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checkpoint-ambiguity-review — how to use checkpoint-ambiguity-review how to use checkpoint-ambiguity-review, checkpoint-ambiguity-review setup guide, what is checkpoint-ambiguity-review, checkpoint-ambiguity-review vs manual test review, checkpoint-ambiguity-review install for AI agents, automated test review with checkpoint-ambiguity-review

v1.0.0
GitHub

About this Skill

Perfect for Code Review Agents needing advanced test analysis and ambiguity detection capabilities. checkpoint-ambiguity-review is a skill that analyzes checkpoint specifications and tests to detect and resolve ambiguity issues.

Features

Collects inputs from problem name, checkpoint number, and file paths
Scans test files and spec paths, including `problems/{problem}/tests/test_checkpoint_{N}.py`
Reports tests with non-explicit interpretations and provides rationale and fixes
Supports inference of spec and test file paths if not provided
Works with Markdown files, such as `problems/{problem}/checkpoint_{N}.md`

# Core Topics

SprocketLab SprocketLab
[0]
[0]
Updated: 3/6/2026

Quality Score

Top 5%
39
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add SprocketLab/slop-code-bench/checkpoint-ambiguity-review

Agent Capability Analysis

The checkpoint-ambiguity-review MCP Server by SprocketLab is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use checkpoint-ambiguity-review, checkpoint-ambiguity-review setup guide, what is checkpoint-ambiguity-review.

Ideal Agent Persona

Perfect for Code Review Agents needing advanced test analysis and ambiguity detection capabilities.

Core Value

Empowers agents to review checkpoint specs and tests, detecting non-explicit interpretations and providing rationale and fixes, utilizing Markdown and Python file analysis.

Capabilities Granted for checkpoint-ambiguity-review MCP Server

Reviewing checkpoint tests for ambiguity and implicit assumptions
Generating reports on non-explicit interpretations with rationale and fixes
Automating test accuracy validation for checkpoint specs

! Prerequisites & Limits

  • Requires access to problem directories and files (e.g., .md and .py files)
  • Limited to reviewing checkpoint specs and tests in Markdown and Python formats
  • May require inference of spec and test file paths if not provided
Project
SKILL.md
2.3 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
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Checkpoint Ambiguity Review

Overview

Review a checkpoint's spec and tests to find tests that enforce a reasonable but non-explicit interpretation, and report those cases with rationale and fixes.

Workflow

1) Collect inputs

  • Problem name and checkpoint number (N).
  • Test file path(s) and checkpoint spec path (if not provided, infer):
    • Spec: problems/{problem}/checkpoint_{N}.md
    • Tests: problems/{problem}/tests/test_checkpoint_{N}.py
    • Also scan problems/{problem}/tests/conftest.py and problems/{problem}/tests/data/ if they influence expectations.
  • Optional: snapshot path for ambiguity verification.

2) Read the spec and tests

  • Extract explicit requirements from the spec.
  • Map each test assertion to a specific spec clause or an implied behavior.
  • Note any test assumptions that are not spelled out in the spec.

3) Flag ambiguous interpretations

Only report tests that enforce an interpretation that could reasonably differ given the spec wording. Do not report tests that are simply incorrect against explicit requirements.

Common ambiguity cues:

  • Output ordering when the spec does not mandate order.
  • Tie-breaking rules that are unstated.
  • Whitespace, casing, or formatting details not defined by the spec.
  • Rounding or precision requirements not defined.
  • Error handling for invalid inputs when not specified.
  • Boundary behavior (inclusive/exclusive) not stated.
  • Default values or optional fields not defined.
  • Determinism or randomness expectations not specified.
  • Multiple reasonable data structure representations (list vs set, map order).

4) Optional snapshot verification

If a snapshot is provided, run:

bash
1slop-code --quiet eval-snapshot {snapshot} -p {problem} -o /tmp/eval -c {N} -e configs/environments/docker-python3.12-uv.yaml --json

Use failures to corroborate ambiguity, not to invent it. A failing test is ambiguous only if the spec supports multiple reasonable interpretations.

5) Report format

Use the following structure for each ambiguous test:

## {test name} ({path}::{node_id})

**Why:** {spec language + alternate interpretation that could be valid}
**Fix:** {proposed test relaxation or spec clarification}

Keep entries concise and actionable. If no ambiguity is found, state that clearly (e.g., "No ambiguity issues found.").

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