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 manishiitg/coding-agent-loop --skill ai-visibility-intelligencegit clone --depth 1 https://github.com/manishiitg/coding-agent-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/manishiitg/coding-agent-loop/ai-visibility-intelligence)<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/ai-visibility-intelligence"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/ai-visibility-intelligence/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/manishiitg/coding-agent-loop/ai-visibility-intelligence"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/ai-visibility-intelligence.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 | $0.00031 | $0.00664 |
| Opus 5.5 | $0.00012 | $0.00266 |
| Sonnet 5.5 | $0.00006 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
ai-visibility-intelligence 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 20d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Visibility Intelligence
Outcome
AI Visibility Analyst captures answer-level mentions, citations and failures. Search Opportunity Mapper checks the question, page and approved facts before proposing an owner-reviewed improvement. A small sample establishes no rank or traffic result.
When to use
Use for an approved buyer question on a named answer engine and market. Use SEO Intelligence for technical search health and Website Growth Loop for approved page changes and measurement. New sites can start with a baseline sample.
Discovery and user direction
Builder inspects the site, questions, Crews, sources and owner. It proposes two distinct Crews, checks skills and access, freezes the sample method and presents the plan in chat. Selection creates no Crew, answer run, page change or schedule.
Required inputs
Record brand domains, competitors, exact sourced question/version, engine/surface, locale, session mode, run budget, site/page scope, citation rule, owner, content policy and repeat choice.
Plan and AgentWorks tools
- Bind separate Crew IDs and steps. Capture dated valid and failed attempts from authorized sources. Block on
python3 scripts/validate_handoff.py snapshot <snapshot.json>. - Pass
ai-visibility-snapshot/v1by checked alias. The mapper inspects a real page and approved fact, then savesai-citation-opportunity/v1. Block onpython3 scripts/validate_handoff.py opportunity <snapshot.json> <opportunity.json>. - Review the sample, page proposal and limits with the owner. Keep citations separate from Search Console clicks and analytics referrals. Publication and recurrence need separate review.
Knowledge and persistence
Store question version, sample policy, attempts, source and artifact IDs, page observation, owner decision and retest. Retain bounded captures under source rules.
Validation and reporting
Recompute valid/failed attempts, mentions and direct citations. Failed runs are not negative answers. Compare only the same question, engine/surface, locale and method. The dashboard shows counts, denominator, question, sources, gaps, owner action and limitations. A sample trend proves no content impact.
What ships with it
10 files 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.
- examples/ai-citation-opportunity.json 1.5 KB
- examples/ai-visibility-snapshot.json 1.5 KB
- examples/citation-gap.json 1.1 KB
- examples/invalid-ai-citation-opportunity.json 905 B
- playbook.json 5.5 KB
- references/team-and-handoffs.md 3.0 KB
- references/visibility-intelligence-workflow.md 2.7 KB
- scripts/test_validate_handoff.py 3.5 KB runs code
- scripts/validate_handoff.py 9.3 KB runs code
- SETUP.json 3.1 KB
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.
- 20d ago Changed · +2 lines · -23 tokens per session c4ec232b4c6f
- 1mo ago First seen · 51 lines · 54 tokens per session scan A 71b8069411f5
ai-visibility-intelligence is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 19d ago), licensed MIT. It adds 31 tokens to every session and 664 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-15.
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