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 varunk130/ai-gtm-skill-library --skill citation-radargit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/citation-radar)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/citation-radar"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/citation-radar/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/varunk130/ai-gtm-skill-library/citation-radar"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/citation-radar.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 | $0.00083 | $0.00796 |
| Opus 5.5 | $0.00033 | $0.00318 |
| Sonnet 5.5 | $0.00017 | $0.00159 |
| Haiku 4.5 | $0.00008 | $0.00080 |
Grade A, and why
citation-radar 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 26d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citation Radar (CLEAR Framework)
Buyers now ask an assistant before they open a search results page. Classic SEO audits measure rank; this skill measures whether a page is the kind of source an answer engine can lift a sentence from and credit. It works from the buyer's questions backward, not from keywords forward.
Core Principle
Answer engines cite claims, not pages. A page earns citations when it contains short, specific, attributable statements that directly answer a question someone actually asks.
The CLEAR Framework
| Letter | Dimension | What It Checks | Weight |
|---|---|---|---|
| C | Claim density | Specific, checkable statements per 100 words (numbers, named entities, dates) | 0.25 |
| L | Liftability | Answer sits in the first 40-60 words of the section; lists and tables for comparisons | 0.15 |
| E | Evidence | Named author, dated update, primary sources, schema markup | 0.20 |
| A | Answer match | Does an H2/H3 or first sentence answer the buyer question verbatim? | 0.25 |
| R | Rarity | Information not found on the top competing pages (original data, pricing, benchmarks) | 0.15 |
Score each dimension 0-10. CLEAR score = Σ(weight × score) × 10 → 0-100.
- 75+ Citable - protect and refresh quarterly
- 50-74 Fixable - one or two dimensions drag it down
- <50 Invisible - rewrite around a specific question
Process
- Question set - collect 20-40 real buyer questions (sales calls, support tickets, community threads); tag each by funnel stage
- Map - match each question to the page that should answer it; unmatched questions are content gaps
- Probe - ask 2-3 assistants each question; record who is cited and which sentence they quote
- Score - apply CLEAR to every mapped page
- Prioritize - rank fixes by
question volume × (75 - current score), capped at 0 for pages already citable - Rewrite brief - for each top fix, name the question, the missing claim, and the proof to add
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.
- 26d ago First seen · 61 lines · 83 tokens per session scan A 392249586889
citation-radar is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 12d ago), licensed MIT. It adds 83 tokens to every session and 796 once invoked, about $0.0003 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 2026-09-20.
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