic
microsoft/azure-skills

Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Te…

568.3K installs

Also in this package

Other skills from smithery/neversight · top by installs.

npx skills add smithery/neversight
zoom-meeting 0 ziwei-doushu 0 zimage-skill 0 zig 0 zhimeng-agent 0 zfc 0 zenbin 0 zeberg-salomon-rotator 0 zdev 0 youtube-transcript 0

Browse all from smithery/neversight

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsBash, Read, Glob, Grep, Write

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,785 B
  • docs SUMMARY.md 153 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Repository Readiness Assessment

Audit any repository to determine readiness for autonomous AI agent workflows. Produces a structured report scoring 81 distinct criteria.

Target: Use $ARGUMENTS if a GitHub URL is provided, otherwise analyze the current working directory.

Workflow

  1. Clone if needed — When $ARGUMENTS is a GitHub URL, clone to /tmp
  2. Discover context — Detect languages, locate source/test/config directories
  3. Identify apps — Count deployable units (monorepo services, libraries, etc.)
  4. Evaluate criteria — Score all 81 criteria from [CRITERIA.md](CRITERIA.md)
  5. Calculate level — Determine maturity level 1-5 based on thresholds
  6. Generate report — Output visual ASCII report per [OUTPUTFORMAT.md](OUTPUTFORMAT.md)
  7. Ask about HTML export — ALWAYS ask the user if they want the D3.js dashboard after the ASCII report; do not proceed until they answer

Boundary Rules

  • Stay within git repository root (where .git exists)
  • Skip .git, node_modules, dist, build, pycache
  • Never access paths outside the repository

Language Detection

Language Indicators
JS/TS package.json, tsconfig.json, .ts/.tsx/.js/.jsx
Python pyproject.toml, setup.py, requirements.txt, .py
Rust Cargo.toml, .rs
Go go.mod, .go
Java pom.xml, build.gradle, .java
Ruby Gemfile, .gemspec, .rb

Application Discovery

An application is a standalone deployable unit:

  • Independent build/deploy lifecycle
  • Serves users or systems directly
  • Could function as its own repository

Patterns:

  • Simple repos → 1 app (root)
  • Monorepos → count each deployable service
  • Libraries → 1 app (the library itself)

Scoring Rules

Repository Scope (43 criteria):

  • Evaluated once for entire repo
  • numerator: 1 (pass), 0 (fail), null (skipped)
  • denominator: always 1

Application Scope (38 criteria):

  • Evaluated per-app
  • numerator: count of passing apps
  • denominator: total apps (N)

Maturity Levels

Level Name Requirement
L1 Functional Baseline (all repos start here)
L2 Documented ≥80% of L1 criteria pass
L3 Standardized L2 + ≥80% of L2 criteria pass
L4 Optimized L3 + ≥80% of L3 criteria pass
L5 Autonomous L4 + ≥80% of L4 criteria pass

Evaluation Principles

  • Deterministic: Same repo → same output
  • Existence-based: Prefer file/config existence over semantic analysis
  • Conservative: Ambiguous evidence = fail
  • Concise rationales: Max 500 characters each

Additional Resources

  • [CRITERIA.md](CRITERIA.md) — Full list of 81 criteria with descriptions
  • [OUTPUTFORMAT.md](OUTPUTFORMAT.md) — ASCII visual report format with ANSI colors
  • [templates/report.html](templates/report.html) — D3.js HTML dashboard template
  • [examples/sample-output.md](examples/sample-output.md) — Example report output

HTML Report Generation (MANDATORY)

IMPORTANT: You MUST ask the user this question every single time after displaying the ASCII report. Do not skip this step and do not proceed until the user responds.

Ask user:

Would you like to generate an interactive HTML report with D3.js charts? [yes/no]

Wait for user response. If yes, use template from [templates/report.html](templates/report.html) and save as readiness-report.html. After generation, always offer to open the report.