# Challenge

> Adversarial verification of research theses — cross-run/cross-thesis contradiction via the entity index, pre-mortem (Klarman/Kahneman: assume the loss already happened, reverse the path), inversion (Munger: invert, always invert), and wrong_if falsifiability review. ≈50-finding cap sorted by severity; content-derived finding IDs; incremental scoping with three backstops; lifecycle hooks at pillar-complete / thesis-complete / pre-reduction.

- Skill: `agentii-ai/challenge` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add agentii-ai/challenge`
- Raw SKILL.md: https://api.skillmd.com/api/skills/agentii-ai/challenge/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: agentii-ai (https://skillmd.com/u/agentii-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/agentii-ai/challenge

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# agentii.challenge

The buyer-side IC verb: *challenge the thesis*. Not `audit` — that name was taken
three times (audit-xls, quality-audit.yaml, G3) — challenge is the thing that
happens in an investment committee.

## Method bodies (the four angles)

1. **Cross-run contradiction** — the entity index (`reduce_journals.build_entity_index`):
   same `(entity, metric, period)` with different values. Same `retrieved_at` →
   true contradiction; different → suspected restatement. **The validator never
   auto-resolves** — candidates go to the IC agenda.
2. **Pre-mortem** — assume the loss already happened; reconstruct the path
   backwards. Which pillar failed first? What data would have to be wrong?
3. **Inversion** — what would make this thesis a sell? What would the bear's
   best case look like? What would falsify the conviction, not just the claim?
4. **`wrong_if` falsifiability** — is each falsifier mechanically checkable
   (metric + threshold + source)? A prose falsifier is not a falsifier (Q8-4).

## Economics (Q40/Q68)

- Findings: ≈50 cap, severity-sorted; overflow aggregates by category.
- Finding IDs are content-derived (`hash(entity+metric+period+gap_type)`) —
  unchanged re-runs yield byte-identical IDs (eval corpus comparability).
- Scope is **incremental** (artifacts whose pins/`as_of` changed since last run)
  with three backstops: every Nth converge / constitution MINOR-MAJOR / subscription
  change (new subscriptions create new comparison pairs — Q9).
- Lifecycle hooks: pillar-complete (cheap, early) / thesis-complete (final line) /
  pre-reduction (claims entering the portfolio are adversarially verified).
  **Unchallenged claims never enter the knowledge base (Q64).**

## Invocation

```bash
python3 scripts/challenge.py --nth-converge 10
```