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AB llama-cpp

llama.cpp local GGUF inference + HF Hub model discovery.

NousResearch/hermes-agent Hermes author: NousResearch MIT 7 files body ≈ 2 120 tokens Open the sourcegithub.com↗ analyzed 2 d ago

llama.cpp local GGUF inference + HF Hub model discovery.

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureGitHubSoftware developmentAI and agentstype 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%
86
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 7. 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 65/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Consistency. The Hermes dialect needs category and tags
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 57 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 2120 tokens
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (8 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 56: 120–800 characters recommended
    • -2localhost URLs: will not work for another user
    • +2Single-language instructions
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 57 items
    • +3Output format is stated explicitly
    • +4Has examples (13 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.