Presents adaptive codebase challenge questions with multiple-choice and trace exercises. Use when testing contributor knowledge of the codebase.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Run Gauntlet Challenge
Present challenges from the knowledge base and evaluate answers.
## When NOT To Use
- The knowledge base does not exist yet (use `gauntlet:extract`)
- A staged path for a new contributor (use `gauntlet:onboard`)
## In-Loop Provider Setup
Before generating a challenge, register the in-loop variation
provider so we do not call out to the Anthropic API just to spawn
a sibling Claude (issue #464). Outside Claude Code this is a
no-op and the default Anthropic provider remains active.
```python
from gauntlet.providers.in_loop import (
register_in_loop_provider_if_inside_claude_code,
)
register_in_loop_provider_if_inside_claude_code()
```
## Steps
1. **Load state**: read `.gauntlet/knowledge.json` and developer
progress
2. **Check for pending challenge**: if
`.gauntlet/state/pending_challenge.json` exists, evaluate the
developer's most recent message as an answer before generating
a new one
3. **Generate challenge**: use adaptive weighting to select a
knowledge entry and challenge type
4. **Present challenge**: show the question with context
5. **Evaluate answer**: score the response (pass/partial/fail)
…
The line at the top of this page pins the label git:20260713.f98719b, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_4kopzbp2ttyotxty
GET https://markdownregistry.com/api/v1/resolve?ref=athola/claude-night-market/challenge
GET https://markdownregistry.com/api/v1/blob/408425855e02953dbba7b4ecfde74616167c79719d7914a63fb0a7eaf6c55183
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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