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experiment-bridge

Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says "实现实验", "implement experiments", "bridge", "从计划到跑实验", "deploy the plan", or has an experiment plan ready to execute.

72

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a well-sequenced, highly actionable orchestration workflow with strong validation and feedback loops. Its main weaknesses are token efficiency and progressive disclosure: it is a long monolith with no bundle files to offload detail.

Suggestions

Tighten verbose prose — e.g., condense the Phase 3 sanity-failure/reimplement paragraph and reduce cross-references to sibling-skill docs that add length without new instruction.

Move self-contained blocks (the full spawn_agent code-review prompt, backend lifecycle rules, and result/log markdown templates) into reference files under references/ and link to them, reducing the inline footprint and enabling true progressive disclosure.

Add a short 'Quick start' overview near the top that points to the phase details, so the essential implement→deploy→collect flow is graspable before the full per-phase detail.

DimensionReasoningScore

Conciseness

The body is operational and largely avoids explaining concepts Claude already knows, but at ~330 lines it is not lean — prose such as the sanity-failure reimplement paragraph and cross-references to other skills' docs could be tightened.

2 / 3

Actionability

Provides concrete, executable commands (/run-experiment, /experiment-queue, git clone), a copy-paste spawn_agent review block, and explicit result/tracker markdown templates; placeholders like [experiment commands] are appropriate for a generator skill.

3 / 3

Workflow Clarity

Six sequenced phases with explicit validation checkpoints (sanity-first, self-review checklist, code review, AUTO_DEPLOY checkpoint) and clear error-recovery feedback loops ('READ the traceback... then fix', discard-and-reimplement, second-opinion rescue).

3 / 3

Progressive Disclosure

Sections are well-organized and the shared-protocol references are one-level-deep and clearly signaled, but no bundle files exist and a large amount of content (full review-prompt block, backend lifecycle rules, result templates) is inline in a single monolithic file rather than split into referenced files.

2 / 3

Total

10

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, bilingual in its triggers, and clearly answers both what the skill does and when to use it. It occupies a distinct orchestration niche with minimal conflict risk.

DimensionReasoningScore

Specificity

Lists four concrete actions — 'Reads EXPERIMENT_PLAN.md', 'implements experiment code', 'deploys to GPU', 'collects initial results' — matching the multi-action anchor; it is not the level below because the actions are comprehensive and specific rather than naming only a domain.

3 / 3

Completeness

Explicitly answers both 'what' (reads plan, implements code, deploys to GPU, collects results) and 'when' via an explicit 'Use when user says...' clause with concrete triggers.

3 / 3

Trigger Term Quality

Provides natural user phrasings across two languages — "implement experiments", "bridge", "deploy the plan", "实现实验", "从计划到跑实验" — giving good coverage of terms a user would actually say, not jargon.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche as the Workflow 1.5 implement+deploy+collect bridge with distinct triggers; unlikely to fire for unrelated skills, despite the slightly generic lone word 'bridge'.

3 / 3

Total

12

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 3 suspicious

Warning

Total

15

/

16

Passed

Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Reviewed

Table of Contents

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