Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Presents adaptive codebase challenge questions with multiple-choice and trace exercises. Use when testing contributor knowledge of the codebase.
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See how to installReview the instructions the agent will receive before you install.
Present challenges from the knowledge base and evaluate answers.
gauntlet:extract)gauntlet:onboard)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.
from gauntlet.providers.in_loop import (
register_in_loop_provider_if_inside_claude_code,
)
register_in_loop_provider_if_inside_claude_code()
Load state: read .gauntlet/knowledge.json and developer
progress
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
Generate challenge: use adaptive weighting to select a knowledge entry and challenge type
Present challenge: show the question with context
Evaluate answer: score the response (pass/partial/fail)
Record result: update developer progress and streak
On pass: write pass token if from pre-commit gate. Show next challenge if in session.
On fail: show correct answer with explanation. Present a new challenge.
| Result | Score | Streak |
|---|---|---|
| Pass | 1.0 | +1 |
| Partial | 0.5 | reset |
| Fail | 0.0 | reset |
.gauntlet/knowledge.json exists and is readable before a
challenge is generated; if missing, the skill surfaces the error
and suggests running gauntlet:extract.gauntlet/state/pending_challenge.json is evaluated before
generating a new challenge when it exists; the file is removed
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
The English summary is being prepared. Showing the original description.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
The English summary is being prepared. Showing the original description.