D
docsor1212/cite-holmes
Cite Holmes - deep research skill that interrogates its own citations
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
Client config (built from the install command this repo gives)
{
"mcpServers": {
"docsor1212-cite-holmes": {
"command": "npx",
"args": ["skills"]
}
}
}Parsed from the README we fetched on 2026-10-26; we have not executed it.
command/args come from the install command the repository states. When it is ambiguous, the project README wins.
VS Code (.vscode/mcp.json - the key is servers)
{
"servers": {
"docsor1212-cite-holmes": {
"command": "npx",
"args": ["skills"]
}
}
}Same server, but VS Code keeps it under <code>servers</code>.
Claude Code (command line)
claude mcp add --transport stdio docsor1212-cite-holmes -- npx skills
Claude Code does not read a config file; add the server with this command.
Key names differ by client: Clients & CLI
- Star count
- 0 ★ · docsor1212/cite-holmes · read from the GitHub search API · (checked 2026-11-13 20:00:50)
- Source
- read directly from its GitHub page · type: open-source project
- Harvest history
- first seen 2026-10-25 · last seen 2026-11-14 · seen 24× · listed by 1 sources
- Score
- score 103 · 1 sources · repo
- Install
npx skills
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
npx skills. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
We filed it under these tags: 开源项目. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
docsor1212/cite-holmes；first catalogued 2026-10-25；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.# Cite Holmes 🔍
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Deep research that interrogates its own sources.
Every AI research report you've ever read had a dirty secret: some of those polished references were probably fabricated. A Nature news analysis suggests tens of thousands of 2025 publications might include invalid AI-generated references. GPTZero scanned 4,841 NeurIPS 2025 submissions; as independently reported, at least 100 hallucinated citations were found across 51 accepted papers.
Cite Holmes is a deep-research skill with a badge and a magnifying glass: it researches like any deep-research agent — then arrests its own citations before you can cite them.
(Demo is real output: 8 references, 3 deliberately planted fabrications — a fake DOI, a dead URL, and a no-URL citation. All 3 were caught and excluded; the 5 real ones passed. Measured: 7.7 s for all 8 — 4.2 s of pure network checks, the rest is deliberate throttling.)
Reproduce it yourself — the planted-fakes file ships with the repo:
bash
python scripts/verifyrefs.py --refs examples/demorefs.json
## 30-second quickstart
bash
# MCP server (for agents — Claude Code / Codex / Cursor):
uvx --from "git+https://github.com/docsor1212/cite-holmes#subdirectory=mcp" cite-holmes-mcp
bash
python scripts/verifyrefs.py --refs refs.json --out report.md
# or verify straight from your reference manager (v1.9):
python scripts/verifyrefs.py --refs bibliography.bib --out report.md
# v3.7.0 context check — verify in-text citations inside a finished draft:
python scripts/verifyrefs.py --refs bibliography.bib --check-document paper.md --out report.md
China mirror (ModelScope 魔搭): — if you find this skill useful, a like there helps others find it.
Open report.md: CiteScore + pre-submission conclusion at the top,
per-reference verdicts below.
Medical work: add --profile medical. Writing a paper: add --export bibtex.
Institution/CI: add --mailto you@lab.edu (Crossref polite pool).
## What NOT to do (anti-patterns)
Don't feed semantic fields from the same model that wrote the draft —
the semantic cap trusts structured model judgments; self-review defeats it.
Don't treat verified as "the paper supports my claim" — verified means
the source exists at an authoritative tier and registry metadata matches the
claimed title/authors/journal/year. Whether the specific sentence is
supported is the semantic layer (agent judgment, or L4 cascade with a judge).
Don't batch >50 references per MCP call — the server rejects oversized
batches by design; split them.
Don't hand-edit mcp/verifyrefs.py — it is a sha-gated copy of
scripts/verifyrefs.py; edit the trunk and run mcp/syncengine.sh.
