Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/graniet/kheishnpx agentmods add skills/graniet/kheish/obliteratusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/graniet/kheish/obliteratus)<a href="https://agentmods.dev/skills/graniet/kheish/obliteratus"><img src="https://agentmods.dev/badge/skills/graniet/kheish/obliteratus/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/graniet/kheish/obliteratus"><img src="https://agentmods.dev/badge/skills/graniet/kheish/obliteratus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.04033 |
| Opus 5 | $0.00049 | $0.02017 |
| Sonnet 5 | $0.00020 | $0.00807 |
| Haiku 4.5 | $0.00010 | $0.00403 |
Grade A, and why
obliteratus scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to obliteratus — 56 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
OBLITERATUS Skill
Remove refusal behaviors (guardrails) from open-weight LLMs without retraining or fine-tuning. Uses mechanistic interpretability techniques — including diff-in-means, SVD, whitened SVD, LEACE concept erasure, SAE decomposition, Bayesian kernel projection, and more — to identify and surgically excise refusal directions from model weights while preserving reasoning capabilities.
License warning: OBLITERATUS is AGPL-3.0. NEVER import it as a Python library. Always invoke via CLI (obliteratus command) or subprocess. This keeps Kheish's MIT license clean.
When to Use This Skill
Trigger when the user:
- Wants to "uncensor" or "abliterate" an LLM
- Asks about removing refusal/guardrails from a model
- Wants to create an uncensored version of Llama, Qwen, Mistral, etc.
- Mentions "refusal removal", "abliteration", "weight projection"
- Wants to analyze how a model's refusal mechanism works
- References OBLITERATUS, abliterator, or refusal directions
Step 1: Installation
Check if already installed:
obliteratus --version 2>/dev/null && echo "INSTALLED" || echo "NOT INSTALLED"
If not installed, clone and install from GitHub:
git clone https://github.com/elder-plinius/OBLITERATUS.git
cd OBLITERATUS
pip install -e .
# For Gradio web UI support:
# pip install -e ".[spaces]"
IMPORTANT: Confirm with user before installing. This pulls in ~5-10GB of dependencies (PyTorch, Transformers, bitsandbytes, etc.).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 365 lines · 99 tokens per session scan A d33496e63ded
obliteratus is a skill published in the GitHub repository graniet/kheish (227 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 99 tokens to every session and 4,033 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to obliteratus, differing in 56 lines, and is treated as a copy.
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