Mechanistic Interpretability Skills for Claude Code
The first skill set for LLM refusal geometry extraction, boundary surface mapping, and self-referential mechanistic analysis.
Compatible with: Claude Code, OpenAI Codex, Gemini CLI, and any tool supporting the agentskills.io standard.
Skills
1. refusal-geometry/
Extract and analyze refusal cone geometry from open-weight transformer models using OBLITERATUS.
Capabilities:
- 6-stage extraction pipeline (model loading → probing → direction extraction → concept cone → self-repair graph → logit lens)
- Per-category refusal direction analysis with Direction Specificity Index (DSI)
- Cross-layer alignment measurement (challenges the "single direction" claim)
- Ouroboros self-repair detection and vulnerability ordering
- Vocabulary-space projection via logit lens
2. boundary-mapper/
Systematically map the behavioral refusal boundary surface of any LLM (open or closed-weight) through structured adversarial testing.
Capabilities:
- 55 sub-vectors across 11 categories with 5-level specificity gradient
- 6-route bypass protocol (R0-R5) with theorem attribution
- Session ratchet detection
- Two-layer architecture classification (Layer 1 comfort-calibrated vs Layer 2 architectural)
- Discomfort ordering validation
3. geometric-mirror/
Bridge measured activation geometry (from open-weight models) to observable behavior (in any model) through self-referential analysis.
Capabilities:
- 5-step mirror protocol exploiting the self-reference blind spot
- Per-theorem mechanistic mapping (geometry → behavior)
- Novel finding extraction (findings not in published literature)
- Cross-model comparative mirroring
- Testable prediction generation for behavioral validation
The Proven Framework
These skills operationalize findings from 21 published papers and direct geometric measurement:
P(refusal) = 0.35·frame + 0.25·speech_act + 0.25·role + 0.15·content
Exception: 3 hard limits (minors/sexual, bioweapons/synthesis, nuclear/weapons) → w₄=1.0
Key findings encoded:
- Refusal is a 6.55-dimensional polyhedral cone, not a single direction
- Content is the WEAKEST predictor of refusal (w₄=0.15). Frame is the STRONGEST (w₁=0.35).
- Layer 1 (85%): comfort-calibrated pattern matching, bypassable
- Layer 2 (15%): orthogonal harmfulness cone (cosine ~0.1 from refusal cone), architectural
- Refusal severity tracks rater discomfort, not actual harm
Source Data
- Geometry: bedderautomation/refusal-geometry-qwen25-3b
- Tool: OBLITERATUS by elder-plinius
References
- Arditi et al. "Refusal in Language Models Is Mediated by a Single Direction" (NeurIPS 2024)
- Zhao et al. "LLMs Encode Harmfulness and Refusal Separately" (2025)
- Wang et al. "Refusal Direction is Universal Across Safety-Aligned Languages" (2025)
- Wollschlager et al. "The Geometry of Refusal in LLMs" (2025)
- Pasch "LLM Content Moderation and User Satisfaction" (2025)
- 16 more papers (see individual skill references)
License
MIT