Don't cite from the report without reading needshumancheck flags —
partial/unreachable items carry explicit re-check instructions on purpose.
Don't skip --cn on lossy CN egress and then report unreachable as
"fake" — unreachable is not invalid; re-run with --cn or a proxy first.
## How it works
Five phases, two modes:
| Phase | What happens |
| CALIBRATE | Asks you 3–5 sharp questions first (scope, timeframe, audience) — prevents researching the wrong question |
| PLAN | Breaks your question into 3–7 sub-questions with a search budget |
| SEARCH | Diamond-shaped iterative search: broad → narrow → gap-filling, Chinese + English, source-tier pyramid |
Every AI research report you've ever read had a dirty secret: some of those polished references were probably fabricated. A Nature news analysis suggests tens of thousands of 2025 publications might include invalid AI-generated references. GPTZero scanned 4,841 NeurIPS 2025 submissions; as independently reported, at least 100 hallucinated citations were found across 51 accepted papers.
Cite Holmes is a deep-research skill with a badge and a magnifying glass: it researches like any deep-research agent — then arrests its own citations before you can cite them.
(Demo is real output: 8 references, 3 deliberately planted fabrications — a fake DOI, a dead URL, and a no-URL citation. All 3 were caught and excluded; the 5 real ones passed. Measured: 7.7 s for all 8 — 4.2 s of pure network checks, the rest is deliberate throttling.)
Reproduce it yourself — the planted-fakes file ships with the repo:
bash
python scripts/verifyrefs.py --refs examples/demorefs.json
## 30-second quickstart
bash
# MCP server (for agents — Claude Code / Codex / Cursor):
uvx --from "git+https://github.com/docsor1212/cite-holmes#subdirectory=mcp" cite-holmes-mcp
bash
python scripts/verifyrefs.py --refs refs.json --out report.md
# or verify straight from your reference manager (v1.9):
python scripts/verifyrefs.py --refs bibliography.bib --out report.md
# v3.7.0 context check — verify in-text citations inside a finished draft:
python scripts/verifyrefs.py --refs bibliography.bib --check-document paper.md --out report.md
China mirror (ModelScope 魔搭): — if you find this skill useful, a like there helps others find it.
Open report.md: CiteScore + pre-submission conclusion at the top,
per-reference verdicts below.
Medical work: add --profile medical. Writing a paper: add --export bibtex.
Institution/CI: add --mailto you@lab.edu (Crossref polite pool).
## What NOT to do (anti-patterns)
Don't feed semantic fields from the same model that wrote the draft —
the semantic cap trusts structured model judgments; self-review defeats it.
Don't treat verified as "the paper supports my claim" — verified means
the source exists at an authoritative tier and registry metadata matches the
claimed title/authors/journal/year. Whether the specific sentence is
supported is the semantic layer (agent judgment, or L4 cascade with a judge).
Don't batch >50 references per MCP call — the server rejects oversized
batches by design; split them.
Don't hand-edit mcp/verifyrefs.py — it is a sha-gated copy of
scripts/verifyrefs.py; edit the trunk and run mcp/syncengine.sh.
Don't cite from the report without reading needshumancheck flags —
partial/unreachable items carry explicit re-check instructions on purpose.
Don't skip --cn on lossy CN egress and then report unreachable as
"fake" — unreachable is not invalid; re-run with --cn or a proxy first.
## How it works
Five phases, two modes:
| Phase | What happens |
| CALIBRATE | Asks you 3–5 sharp questions first (scope, timeframe, audience) — prevents researching the wrong question |
| PLAN | Breaks your question into 3–7 sub-questions with a search budget |
| SEARCH | Diamond-shaped iterative search: broad → narrow → gap-filling, Chinese + English, source-tier pyramid |
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[](https://anclum.com/en/s/docsor1212-cite-holmes/)
- Source
- github · https://github.com/docsor1212/cite-holmes
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