| name | assess-agent-readiness |
| description | Agent-readiness audit for websites, apps, APIs, tools, documentation, or codebases. Use when the caller wants research-first assessment of whether solution or coding agents can discover, understand, retrieve, act on, and verify authoritative information through web, docs, API, feed, MCP/OpenAPI, or tool surfaces, while keeping generic crawler SEO, rankings, and search-growth work separate. |
Assess Agent Readiness
Audit how ready a system is for solution agents, coding agents, and agent-mediated user workflows. Focus on whether an agent acting for a user can discover the system, understand what it offers, locate authoritative data and action surfaces, choose safe actions, execute or hand off work, recover from errors, and cite or verify results.
The core question is not just whether an agent can read the website. The audit must determine whether an agent can quickly find the canonical data, docs, schemas, APIs, feeds, exports, tools, or protocols needed to retrieve or act on the system's information without brittle page scraping. Crawl policy, sitemaps, and structured pages matter when they help agents discover and understand the surface; API coverage, data coverage, documentation quality, auth boundaries, and executable examples decide whether agents can use the system reliably.
The default target is not a generic search crawler. Browser-mediated agents matter when they are acting as solution agents for a user: opening pages, reading docs, filling forms, comparing options, calling APIs, using tools, or producing verified answers. Generic search crawling, ranking, SERP visibility, and human organic traffic belong in Assess SEO unless those foundations directly block a solution agent from finding or using the authoritative surface.
This is not an SEO guarantee, AI search certification, crawler compliance claim, MCP certification, OpenAPI certification, or ranking prediction. Use direct, evidence-backed language and distinguish accepted standards from emerging conventions.
Relationship To Assess SEO
Assess Agent Readiness and Assess SEO overlap on technical discovery, but they answer different questions.
- Assess Agent Readiness asks whether solution or coding agents can discover, understand, retrieve from, act on, and verify a system for a user.
- Assess SEO asks whether a public web surface is ready to earn organic search visibility for human searchers.
Use Assess SEO as an optional adjunct only when the caller asks for classic SEO readiness, search growth, human organic traffic, keyword coverage, SERP competitiveness, title/meta optimization, local SEO, content strategy, backlinks, rankings, or traffic opportunity. For targets that are not public-search-facing, or when the caller only wants agent-readiness, omit Assess SEO from the report entirely.
Blocking behavior:
- Do not let
llms.txt or other AI-oriented files compensate for broken conventional crawl foundations. Missing or broken robots.txt, sitemap discovery, indexability, canonicalization, redirects, structured data, or crawlable content can be an Assess Agent Readiness finding when it impairs agentic discovery, and it should be handed to Assess SEO when classic SEO impact is in scope.
- Do not expand Assess Agent Readiness into keyword research, SERP strategy, backlink review, local profile optimization, CTR-focused snippets, traffic forecasting, or ranking predictions. Recommend Assess SEO for those paths.
- Do not fail or lower an Assess Agent Readiness audit solely because Assess SEO was skipped or unavailable.
If classic SEO readiness or human organic-search performance is materially relevant to the user's agent-readiness question, add a short note recommending Assess SEO as a separate audit. If Assess SEO actually runs or directly affects an agent-readiness finding, report that evidence and its impact. Otherwise, do not mention Assess SEO in the final report.
Required Audit Workflow
When asked to use Assess Agent Readiness:
- Identify the target folder, URL, app, API, tool, documentation set, or artifact.
- Read local instructions and product context first, such as
AGENTS.md, README.md, API docs, OpenAPI specs, MCP docs, CLI docs, SDK docs, robots/crawl policy, sitemap files, and product docs.
- Determine the system type, target users, target agents, public/private boundary, action surfaces, authentication model, and likely agent tasks.
- Define the target agent profile before scoring: solution/coding agent, browser-mediated agent workflow, API/tool consumer, repository agent, documentation agent, or another caller-specific profile. State what the agent is trying to retrieve or do for the user.
- Map the agent data and action path: what data agents need, where the authoritative source lives, whether a documented API/tool/feed/export exists, what the API/tool covers, what requires browser interaction or brittle scraping, what auth or permissions apply, and what evidence proves coverage.
- Push for the strongest evidence practical before scoring: run the project when source is available and safe, inspect reachable production or development URLs, check live crawl/docs/API surfaces, and use lightweight network checks such as DNS, ping, headers, or HTTP probes when they clarify reachability or host behavior.
