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SprocketLab AI Skills

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浏览 SprocketLab 维护的已发布 AI 技能,比较用途与说明,并通过来源仓库了解安装方式。

作者统计

已收录技能

9

收录技能的仓库 Stars 合计

153

仓库 Forks

9

平均每个技能的仓库 Stars

17

同一仓库中的多个技能可能共享 Stars,合计包含重复仓库数据。

9 个技能

全部技能

GitHub

edge-cases

SprocketLab

Analyze checkpoint tests and suggest missing edge cases. Use after writing tests or when reviewing test coverage. Invoke with /edge-cases .

GitHub

edge-cases

SprocketLab

Analyze checkpoint tests and suggest missing edge cases. Use after writing tests or when reviewing test coverage. Invoke with /edge-cases .

GitHub

test-ambiguity-detector

SprocketLab

Analyze test cases against specifications to find ambiguous assumptions. Use when tests might be making assumptions not explicitly defined in the spec. Invoke with /test-ambiguity-detector .

GitHub

validate-run

SprocketLab

Validate all checkpoints from an agent run directory in parallel. Spawns test-validator agents for each checkpoint and summarizes results. Invoke with /validate-run [problem].

GitHub

run-tests

SprocketLab

Run problem tests using eval-snapshot instead of raw pytest. Use this to evaluate solutions against benchmark tests in Docker. Invoke with /run-tests .

GitHub

reclassify-tests

SprocketLab

Reclassify tests by adding @pytest.mark.functionality to tests not explicitly shown in the spec. Invoke with /reclassify-tests .

GitHub

audit-spec

SprocketLab

Audit a checkpoint specification for realism and design decision forcing. Reviews specs to remove hand-holding, hidden corner cases, and architectural giveaways. Invoke with /audit-spec .

GitHub

fix-solution

SprocketLab

Continually test and repair solutions to benchmark problems until they pass. Use when a solution has failing tests. Invoke with /fix-solution .

GitHub

checkpoint-ambiguity-review

SprocketLab

Review checkpoint specs and tests to identify tests that encode ambiguous interpretations rather than explicit requirements. Use when asked to check checkpoint_N.md against test_checkpoint_N.py, when auditing t...

默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。