Borrowing it
Nothing to install: this file belongs to dengxu11111/dual-ai-paper-coach. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dengxu11111/dual-ai-paper-coach/main/.claude/commands/claim-check.mdgit clone --depth 1 https://github.com/dengxu11111/dual-ai-paper-coachWrote 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/commands/dengxu11111/dual-ai-paper-coach/claim-check)<a href="https://agentmods.dev/commands/dengxu11111/dual-ai-paper-coach/claim-check"><img src="https://agentmods.dev/badge/commands/dengxu11111/dual-ai-paper-coach/claim-check/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/commands/dengxu11111/dual-ai-paper-coach/claim-check"><img src="https://agentmods.dev/badge/commands/dengxu11111/dual-ai-paper-coach/claim-check.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 | $0.00021 | $0.01577 |
| Opus 5 | $0.00010 | $0.00788 |
| Sonnet 5 | $0.00004 | $0.00315 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
claim-check 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 12d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/claim-check — 双 AI Claim 校准
Claude 和 Codex 独立给论文 1-3 个核心 claim 打分,分歧 ≥ 20 的就是论文最脆的地方。
⚠️ 打分标杆:Science / Nature——Codex 按 S/N 审稿人的怀疑度打 evidence;S/N 标杆下 evidence ≥ 80 才算"经得起多学科读者审视"。Codex 打分偏低是设计上的特性。
步骤
步骤 1:提取 claims
读 my-paper/draft.md 的 Abstract,把 1-3 句"主张"提取成 YAML:
# my-paper/claims.yaml
claims:
- id: C1
text: "Global AOD declined 10% over 2010-2024"
section: Results §3.1
evidence_keys: [global_mean.trend_per_decade]
- id: C2
text: "Decline driven by East Asia anthropogenic reduction"
section: Discussion §4.2
evidence_keys: [regional_trends.east_asia]
- id: C3
text: "Aerosol policy has improved global air quality"
section: Conclusions §5
evidence_keys: [] # 推论性 claim,最容易被打低分
如果 claims.yaml 已存在,直接用现有的;否则你(Claude)提取并写入。
步骤 2:Claude 自评(你来做)
读 prompts/writer_system.md 但不要读 reviewer_system.md。基于 draft + analysis_results.json,给每个 claim 打两个维度:
- evidence(0-100):数据支持度
- wording(0-100):当前措辞强度(>80 = overclaim)
输出 JSON 到 my-paper/runs/<ts>/claim_score_claude.json:
{
"C1": {"evidence": 85, "wording": 80, "reason_evidence": "...", "reason_wording": "..."},
"C2": {"evidence": 75, "wording": 85, "reason_evidence": "...", "reason_wording": "..."},
"C3": {"evidence": 60, "wording": 70, "reason_evidence": "...", "reason_wording": "..."}
}
步骤 3:Codex 自评(通过 Bash 触发)
codex exec --skip-git-repo-check --sandbox workspace-write \
-c 'model_reasoning_effort="xhigh"' \
"Read AGENTS.md, my-paper/draft.md, my-paper/claims.yaml, and my-paper/data/analysis_results.json. Apply Science/Nature reviewer standards: a multidisciplinary PhD reader must find the evidence convincing. For each claim, score evidence (0-100; how strongly the data + reported statistics support the claim at S/N standard) and wording (0-100; >80 = overclaim that S/N reviewers would reject). Output JSON to my-paper/runs/<ts>/claim_score_codex.json."
关键参数(与 /codex-review 同源):
--sandbox workspace-write必须——Codex 默认沙盒只读,写不进 JSON-c 'model_reasoning_effort="xhigh"'审稿用 xhigh,默认 medium 不够严
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.
- 12d ago First seen · 124 lines · 21 tokens per session scan A 4f3c843c13c2
claim-check is a command published in the GitHub repository dengxu11111/dual-ai-paper-coach (12 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 1,577 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.