Conciseness

Mostly code and tables that earn their place, but there is noticeable padding: '840+ stars' (time-sensitive vanity metric), the 'Supported Models' list, the 'Comparison with Other Tools' table, the 'Standard import' snippet, a long 'External Resources' links section, and a version-bound note ('pyvene v0.1.8+ supports LoRAs') that isn't quarantined in a deprecation/versions section. This sits between the 'noticeably verbose' (2) and 'efficient' (4) anchors.

Actionability

Four complete workflows with concrete, mostly copy-paste-ready code (IntervenableConfig construction, patching sweeps, save/load, steering) plus a targeted 'Common Issues & Solutions' section with WRONG/RIGHT examples. Not a 5 because Workflow 3's training loop relies on undefined 'dataloader' and 'criterion' placeholders and Workflow 1 ends in a bare '# 5. Visualize' comment rather than executable code.

Workflow Clarity

Each workflow is explicitly sequenced with numbered inline steps ('# 1. Define clean and corrupted inputs' ... '# 5. Visualize'), and Workflow 1 includes a checklist; the 'Common Issues' section serves as error-recovery guidance. Falls short of 5 because validation checkpoints are largely implicit — e.g., no step verifying an intervention actually fired or that logits changed as expected before sweeping all layers.

Progressive Disclosure

The body is an overview with four workflow walkthroughs, and detailed API/tutorial material is correctly split into references/ (README.md, api.md, tutorials.md — all real files, ~830 lines) linked via a clearly labeled table with per-file descriptions, one level deep. Not a 5 because reference-style content (the 'Key Classes Reference' table, 'Component Targets' enumeration, tool comparison) is inlined in SKILL.md where it belongs in references/api.md.

Specificity

The description names the domain ('causal interventions on PyTorch models using pyvene's declarative intervention framework') and lists several concrete actions — 'causal tracing, activation patching, interchange intervention training, or testing causal hypotheses'. It falls just short of the 5 anchor because the opening 'Provides guidance for performing' is slightly padded and a few capabilities (e.g., steering, saving/sharing interventions) are not mentioned.

Completeness

Both questions are explicitly answered: 'what' via 'Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework' and 'when' via an explicit 'Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses' clause with concrete trigger phrases. Not scoreable below 5 — this matches the top anchor including the explicit 'Use when...' construction.

Trigger Term Quality

Natural terms a user would say are present: 'causal tracing', 'activation patching', 'interchange intervention training', 'causal hypotheses', 'PyTorch', 'pyvene'. Not a 5 because common variations users might say — 'circuit analysis', 'steering', 'interpretability', 'intervention' alone — are missing.

Distinctiveness Conflict Risk

pyvene plus its specific technique names give it a clear niche with minimal conflict risk against unrelated skills. Not a 5 because related interpretability skills (TransformerLens/SAELens-style tasks) share the 'activation patching' / 'causal' vocabulary, so a small overlap risk remains.