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/radar-source-verify

Verify a signal or evidence candidate for the AI Radar ledger: open primary sources, pin exact dates and authors, check source independence, and classify the result as citable evidence or an unverified observation_queue item. Use whenever adding evidence to TRENDS.md, promoting

From plugin
ai-security-research-radar
411 skills
Install
$ npx -y skills add Neetx/ai-security-research-radar --skill radar-source-verify --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/radar-source-verify

Context preview

The summary Claude sees to decide when to auto-load this skill.

Verify a signal or evidence candidate for the AI Radar ledger: open primary sources, pin exact dates and authors, check source independence, and classify the result as citable evidence or an unverified observation_queue item. Use whenever adding evidence to TRENDS.md, promoting

SKILL.md

radar-source-verify.SKILL.md
name: radar-source-verify
description: |
  Verify a signal or evidence candidate for the AI Radar ledger: open primary sources, pin exact dates and authors, check source independence, and classify the result as citable evidence or an unverified observation_queue item. Use whenever adding evidence to TRENDS.md, promoting a queue item, or fact-checking a claim found in search results.

Verify a source for the ledger

Goal: every evidence line in `TRENDS.md` has (a) a URL that was actually opened this session, (b) a real date, (c) one line of context.

Procedure

1. **Find** candidates with the `tavily-search` skill (`--include-domains arxiv.org,github.com,huggingface.co` for primary sources, `--time-range` for recency). Search snippets are never citable on their own. 2. **Open** each candidate with the `tavily-extract` skill. Opened = citable. 3. **Pin dates** — by source type:

  • Date by the NEWS EVENT: the evidence-line date is the date of whatever

makes the item worth logging now — v1 for a new paper, the acceptance or latest-version date for an older paper that just got accepted/revised, the release date for software. Keep the other dates in the context line, e.g. "(v1 2025-11-12; accepted ACL 2026, v4 2026-04-16)" with the line dated 2026-04-16. This keeps the ledger's freshness machine-readable.

  • arXiv: `curl -sL 'https://export.arxiv.org/api/query?id_list=ID1,ID2'`

gives exact v1/latest dates and the full author list (the abs page often hides them from extraction). Use https, not http.

  • GitHub: cite the latest release tag and its date, not the access date.
  • Hugging Face org pages: "updated X days ago" → compute the estimate and

write "(date estimated from the org page)".

  • Undated pages: "(undated, accessed YYYY-MM-DD)".

4. **Investigate the content and lineage — never judge by the surface.** Do not accept or dismiss a signal on the first/oldest hit or a single date. Open the ACTUAL artifact the signal is about (not just the top search result), and trace the line: is there a newer version, a follow-up paper, a 2026 continuation? A research line can look "stale" only because your first hit is its oldest paper while a fresh one exists (e.g. a 2025 method with a 2026 follow-up). Judge freshness and relevance from the real, latest artifact, not from what the search surfaced first. 5. **Independence check** (for the ≥3-sources trend bar): sources must come from different orgs/author groups. Shared co-authors collapse two papers into one group — compare author lists before counting. 6. **Classify**:

  • Opened + dated + primary → evidence line:

`- YYYY-MM-DD — URL — one line of context`

  • Not opened, or secondary-only → `observation_queue` line marked

"unverified", stating exactly what is missing.

Red flags (queue or drop — never cite)

  • SEO content farms, model comparators/aggregators, market-wire press syndication

are never CITABLE — but they can POINT to a real primary artifact. Before discarding one, read what paper/repo/release it is actually about, verify that primary, and cite IT (never the SEO source). Don't let a clickbait wrapper hide a live underlying result.

  • Releases claimed by aggregators but absent from the vendor's official channel

(check the org's HF page or official blog index before believing them).

  • Vendor-reported adoption numbers: citable, but label them "vendor-reported".
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Ships withai-security-research-radar

Autonomous tracker of the offensive AI-security frontier — AI for offense and attacks against AI — for a security researcher; generated from TRENDS.md.

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