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

86%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

72%

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

A highly actionable body with well-structured progressive disclosure, but it loses points on conciseness (promotional/biographical padding and duplication) and workflow clarity (batch operations lack validation checkpoints). Fixing these would raise it to the top band.

Suggestions

Remove non-actionable promotional context from the intro ('de facto standard', author names, star count) and de-duplicate the installation/basic-usage block that already appears in references/README.md.

Add an explicit validation checkpoint inside the activation patching workflow (e.g., assert the metric returns sane values, or verify hooks are reset with reset_hooks() before and after the batch loop) so the batch operation has a validate-fix-retry feedback loop.

Fix the dangling papers.md reference in references/README.md (the file does not exist in the bundle) to keep navigation trustworthy.

Dimension	Reasoning	Score
Conciseness	Mostly efficient executable code, but padded with non-actionable promotional/biographical context ('de facto standard library', 'Created by Neel Nanda and maintained by Bryce Meyer', '(2,900+ stars)') and duplicated install/basic-usage content that could be tightened; not lean enough for the top anchor.	2 / 3
Actionability	Provides multiple complete, executable Python examples with real TransformerLens API calls (run_with_cache, cache['resid_post', 5], the patching loop, SAELens integration) that are copy-paste ready, matching the top anchor.	3 / 3
Workflow Clarity	Workflows have numbered steps and checklists, but the activation patching workflow is a batch operation over every (layer, position) with no validation checkpoint or validate-fix-retry feedback loop inside it, which caps it at 2 per the batch-operation guideline.	2 / 3
Progressive Disclosure	A clear Reference Documentation table signals one-level-deep links to real files (README.md, api.md, tutorials.md) and the full API reference is appropriately pushed to references/; navigation is easy. (Note: references/README.md lists a non-existent papers.md, a minor bundle defect.)	3 / 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.

A strong, well-targeted description: it states concrete capabilities and gives an explicit 'Use when' trigger using natural domain vocabulary, with a clearly distinct niche that is unlikely to conflict with other skills.

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, or performing activation patching experiments'), matching the anchor for listing several specific concrete actions.	3 / 3
Completeness	Explicitly answers both what ('Provides guidance... to inspect and manipulate transformer internals') and when ('Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments') with an explicit trigger clause.	3 / 3
Trigger Term Quality	Uses natural domain vocabulary a mechanistic-interpretability user would actually say ('mechanistic interpretability', 'TransformerLens', 'activation patching', 'attention patterns'); these are precise domain terms rather than generic jargon, so it is not the level below.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (TransformerLens, HookPoints, activation patching) with distinct triggers unlikely to fire for unrelated skills; not generic enough to conflict as the level-2 anchor would.	3 / 3
	Total	12 / 12 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: Orchestra-Research/AI-Research-SKILLs
Path: 04-mechanistic-interpretability/transformer-lens/SKILL.md
Commit: 773a529

Reviewed: about 13 hours ago

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