mandela
Audit any eval, metric, experiment, or benchmark for leakage — does external ground-truth enter independently, or are the model, scorer, and designer just confirming a result no outside truth ever produced? Use before trusting any 'how we'll know it worked' — an A/B, a holdout, a score, a validation — and whenever a result feels too clean or self-confirming. Walks an 8-pattern leakage taxonomy and returns only the patterns that fire, each with an independence fix. Read-only.
How do I install this agent skill?
npx skills add https://github.com/lilmgenius/paperthin --skill mandelaIs this agent skill safe to install?
- Gen Agent Trust Hubpass
The skill provides a framework for auditing data leakage in experiments and benchmarks. It is purely instructional and contains no code, external dependencies, or sensitive operations.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
Audit a validation for leakage: does outside ground-truth actually enter, or is everyone confirming a result no one independently produced?
Goal
The name is the Mandela Effect — a whole population confidently remembers something that never independently happened; a leaky validation is the same shape. Walk the 8 patterns below. mandela checks one thing: whether a validation is independent, or whether the designer, model, and scorer are only confirming each other.
Workflow
- Identify the validation (eval / metric / experiment / holdout / "how we'll know"). Name its components — what plays model, scorer, designer, dataset.
- Ask the core question: does external ground-truth enter independently?
- Test the validation against all 8 patterns below (some apply only to certain components — a human subject, a scorer); report only the ones that fire, each by name.
- Give the independent-ground-truth fix for each hit.
The 8 leakage patterns
- Recall, not reason — a memorized answer recited instead of one actually derived; the system already knows the result it is supposedly computing.
- Wrong null hypothesis — an ablation that removes a surface label but not the underlying signal the system actually exploits, so the "control" still leaks.
- Shared hallucination — two components verifying each other; circularity reported as a number.
- Tautology — a scorer grading buckets it drew itself.
- Verifier = designer — a private, unreproducible recipe in a holdout's clothes.
- Shared-pool bias — train and holdout drawn from one labeler pool, so one bias enters both sides.
- Frame injection — a question that hands the subject the hypothesis.
- Demand characteristics — measured subjects who know they're being measured.
Rules
- Subtlety that bites twice: you can blind the output value and still leak the collection recipe.
- Read-only — name the leak and the independence fix; don't rewrite the experiment.
- For a high-stakes validation, you may add one independent fresh-context auditor (
N=1) handed only the validation design, not this session's reasoning, to re-run the 8-pattern taxonomy blind; the default remains same-session and read-only.
Verification
Turn mandela on this audit:
- Run patterns #3–#5 on yourself: are you a scorer grading buckets you drew (Tautology, #4)? is the verifier the designer (#5)? is your verdict a shared hallucination with the design's own claims (#3)?
- Could a reader who didn't run the audit reach your verdict from the cited evidence alone — independent ground-truth?
- The report names the root, not a laundry list.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/lilmgenius/paperthin/mandela">View mandela on skillZs</a>