SKILLEMALL.ai
GitHub ↗ RU Sign in

AC pdf

PDF files: create, read, merge, fill, OCR, edit text.

NousResearch/hermes-agent Hermes author: NousResearch MIT 21 files body ≈ 3 022 tokens Open the sourcegithub.com↗ analyzed 2 d ago

PDF files: create, read, merge, fill, OCR, edit text.

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

ProcedurePDFData and analyticstype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
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: 20. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 8 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Consistency. The Hermes dialect needs category and tags
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 43 steps
    • 100Execution cost. Instruction body is 3022 tokens

    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
    • +3Description length 53: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -31 of 15 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.