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mlava/agent-ready-api

Use the Agent Ready REST API to scan any public URL for AI agent-readability against the Vercel Agent Readability Spec, the llmstxt.org standard, and agent-protocol manifests (MCP server cards, A2A, agents.json, agent-permissions.json, UCP, x402, NLWeb). Activates for "scan this site for AI agent-readability", "run an Agent Ready scan on {URL}", "check the Agent Ready score for {URL}", "what's the agent-readability rating for {URL}", or any time the user wants a programmatic readability scan via HTTP. Picks this skill when the user does NOT have the Agent Ready MCP server installed — for MCP, use the `agent-ready-mcp` skill instead.

72

Quality

90%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

Overview
Quality
Evals
Security
Files

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-structured, highly actionable skill with clear step-by-step workflows, executable code examples, and thorough error handling. Its main weakness is moderate verbosity — the API key discovery section and inline reference tables add length that could be trimmed or offloaded. The security section is valuable but contributes to overall length, and the referenced bundle files (EXAMPLES.md, REFERENCE.md) cannot be verified.

Suggestions

Trim the API key discovery section (Step 1) — scenarios A–C could be condensed into a single bash snippet with comments, since Claude can handle the logic without exhaustive sub-sections.

Consider moving the response fields table and error recovery table to REFERENCE.md to reduce the main skill's token footprint while keeping the workflow lean.

DimensionReasoningScore

Conciseness

The skill is mostly efficient and avoids explaining concepts Claude already knows, but it's somewhat verbose in places — the API key discovery section (Step 1 A–D) is thorough but lengthy, and the response fields table could be more compact. The security section, while important, adds significant length.

2 / 3

Actionability

Excellent actionability throughout — every step includes copy-paste-ready curl commands, concrete bash scripts for polling, specific JSON response examples, and a clear error recovery table with exact status codes and fixes.

3 / 3

Workflow Clarity

The 5-step workflow is clearly sequenced with explicit validation: Step 1 has a decision tree for API key discovery, Step 3 includes a polling loop with status checks, Step 4 provides structured summarization guidance, and the error table provides clear recovery paths for each failure mode.

3 / 3

Progressive Disclosure

References to EXAMPLES.md and REFERENCE.md are well-signaled and one-level deep, but no bundle files were provided to verify they exist. The main SKILL.md includes substantial inline content (full error table, response fields table, security section) that could potentially be offloaded, though keeping them inline is defensible for a skill of this scope.

2 / 3

Total

10

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is an excellent skill description that clearly defines what the skill does (scans URLs for AI agent-readability via REST API against specific standards), when to use it (with multiple natural trigger phrases), and when NOT to use it (explicitly distinguishing from the MCP variant). It is specific, well-triggered, complete, and distinctive.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: scan public URLs, check against named standards (Vercel Agent Readability Spec, llmstxt.org, agent-protocol manifests including MCP server cards, A2A, agents.json, etc.), and explicitly mentions using the Agent Ready REST API.

3 / 3

Completeness

Clearly answers both 'what' (scan URLs for AI agent-readability against multiple standards via REST API) and 'when' (explicit activation phrases plus the disambiguation rule about when NOT to use it vs the MCP skill). The 'Activates for...' clause serves as an explicit trigger guidance.

3 / 3

Trigger Term Quality

Includes highly natural trigger phrases users would say: 'scan this site for AI agent-readability', 'run an Agent Ready scan on {URL}', 'check the Agent Ready score', 'agent-readability rating'. These are realistic user utterances with good variation coverage.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive with a clear niche (Agent Ready REST API scanning) and explicitly disambiguates from the related `agent-ready-mcp` skill by specifying when to use each. The specific standards and API mentioned make conflicts with other skills very unlikely.

3 / 3

Total

12

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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