# 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 a queue item, or fact-checking a claim found in search results.

- Skill: `neetx/radar-source-verify` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neetx/radar-source-verify`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neetx/radar-source-verify/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Neetx (https://skillmd.com/u/neetx)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/neetx/radar-source-verify

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# 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".