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Delegate coding to OpenHands CLI (model-agnostic, LiteLLM).

NousResearch/hermes-agent Hermes author: NousResearch MIT 1 file body ≈ 1 984 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Delegate coding to OpenHands CLI (model-agnostic, LiteLLM).

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

IntegrationGitHubSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
82
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration net-credential-use SKILL.md:62
      Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
      command="OPENHANDS_SUPPRESS_BANNER=1 LLM_MODEL=openrouter/openai/gpt-4o-mini LLM_API_KEY=$OPENROUTER_API_KEY LLM_BASE_URL=https://openrouter.ai/api/v1 openhands --headless --json --override-with-envs
      quoted
    • low Exfiltration net-credential-use SKILL.md:137
      Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
      command="OPENHANDS_SUPPRESS_BANNER=1 LLM_MODEL=openrouter/openai/gpt-4o-mini LLM_API_KEY=$OPENROUTER_API_KEY LLM_BASE_URL=https://openrouter.ai/api/v1 openhands --headless --json --override-with-envs
      quoted

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 114): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 48/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 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
    • 85Steps. 23 steps, 2 vague phrases
    • 100Execution cost. Instruction body is 1984 tokens
    • 100Running it twice. No mutating operations
    • low The response is described with custom markup (3 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 59: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (7 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.