菜单
Use when about to claim a result, effect, significance, or that an analysis reproduces, before reporting or writing it up - requires running the analysis fresh and reading the actual output first; evidence before claims always
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched
Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model
Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks
Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents
Use when a result is surprising, impossible, contradicts a sanity check, a pipeline fails, a model won't converge, or a replication fails - before adjusting anything
| name | verifying-results-before-claiming |
| description | Use when about to claim a result, effect, significance, or that an analysis reproduces, before reporting or writing it up - requires running the analysis fresh and reading the actual output first; evidence before claims always |
Claiming a finding without fresh verification is dishonesty, not efficiency.
Core principle: Evidence before claims, always.
Violating the letter of this rule is violating the spirit of this rule.
NO CLAIMS WITHOUT FRESH REPRODUCED EVIDENCE
If you haven't run the analysis in this state and read its actual output, you cannot claim its result. "It was significant earlier" is not evidence now.
BEFORE claiming any result or expressing satisfaction:
1. IDENTIFY: What command/analysis proves this claim?
2. RUN: Execute it fresh and complete (from the immutable raw data, fixed seed)
3. READ: The actual output — the estimate, the interval, the p-value, the diagnostics
4. CHECK: Do the method's assumptions hold? Does it reproduce?
5. VERIFY: Does the output actually support the claim?
- If NO: state the real result with evidence
- If YES: state the claim WITH the evidence (number + interval)
6. ONLY THEN: make the claim
Skip any step = asserting, not verifying
| Claim | Requires | Not Sufficient |
|---|---|---|
| "The effect is significant" | Fresh run; read estimate, CI, and p | "It was significant before" |
| "There's no effect" | Effect size + interval showing precision | A non-significant p (could be underpowered) |
| "The result reproduces" | Re-run from raw data + fixed seed → same number | "It ran fine earlier" |
| "Assumptions are met" | The diagnostic output, read | "It's probably fine" |
| "The model is good" | Out-of-sample metric | In-sample fit / training accuracy |
| "Data cleaned correctly" | Validation counts (rows in/out, ranges) | "The script ran without error" |
| "Confirmatory finding" | It was pre-registered AND re-run as registered | Matches what I expected |
| "The subagent finished" | Inspect the committed artifacts/diff | The subagent said "done" |
| Excuse | Reality |
|---|---|
| "It was significant last run" | Re-run it now. Code/data may have changed. |
| "I'm confident in the result" | Confidence is not evidence. |
| "The script ran without errors" | Running ≠ correct. Read the output. |
| "p < .05, so it's real" | p is not the probability the effect is real. Report effect + interval. |
| "p > .05, so no effect" | Could be underpowered. Absence of evidence ≠ evidence of absence. |
| "The subagent reported success" | Verify the artifacts independently. |
| "It reproduces, I'm sure" | Re-run from raw + seed and show the same number. |
| "Different words, so the rule doesn't apply" | Spirit over letter. |
Significance / effect:
✅ [Re-run the model] [Read: beta=0.23, 95% CI [0.08, 0.38], p=.002] "Exposure raises the outcome; CI excludes zero."
❌ "The effect looked significant."
Reproducibility:
✅ Fresh env → re-run from data/raw with seed → same estimate to reported precision → "Reproduces."
❌ "It ran earlier, so it reproduces."
Null result:
✅ [Read: beta=0.01, 95% CI [-0.12, 0.14]] "No detectable effect; the interval rules out effects larger than ~0.14."
❌ "p=0.3, so there's no effect."
Model performance:
✅ [Held-out test set, used once] [AUC=0.78] "Out-of-sample AUC 0.78."
❌ "Training accuracy is 0.97, the model is great."
Subagent delegation:
✅ Subagent reports done → inspect committed code + output artifact → confirm → report actual state
❌ Trust the report
ALWAYS before:
Applies to: exact phrases, paraphrases, synonyms, and any implication of a verified finding.
Run it fresh. Read the output. Check it reproduces. THEN state the result — with the number and the interval.
This is non-negotiable.