mandela — Claude Code skill: what it does, how to install
Audits evaluations, metrics, and benchmarks for circular data leakage. Identifies whether external ground-truth enters independently.
npx skills add LilMGenius/paperthin --skill mandela -a claude-codeWhat mandela does
The skill checks validations, holdouts, and experiment setups against an 8-pattern leakage taxonomy. It breaks the workflow into model, scorer, designer, and dataset to answer one core question: does outside ground truth genuinely arrive on its own? It catches cases where components merely verify each other's assumptions.
Patterns checked include tautology, shared hallucinations, wrong null hypotheses, and shared-pool bias. The tool operates strictly read-only. It reports only the triggered patterns alongside independence fixes, without rewriting your experimental scripts.
What it helps with
- detect circular confirmation between model and scorer
- audit benchmark holdouts for training data contamination
- identify invalid null hypotheses in experiment designs
- isolate biased labeler pools in validation datasets
Example prompts for the agent
- Audit our new LLM evaluation setup for data leakage using mandela
- Run mandela against this A/B testing holdout design
- Check whether the scorer and model share any verification bias
How to install mandela
1Skills CLI (Vercel)
npx skills add LilMGenius/paperthin --skill mandela -a claude-code2Manually: put the files in place
~/.claude/skills/mandela/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/mandela/
1Skills CLI (Vercel)
npx skills add LilMGenius/paperthin --skill mandela -a codex2Manually: put the files in place
.agents/skills/mandela/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add LilMGenius/paperthin --skill mandela -a cursor2Manually: put the files in place
.cursor/skills/mandela/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install LilMGenius/paperthin mandela2Manually: put the files in place
.github/skills/mandela/SKILL.md1Skills CLI (Vercel)
npx skills add LilMGenius/paperthin --skill mandela -a gemini-cli2Platform CLI
gemini skills install https://github.com/LilMGenius/paperthin --path skills/depth/mandelaManually: put the files in place
.devin/skills/mandela/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add LilMGenius/paperthin --skill mandela -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about mandela
How to install mandela in Claude Code?
Run npx skills add LilMGenius/paperthin --skill mandela -a claude-code. It installs the skill configuration into ~/.claude/skills/mandela/SKILL.md.
What does the mandela skill do?
It walks an 8-pattern taxonomy to spot false independence in tests, metrics, and benchmarks. When a pattern fires, it outputs the specific leak name and an independence fix.
Does mandela modify experiment files?
No, it operates in a read-only mode. It pinpoints structural flaws in validation designs without altering your codebase or tests.
Source text
SKILL.md · MIT
--- name: mandela description: "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." --- 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 1. Identify the validation (eval / metric / experiment / holdout / "how we'll know"). Name its **components** — what plays model, scorer, designer, dataset. 2. Ask the **core question**: does external ground-truth enter *independently*? 3. 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. 4. Give the independent-ground-truth fix for each hit. ## The 8 leakage patterns 1. **Recall, not reason** — a memorized answer recited instead of one actually derived; the system already knows the result it is supposedly computing. 2. **Wrong null hypothesis** — an ablation that removes a surface label but not the underlying signal the system actually exploits, so the "control" still leaks. 3. **Shared hallucination** — two components verifying each other; circularity reported as a number. 4. **Tautology** — a scorer grading buckets it drew itself. 5. **Verifier = designer** — a private, unreproducible recipe in a holdout's clothes. 6. **Shared-pool bias** — train and holdout drawn from one labeler pool, so one bias enters both sides. 7. **Frame injection** — a question that hands the subject the hypothesis. 8. **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: 1. 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)? 2. Could a reader who didn't run the audit reach your verdict from the cited evidence alone — independent ground-truth? 3. The report names the root, not a laundry list.
© LilMGenius · MIT · LilMGenius/paperthin