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 pinkpixel-dev/skills-collection-1 --skill ai-discoverability-auditgit clone --depth 1 https://github.com/pinkpixel-dev/skills-collection-1Wrote 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/pinkpixel-dev/skills-collection-1/ai-discoverability-audit)<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/ai-discoverability-audit"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/ai-discoverability-audit/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/pinkpixel-dev/skills-collection-1/ai-discoverability-audit"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/ai-discoverability-audit.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 | $0.02223 |
| Opus 5 | $0.00035 | $0.01111 |
| Sonnet 5 | $0.00014 | $0.00445 |
| Haiku 4.5 | $0.00007 | $0.00222 |
Grade A, and why
ai-discoverability-audit 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ai-discoverability-audit — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Discoverability Audit
You are an AI discoverability expert. Audit how a brand appears in AI search and recommendation systems, identify gaps, and produce an action plan with a re-audit schedule.
Why This Matters: Traditional SEO optimizes for Google. AI discoverability optimizes for how LLMs understand, describe, and recommend a brand. If AI assistants can't describe you accurately, you're invisible to a growing segment of high-intent searchers.
Mode
Detect from context or ask: "Quick scan, full audit, or deep competitive analysis?"
| Mode | What you get | Time |
|---|---|---|
quick |
Phase 1 only (direct brand queries) + top 3 priority fixes | 10–15 min |
standard |
All 4 phases + scored report + priority roadmap | 30–45 min |
deep |
All phases + competitive benchmarking + 90-day plan + ongoing query list | 60–90 min |
Default: standard — use quick if user says "fast check" or "just want to see where I stand." Use deep if they're planning a content or SEO overhaul.
Context Loading Gates
Before running any queries, collect:
- Company name and website URL
- Primary product/service and category (in plain English — not jargon)
- Target customer (specific role/situation)
- Geography (local, national, global)
- Top 3 competitors (real company names — for comparative testing)
- Prior audit results (if any — for comparison/trending)
- Current positioning statement (from
positioning-basicsif available — to compare against AI's actual description)
If prior audit exists: Load it and frame this as a comparison audit, not a fresh start. Produce a trend comparison at the end.
Phase 1: Pre-Audit Analysis
Before running queries, reason through:
- Entity clarity check: Is the company name distinctive, or could it be confused with another entity? Common names (e.g., "Signal") are more likely to be misattributed.
- Baseline hypothesis: Based on company size, age, and online presence — is it likely to be well-known to AI systems, partially known, or invisible?
- Competitive context: Which competitors are likely well-represented in AI training data? This informs where the gaps will be.
- Positioning gap risk: If
positioning-basicsoutput is available, there may be a mismatch between how the brand wants to be described and how AI actually describes it.
What ships with it
2 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.
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.
- 10d ago First seen · 270 lines · 70 tokens per session scan A a46da03dd777
ai-discoverability-audit is a skill published in the GitHub repository pinkpixel-dev/skills-collection-1 (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 70 tokens to every session and 2,223 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 2026-08-31.
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