SKILLEMALL.ai
GitHub ↗ RU Sign in

AC obliteratus

OBLITERATUS: abliterate LLM refusals (diff-in-means).

NousResearch/hermes-agent Hermes author: NousResearch MIT 6 files body ≈ 3 723 tokens Open the sourcegithub.com↗ analyzed 26 h ago

OBLITERATUS: abliterate LLM refusals (diff-in-means).

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

AnalyzerAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "dependencies"

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Consistency. The Hermes dialect needs category and tags
    • 85Steps. 57 steps, 3 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 3723 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 19 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 53: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Structure: 33 headings
    • +3Step-by-step instructions: 57 items
    • +4Has examples (10 code blocks)
    • +4Reference files are cited in the instructions (1 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.