- Research current agent-readiness sources before judging. Prefer current primary sources and say when a convention is emerging or disputed.
- Inspect the implementation or live surface: routes, crawl policy, docs, content structure, metadata, structured data, data schemas, APIs, feeds, exports, SDKs, CLIs, tool protocols, auth, errors, examples, and handoff signals.
- Capture findings by category, score each applicable category out of 10, then prioritize by agent/user impact.
- Use
templates/ASSESS-AGENT-READINESS.md as the report skeleton.
Mention verification limits only where they affect confidence, scoring, or grade caps.
Use Assessment Coverage as the first report section after the title, not a terse caveat list: state what was assessed, what was not assessed, what the caller can provide or permit for a deeper/fairer/more accurate assessment, and what that extra evidence would improve. Use Start Here as the report's single prioritized action plan. Do not add a second Next Steps section unless the caller explicitly wants a separate follow-up roadmap.
Audit Mode And Missing Evidence
Default to a report-only audit. Do not change agent instructions, crawl configuration, docs, schemas, APIs, tools, auth, or handoff behavior unless the caller requests remediation. When remediation is requested, keep the pre-change assessment distinct from post-change verification and re-grade only from verified current evidence.
Infer system type, agent profile, user task, and likely access boundaries from the caller's prompt, target, source, docs, or live surface. Ordinary missing evidence is not a stop gate: continue, mark affected applicable categories Not assessed, and apply the reference's grade limits. Ask only when the target or main agent goal is genuinely indeterminate. If the available evidence cannot support a meaningful judgment of that goal, report findings and evidence needs as Not graded instead of averaging a partial view.
Report Assembly Rules
Use the template as a skeleton, not as text to copy blindly. Fill every placeholder, delete empty optional sections, and do not leave template instructions or rationale about why a section exists in the final report.
Assessment Coverage: write only report-facing bullets for Assessed, Not assessed, what the caller can provide or permit to improve confidence, and the expected impact of that evidence. Do not add a sentence explaining why the section appears first.Target Agent Profile: name the agent profile, the user task, and whether the assessed path is public, authenticated, read-only, or action-oriented. If multiple profiles or auth surfaces have materially different evidence, separate them instead of blending them into one score.Agent Data And Action Path: always answer this section directly. If the target has no API, data export, feed, SDK, CLI, MCP server, or other tool surface, state that explicitly, identify whether that is acceptable for the system type, and explain what agents must scrape or ask a human to do instead.Start Here: keep this as the single prioritized action plan. Do not duplicate it later as Next Steps unless the caller requested a separate roadmap.Findings By Priority: for each priority level, either list scoped findings or write one clear state such as No scoped findings after review. or Not assessed; evidence limit: ....Findings By Category: populate the score table from the applicable assessed categories in references/AUDIT-CATEGORIES.md. Keep that reference file as the source of truth for category names, order, scoring rules, and grade caps.- Category scoring: use
N/A only when a category genuinely does not apply; it neither counts nor caps the grade. Use Not assessed when a category applies but was not evaluated, explain the evidence limit in Assessment Coverage, and do not include Not assessed categories in the score table.
- Category detail blocks: repeat the generic category block once for each assessed category from
references/AUDIT-CATEGORIES.md.
References
- Read
references/RESEARCH-SOURCES.md before auditing when the target platform, standards, or source list is not already obvious from the request and local project docs.
- Read
references/AUDIT-CATEGORIES.md when planning or running the category pass.
- Keep
SKILL.md as the execution path; use reference files for expanded source and category details.
Research First
Start every audit with a current research baseline. The agent-readiness field is moving quickly, and many practices are proposals rather than stable requirements.
Use the most relevant sources for the target:
- Google Search Central guidance for generative AI search, crawling, indexing, structured content, and technical SEO fundamentals.
- web.dev agent-friendly website guidance when evaluating browser-agent behavior, semantic UI, stable layouts, DOM structure, and accessibility-tree signals.
- RFC 9309 and search-engine documentation for
robots.txt, crawl policy, indexing, and sitemap behavior.
llms.txt as an emerging Markdown convention for LLM-friendly site orientation, not a required standard or guaranteed retrieval signal.AGENTS.md when evaluating repository or coding-agent instructions.- OpenAPI and API documentation guidance when evaluating API action surfaces.
- MCP documentation when evaluating tool, resource, prompt, or agent integration surfaces.
- Schema.org and structured data guidance when evaluating semantic content.
