Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dkharlanau/agent-ready-web-profile --skill arwp-ai-search-contentgit clone --depth 1 https://github.com/dkharlanau/agent-ready-web-profileWrote 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-ai-search-content)<a href="https://agentmods.dev/skills/dkharlanau/agent-ready-web-profile/arwp-ai-search-content"><img src="https://agentmods.dev/badge/skills/dkharlanau/agent-ready-web-profile/arwp-ai-search-content/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-ai-search-content"><img src="https://agentmods.dev/badge/skills/dkharlanau/agent-ready-web-profile/arwp-ai-search-content.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 | $0.00086 | $0.01279 |
| Opus 5 | $0.00043 | $0.00639 |
| Sonnet 5 | $0.00017 | $0.00256 |
| Haiku 4.5 | $0.00009 | $0.00128 |
Grade A, and why
arwp-ai-search-content 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 3d ago.
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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARWP AI Search Content
Use this skill to improve content quality for humans first, while making the content easier for search and AI systems to retrieve, understand, cite, compare and route.
Core rule
Do not rewrite content into generic "AI SEO" prose. Google explicitly emphasizes useful, original, non-commodity content rather than special AI-only markup. Treat special AI files as supporting surfaces, not ranking shortcuts.
Before editing, use docs/ANTI-PATTERNS.md and knowledge/research/anti-patterns.json to review applicable negative examples. Record context and false-positive boundaries using templates/growth/anti-pattern-review.md; automated text matches cannot prove low quality.
Workflow
-
Identify the page's real job.
- What user problem does it solve?
- What query/task should it satisfy?
- What makes this page non-commodity: first-hand experience, data, benchmark, implementation detail, decision framework, original example, product evidence, or expert synthesis?
- If nothing is distinctive, add evidence/value before adding more words.
-
Make the answer retrievable.
- Put a direct answer near its relevant question when that serves the reader; keep meaningful narrative context. Do not enforce a chunk size, word count or FAQ quota.
- Use descriptive H2/H3 sections with stable IDs where the framework supports them.
- Keep important facts in visible page content.
- Use tables only when they genuinely clarify comparisons or dense data.
- Add lists/process steps only where the information is procedural.
-
Strengthen evidence and provenance.
- Cite primary sources for changing technical/platform claims.
- Add publication/update dates where meaningful.
- Identify author/organization when useful for accountability.
- Link benchmarks, datasets, receipts, changelogs or source repositories when claims depend on them.
- Distinguish measured facts, interpretation and prediction.
-
Build entity clarity.
- Use the canonical product/project/person/organization name consistently.
- State what the entity is and what category it belongs to in normal prose.
- Link to canonical About/Product/Comparison pages.
- Add appropriate Schema.org JSON-LD when the page maps cleanly to a supported type; do not invent types or stuff keywords.
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.
- 3d ago Changed · +14 lines 980985901df8
- 5d ago First seen · 84 lines · 86 tokens per session scan A 4775677e4249
arwp-ai-search-content is a skill published in the GitHub repository dkharlanau/agent-ready-web-profile (0 stars, last pushed yesterday), licensed Apache-2.0. It adds 86 tokens to every session and 1,279 once invoked, about $0.0004 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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