Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/dkharlanau/agent-ready-web-profilenpx agentmods add skills/dkharlanau/agent-ready-web-profile/arwp-growth-loopWrote 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.
[](https://agentmods.dev/skills/dkharlanau/agent-ready-web-profile/arwp-growth-loop)<a href="https://agentmods.dev/skills/dkharlanau/agent-ready-web-profile/arwp-growth-loop"><img src="https://agentmods.dev/badge/skills/dkharlanau/agent-ready-web-profile/arwp-growth-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dkharlanau/agent-ready-web-profile/arwp-growth-loop"><img src="https://agentmods.dev/badge/skills/dkharlanau/agent-ready-web-profile/arwp-growth-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00100 | $0.03954 |
| Opus 5 | $0.00050 | $0.01977 |
| Sonnet 5 | $0.00020 | $0.00791 |
| Haiku 4.5 | $0.00010 | $0.00395 |
Grade A, and why
arwp-growth-loop 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARWP Growth Loop
Use this skill when the outcome is not merely “make the site agent-ready” but “improve the site's chances of being discovered, selected, cited or recommended while keeping the work evidence-backed and measurable.”
Product loop
research → classify → technical preflight → internal discovery/demand baseline → hypothesis → implement → verify implementation scope → measure → keep/revert/revise
Do not collapse this into a generic SEO checklist.
Workflow
- Research before adding a tactic. Start with:
node bin/arwp-trends.mjs list --since=90 --exclude-retired
node bin/arwp-hypotheses.mjs list --vertical=general
When network access exists, review current primary sources for the target surfaces. Prefer official platform documentation and specifications. Classify a mechanism as platform requirement, platform guidance, platform feature, platform measurement, or project experiment. Newness alone is not evidence.
For page-level structured data, identity, canonicalization, authorship, events, datasets, localization or terminology, load registry/page-semantics-profiles.json (or the published recommendations/page-semantics.json). Treat it as an implementation-routing profile, not as a ranking hypothesis.
- Establish the site baseline and run Technical Integrity. Inspect framework, deployment, public root, routes, content architecture, metadata, sitemap/robots, structured data, images/video, crawler policy, existing agent surfaces and owner-side metrics where available. Then run both:
node bin/arwp.mjs technical-integrity https://example.com/ --max-pages=20 --max-link-targets=24 --json
node bin/arwp-growth.mjs https://example.com --vertical=<vertical> --json
Technical Integrity is the bounded preflight for problems that can already be checked before Search outcome windows mature. It evaluates provider/source-backed technical blockers and conservative review heuristics including:
- robots.txt fetch semantics and path-specific Googlebot access on sampled priority URLs;
- known HTTP/noindex failures;
- Google AI snippet restrictions;
- canonical integrity on HTML pages only;
- bounded retrieval-footprint outliers without relabeling audit limits as Search failures;
- soft-404 suspicion;
- crawlable internal-link markup plus a capped health probe of important same-origin link targets;
- raw textual availability and JS-shell risk;
- hreflang reciprocity;
- Bing preview/grounding controls;
- near-duplicate priority content;
- OAI-SearchBot policy separately from GPTBot training policy.
What ships with it
1 file 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.
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.
- today Changed · +128 lines 7b90111b0afd
- yesterday Changed · +4 lines e8b4e3adfdc4
- 3d ago First seen · 106 lines · 100 tokens per session scan A ffe987fe2543
arwp-growth-loop is a skill published in the GitHub repository dkharlanau/agent-ready-web-profile (0 stars, last pushed today), licensed Apache-2.0. It adds 100 tokens to every session and 3,954 once invoked, about $0.0005 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 2026-09-07.
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