found-by-ai
Measure whether AI engines actually recommend a business when buyers ask. Runs the free live scan at areyoufoundbyai.com (no…
Use when a client audit, GEO/AI-visibility snapshot, or remediation re-scan needs the Cloudflare agent-readiness score from isitagentready.com, e.g. Theo client audits, "is the site agent-ready", markdown negotiation / MCP / llms.txt / Content-Signal checks, or tracking score
Claude Code
$ npx -y skills add techhorizonlabs/thl-open --skill agent-readiness-scan --agent claude-code
Codex
$ npx skills add techhorizonlabs/thl-open --skill agent-readiness-scan -a codex -y
Cursor
$ npx skills add techhorizonlabs/thl-open --skill agent-readiness-scan -a cursor -y
Gemini CLI
$ npx skills add techhorizonlabs/thl-open --skill agent-readiness-scan -a gemini-cli -y
GitHub Copilot
$ npx skills add techhorizonlabs/thl-open --skill agent-readiness-scan -a github-copilot -y
OpenCode
$ npx skills add techhorizonlabs/thl-open --skill agent-readiness-scan -a opencode -y
Windsurf
$ npx skills add techhorizonlabs/thl-open --skill agent-readiness-scan -a windsurf -y
name: agent-readiness-scan description: Use when a client audit, GEO/AI-visibility snapshot, or remediation re-scan needs the Cloudflare agent-readiness score from isitagentready.com, e.g. Theo client audits, "is the site agent-ready", markdown negotiation / MCP / llms.txt / Content-Signal checks, or tracking score deltas after Tier 0/1 fixes. metadata: version: 1.0.0 origin: extracted from production GEO-audit + agent-readiness remediation sessions
Produce the official Cloudflare agent-readiness result for a domain as audit-grade artifacts: fixed-schema CSV + raw evidence + the 0-100 score.
1. **Two sources, both required:**
2. **Don't freeze the checklist.** The scanner evolves (new checks appear). Emit whatever `.checks` returns, mapped through the fixed CSV schema; never hand-author check rows. 3. **Scored vs supplementary.** llms.txt, llms-full.txt, security.txt are NOT scored by Cloudflare but Theo audits track them. They go in `Supplementary (not scored)` rows from curl probes, never mixed into scored categories. 4. **Commerce is informational** unless `isCommerce` is true: one `NOT CHECKED` row, "does not affect score".
scripts/run_scan.sh https://<domain> <outdir>/raw-data
Then get the official score via Playwright (see Critical fact 1 for the exact method). Save the page snapshot to `raw-data/isitagentready_<client-slug>_snapshot.txt` (client slug, e.g. `acme`, matching your audit config slug). Then:
python3 scripts/scan_to_csv.py \ <outdir>/raw-data/iar_scan.json --score <N> --out <outdir>/csv-base-data/agent_readiness_checks.csv
(`csv-base-data/` is the Theo full-pack convention; scoped snapshots have used plain `csv/`; either is fine, pass `--out` explicitly.)
`category,check,result,detail,source`
Headline format: `Agent readiness (Cloudflare isitagentready.com) | **N/100, Level L "Name"** (p pass, f fails)`. For remediation, lift `nextLevel.requirements[].prompt` verbatim (they're copy-paste fix prompts with spec URLs); the common Tier 0/1 fix pattern is a markdown negotiation map, robots Content-Signal, link headers, llms-full.txt, and security.txt.
| Mistake | Fix | |---|---| | Reporting only Level, no 0-100 | UI aria-label is the only score source; the Playwright step is not optional | | Inventing/renaming categories ("Protocol Discovery") | Use the mapping in scan_to_csv.py; `discovery` → `API Auth MCP & Skill Discovery` | | WebFetch on isitagentready.com/<host> | JS app: returns shell, no results. API or Playwright only | | curl-only assessment without the official scan | curl corroborates; the scan JSON is the authority for scored checks | | Mixing llms.txt into scored categories | Supplementary, not scored |
Turn a client website into an AI-readiness audit, a ranked fix plan and a branded report. Open agent skills and reporting tools from Tech Horizon Labs. Start in Claude Code, or use a compatible agent that reads SKILL.md files.
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