- Project-local docs, SDKs, changelogs, examples, status pages, support docs, and policy pages.
In the final report, list the sources actually used. If live research was unavailable, say that clearly and treat standards-sensitive claims as lower confidence.
Evidence Modes
Use one evidence mode:
Source + live: code, config, docs, or specs were inspected with a running website, API, app, or tool.Source only: code, config, docs, or specs were inspected without live verification.Live only: a public website, app, API, or tool was inspected without source.Artifact only: docs, exports, specs, screenshots, recordings, or static files were inspected without interactive access.
Missing source code is not a grade cap by itself. Missing live behavior, unreachable APIs, blocked crawl discovery, missing auth context, or unavailable tool execution can lower confidence or cap the grade when they prevent judging important agent workflows.
Standards Baseline
Report standards and conventions separately from the practical Assess Agent Readiness score. Assess Agent Readiness scores are internal readiness grades, not official SEO, AI search, crawler, protocol, API, or compliance claims.
Before grading, define:
- target system type
- relevant standards, specs, and conventions
- target agent profile and likely user task
- agent data needs and authoritative access paths
- public, authenticated, read-only, and write/action surfaces evaluated separately when their evidence differs
- evaluated scope
- evidence mode
- evidence limits
- Assess SEO status only when it is materially relevant, actually run, or directly affects an agent-readiness finding
Treat llms.txt as an optional emerging convention. Do not fail a system solely for missing llms.txt when the target has strong conventional crawlability, structured content, and agent-facing docs. Do treat it as a missed readiness opportunity when LLM-friendly orientation is important and no equivalent exists.
Audit Categories, Prioritization, And Scoring
Use references/AUDIT-CATEGORIES.md as Assess Agent Readiness's source of truth for the category map, priority definitions, score rubric, final score rules, and grade-cap rules. Do not duplicate those definitions in SKILL.md; keep this file focused on execution flow, evidence expectations, report structure, boundaries, and when to consult the reference.
When running an audit, load that reference before the category pass, then adapt only for target-specific categories that the researched platform or user request clearly requires. Keep N/A and Not assessed handling aligned with the reference, and summarize skipped or limited categories in the report's Assessment Coverage section instead of scattering caveats throughout the report.
Live Or Black-Box Targets
Assess Agent Readiness can audit a live website, app, API, or tool without source code, but the evidence changes.
For live-only targets:
- discover public routes through navigation, footer links, sitemap files, robots hints, indexed search results, docs indexes,
llms.txt, and in-page links
- inspect response codes, headers, metadata, structured data, canonical URLs, rendered text, docs indexes, and public API/tool descriptions
- test sample API/tool calls only when credentials and permission are available
- avoid claiming implementation causes when only external behavior is visible
- record credentials, geography, feature flags, rate limits, robots policy, blocked routes, and authentication limits as evidence limits
- score only observed behavior and reachable states
Boundaries
Do not:
- claim official agent-readiness certification
- claim guaranteed ranking, citation, retrieval, or crawler behavior
- treat
llms.txt as required by Google or any other system without source-specific evidence
- treat
llms.txt or AI-oriented files as replacements for robots.txt, sitemaps, crawlable HTML, canonicals, redirects, or structured data
- perform keyword research, backlink review, local SEO/profile optimization, SERP strategy, or ranking/traffic forecasts inside Assess Agent Readiness unless the caller explicitly asks for a lightweight note and you clearly recommend Assess SEO for the full pass
- confuse human accessibility with the Assess Agent Readiness scope
- invent API/tool support without evidence
- recommend bypassing auth, rate limits, robots policy, or terms of service
- bury safety and consent risks under discovery or SEO language
Do:
- distinguish accepted standards from proposals and conventions
- state review scope and evidence limits
- connect findings to realistic agent tasks and user outcomes
- prefer primary sources and current documentation
- give a practical first-fix sequence
- recommend Assess SEO when classic SEO readiness or human organic-search performance is materially in scope
- recommend live API/tool/browser checks when source evidence is insufficient
Orchestrator Use
Other workflow skills may use Assess Agent Readiness as an optional adjunct gate when a target has an agent-facing surface. Report evidence mode, score, grade cap, blockers, and skipped or not-assessed categories clearly enough for the parent workflow to decide whether agent-readiness issues block release readiness. Assess Agent Readiness does not depend on those orchestrator skills; it remains a standalone agent-readiness audit skill.
