transformer-lens-interpretability

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

Quality

89%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

86%Weight 40%Scale 1-5

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

The body is a strong, actionable reference with executable code, well-sequenced workflows, and clean one-level-deep file splitting verified against the actual bundle. Minor conciseness drag comes from promotional intro attribution and star counts, and workflows lack explicit feedback loops.

Suggestions

Trim the promotional/contextual padding in the intro ('de facto standard', author attribution, '2,900+ stars') — Claude does not need this to use the library.

Add a short validate/feedback step to the activation-patching and circuit-analysis workflows (e.g. sanity-check the metric sign or baseline logit diff before sweeping layers) to lift workflow clarity.

Add 'circuit analysis' and 'causal tracing' as trigger synonyms so users phrase the need naturally.

Dimension	Reasoning	Score
Conciseness	Mostly dense, code-and-table reference material that earns its tokens, but a few sections add promotional/contextual padding Claude doesn't need ('de facto standard library', 'Created by Neel Nanda and maintained by Bryce Meyer', '2,900+ stars').	4 / 5
Actionability	Fully executable, copy-paste-ready code across activation caching, patching, circuit analysis, induction-head detection, and SAE integration, with concrete API keys and shapes that cover the common cases.	5 / 5
Workflow Clarity	Three workflows are clearly sequenced with numbered in-code steps and end-of-workflow checklists, but they lack explicit validate-then-fix feedback loops (acceptable since these are non-destructive analysis tasks, so the destructive-operation cap does not apply).	4 / 5
Progressive Disclosure	The body is a well-organized overview that signals one-level-deep references to real files (references/README.md, api.md, tutorials.md) via a clear table, with bulk API detail pushed to those bundles.	5 / 5
	Total	18 / 20 Passed

Description

92%Weight 40%Scale 1-5

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 third-person, concise, and clearly answers both 'what' and 'when' with concrete, domain-appropriate trigger phrases. It is distinctive and well-targeted, with only minor synonym gaps in trigger terms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'inspect and manipulate transformer internals via HookPoints and activation caching', 'reverse-engineering model algorithms', 'studying attention patterns', 'activation patching' — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Explicitly states what it does ('Provides guidance... to inspect and manipulate transformer internals via HookPoints and activation caching') and when to use it ('Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural terms for the niche ('mechanistic interpretability', 'reverse-engineering model algorithms', 'attention patterns', 'activation patching') that a user would say, but a few common synonyms (e.g. 'circuit analysis', 'causal tracing') are absent.	4 / 5
Distinctiveness Conflict Risk	A clear niche (mechanistic interpretability via TransformerLens, HookPoints, activation caching) with distinct triggers and minimal overlap risk against other skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/transformer-lens-interpretability/SKILL.md
Commit: ddcaa2a

Reviewed: 11 days ago

Table of Contents

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