Lists multiple specific concrete actions: reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. These are clear, actionable steps in a defined workflow.

Clearly answers both 'what' (reads experiment plan, implements code, deploys to GPU, collects results) and 'when' (explicit 'Use when' clause with specific trigger phrases and a situational trigger). Both dimensions are well-covered.

Includes strong natural trigger terms in both English and Chinese: '实现实验', 'implement experiments', 'bridge', '从计划到跑实验', 'deploy the plan', plus the contextual trigger 'experiment plan ready to execute'. Good coverage of how users would naturally phrase this request.

Highly distinctive with a clear niche: it's specifically a bridge workflow (1.5) between idea discovery and auto review, targeting GPU deployment of experiment plans. The specific file reference (EXPERIMENT_PLAN.md) and workflow numbering make it very unlikely to conflict with other skills.

The skill is quite long (~250+ lines) with some sections that could be tightened (e.g., the Constants section explains each flag verbosely, the ASCII workflow diagram is helpful but adds bulk). However, most content is genuinely instructive and not explaining things Claude already knows—it's domain-specific workflow orchestration. Some redundancy exists between the checkpoint display, the results template, and the handoff template.

The skill provides concrete, executable guidance throughout: specific file paths to read, exact commands to run (/run-experiment, /experiment-queue, /monitor-experiment), detailed code review prompts with spawn_agent syntax, structured markdown templates for output, and explicit checklists for self-review. The experiment result tables and log formats are copy-paste ready.

The workflow is exceptionally well-sequenced across 6 phases with clear milestone ordering (sanity → baseline → main → ablation → polish). Validation checkpoints are explicit: Phase 2 has a self-review checklist, Phase 2.5 adds cross-model code review with blocking/non-blocking issue classification, Phase 3 is a dedicated sanity check with failure recovery (including rescue diagnosis), and Phase 4 has an AUTO_DEPLOY checkpoint. Feedback loops are present for error recovery throughout.

The skill references shared protocols (output-versioning.md, output-manifest.md, output-language.md) and other skills (/run-experiment, /experiment-queue, /ablation-planner, etc.) appropriately. However, no bundle files are provided to verify these references exist, and the skill itself is quite long—some sections like the detailed result templates and backend lifecycle rules could potentially be split into reference files. The composing section at the end is well-organized.