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 forsvn-labs/meta-skills --skill optimize-searchgit clone --depth 1 https://github.com/forsvn-labs/meta-skillsWrote 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/forsvn-labs/meta-skills/optimize-search)<a href="https://agentmods.dev/skills/forsvn-labs/meta-skills/optimize-search"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/optimize-search/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/forsvn-labs/meta-skills/optimize-search"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/optimize-search.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 | $0.00070 | $0.00550 |
| Opus 5 | $0.00035 | $0.00275 |
| Sonnet 5 | $0.00014 | $0.00110 |
| Haiku 4.5 | $0.00007 | $0.00055 |
Grade A, and why
optimize-search 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 12d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize search and answer visibility
Diagnose before prescribing.
Find the highest-leverage constraint before prescribing content.
Use the correct order
- Confirm the page can be crawled, rendered, indexed, and canonicalized.
- Confirm the query or question matches the audience and page purpose.
- Confirm the answer, entity, and product claim are explicit, supported, and extractable.
- Confirm internal links, authority, and evidence support selection.
- Improve retrieval and citation structure only after the foundations hold.
Inspect source, rendered output, metadata, robots directives, sitemap inclusion, canonicals, redirects, status codes, structured data, internal links, performance, and template duplication when accessible. Do not infer implementation defects from a screenshot.
Keep evidence streams separate
Do not merge classic rankings, impressions, click-through, Google AI surfaces, answer-engine citations, and referral traffic into one score.
For volatile observations, record:
- provider, model, or search surface;
- exact query;
- location, language, device, or account context when relevant;
- run time and observation window;
- repeated-run agreement for stochastic answers.
Treat an unavailable provider as missing data, never zero. A competitor citation remains evidence about the answer set when the subject product is absent. Check releases, migrations, seasonality, demand shifts, SERP layout, and competitor changes before attributing movement.
Diagnose
Classify the primary constraint:
- technical eligibility;
- relevance or intent mismatch;
- weak or unsupported answer;
- insufficient authority or proof;
- answer not extractable;
- eligible but not selected;
- measurement unavailable or inconclusive.
Separate observation, inference, and assumption. Never promise rankings, citations, traffic, or a specific indexing timeline.
Deliver
Return:
- concise diagnosis with evidence and confidence;
- prioritized corrections by impact, confidence, and effort;
- exact page or technical changes for the first correction;
- query-to-page map when multiple pages compete;
- measurement protocol and next observation;
- risks, dependencies, and claims requiring verification.
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.
- 12d ago First seen · 74 lines · 70 tokens per session scan A 61a2ce05a5db
optimize-search is a skill published in the GitHub repository forsvn-labs/meta-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 550 once invoked, about $0.0003 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 2026-08-30.
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