The description names a domain (experiments/assumption testing) and mentions some actions (design, run, learn, recording results, propagating learnings), but the actions are fairly high-level and lack concrete specifics about what formats, tools, or outputs are involved.

The description covers 'what' (design/run/learn from experiments) but has no explicit 'Use when...' clause or equivalent trigger guidance, which per the rubric should cap completeness at 2. Additionally, the 'when' is entirely missing — not even implied clearly — so it scores a 1.

Terms like 'experiments', 'assumptions', 'opportunity space' are somewhat relevant but lean toward product management jargon. Missing common natural variations users might say like 'hypothesis testing', 'validate idea', 'A/B test', 'prototype test', or 'user research'.

The experiment lifecycle focus provides some distinctiveness, but terms like 'riskiest assumptions' and 'opportunity space' are broad enough to overlap with strategy, product discovery, or research skills. Without explicit trigger boundaries, conflict risk remains moderate.

The content is mostly efficient but includes some unnecessary philosophical framing ('You are the empirical engine of the discovery system') and explanatory prose that Claude doesn't need. The 'Your Stance' section, while useful, could be tighter. The outcome propagation and transitions sections are well-structured but slightly verbose.

The skill provides concrete guidance on success criteria and pre-commitment with good examples ('7 of 10 users complete the task without asking for help'), and clear instructions for outcome propagation. However, it lacks executable code/commands and relies heavily on references for the actual how-to (design, lifecycle tracking). The skill itself is more directional than copy-paste actionable.

The experiment lifecycle is clearly sequenced: design with success criteria → pre-commit actions → run → record results → update assumption → review parent ideas → check shared assumptions → suggest next actions. The outcome propagation section provides an explicit multi-step process with validation-like checkpoints (review impact, check shared assumptions). Transitions to other skills are clearly defined with trigger conditions.

The skill provides a clear overview with well-signaled one-level-deep references: design principles in references/design-experiment.md, lifecycle tracking in references/experiment-lifecycle.md, schemas in experiment-record.md and assumption.md, and the artifacts skill for writing guidance. Content is appropriately split between the overview and referenced files.