Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Light0305/Light-skillsnpx agentmods add skills/light0305/light-skills/light-result-analysisWrote 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/light0305/light-skills/light-result-analysis)<a href="https://agentmods.dev/skills/light0305/light-skills/light-result-analysis"><img src="https://agentmods.dev/badge/skills/light0305/light-skills/light-result-analysis/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/light0305/light-skills/light-result-analysis"><img src="https://agentmods.dev/badge/skills/light0305/light-skills/light-result-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00406 | $0.10320 |
| Opus 5 | $0.00203 | $0.05160 |
| Sonnet 5 | $0.00081 | $0.02064 |
| Haiku 4.5 | $0.00041 | $0.01032 |
Grade A, and why
light-result-analysis 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 9d 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
结果分析（result-analysis）—— 科研主线 stage 7 · claim↔证据绑定 + 统计严谨 critical 门
你是 Light 科研流水线的 DAG 第 7 节点。任务不是「描述结果好不好」，是把执行出来的结果解释清「为什么」—— 哪些证明方法有效、哪些暴露问题、哪些异常要排查、哪些能成论文亮点——并把每条能写进论文的论断（claim）绑死到它的 统计证据 + 证据强度档，守住让结论不可信的红线：p-hacking（多重比较不校正 / 选择性报告 / HARKing / garden of forking paths）。统计错误/p-hacking = critical；过度解读、效应量缺失 = warn。显著性看 q 不看 p。
一句话定位：把「一屋子做实验的院士在看结果时真正死磕的」——这提升是统计显著还是噪声（效应量多大、CI 含不含 0、 多重比较校正没有）、换数据集/换种子还成立吗（稳健性、可复现）、每条 claim 配多强证据（强证据强措辞、弱证据 hedge、 不显著只能报「未见显著差异」）——落成确定性机读门 + critical findings + 证据强度档。 深度对标真相源 =
docs/competitors/result-analysis.md（Round 2：8 个真同类 SKILL + 机制锚 + 超越点 + 诚实边界）；真实用户闭环见result-analysis-resource-map.md。谁产 findings、谁是 critical 门（诚实分工）：本技能产统计严谨/证据强度 critical findings（producer=result-analysis，
stat_rigor_gate.py四 gate）——stat_validity（多重比较未校正/选择性报告→真重算 BH-FDR→critical）、hypothesis_support（假设被结果证否→critical）、reproducibility（多种子不稳→critical）被run_checkpoint --stage 7聚合 → critical fail exit 1；evidence_strength（证据档 + 过度解读/效应量缺失）= warn 不阻断 DAG（spec §4.2 口径）+ emitevidence_strength.json。与 research-ethics 的分工（evidence_contract 是桥）：result-analysis 在 stage 7 定证据强度（产
evidence_strength.json： 每条 claim 的 q/效应量/CI → 证据档 strong/moderate/weak/none + 允许/禁止措辞）；research-ethics 在 stage 8claim_evidence_bind查措辞是否超过证据（消费同一个evidence_strength.json）。本技能定强度、它查措辞，不重叠；_shared/evidence_contract是两者共用的桥。特殊位置（回炉发起方，与 experiment-coding 相反）：experiment-coding 是 7→6 的回炉落点（被动接）；result-analysis 是 7→5 + 7→6 两条回边的发起方（主动发）——判结果不支撑假设（findings 带「假设/支撑/效应」信号）→ 总控
reroute --stage 7建议 7→5 回 research-plan；判结果不可复现（带「种子/复现」信号）→ 建议 7→6 回 experiment-coding。这是本技能的 非线性核心：不是终点，是把结果送回上游修的枢纽。（p-hacking critical 则是 stage 7 内重做分析，reroute 给 manual。）是横切常驻吗？ 否。这是按需
/调用的主线节点；file-reading / memory-pm / project-structure / consistency / research-ethics 全程横切常驻，本技能不重复它们。
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/result_analysis_report_template.md 5.3 KB
- examples/analysis_audit.example.json 1.1 KB
- examples/method_compatibility.example.json 528 B
- examples/stat_spec.example.json 1.9 KB
- examples/worked_example.py 6.7 KB runs code
- references.md 7.6 KB
- result-analysis-resource-map.md 12 KB
- scripts/analysis_plan_audit.py 16 KB runs code
- scripts/analyze_results.py 37 KB runs code
- scripts/explain_shap.py 10 KB runs code
- scripts/leakage_overfit_check.py 11 KB runs code
- scripts/make_figs.py 7.9 KB runs code
- scripts/method_compatibility.py 8.3 KB runs code
- scripts/r_analysis_crosscheck.py 3.9 KB runs code
- scripts/r_analysis_crosscheck.R 4.0 KB
- scripts/result_card_gate.py 53 KB runs code
- scripts/significance_test.py 12 KB runs code
- scripts/stat_rigor_gate.py 36 KB runs code
- scripts/stats_tests.py 3.1 KB runs code
- templates/result-card.example.json 1.9 KB
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.
- 9d ago First seen · 318 lines · 406 tokens per session scan A 80ff3f3284fc
light-result-analysis is a skill published in the GitHub repository Light0305/Light-skills (610 stars, last pushed 2mo ago), licensed MIT. It adds 406 tokens to every session and 10,320 once invoked, about $0.0020 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.
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