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Poll RSS, JSON APIs, and GitHub with watermark dedup.

NousResearch/hermes-agent Hermes author: NousResearch MIT 5 files · 4 scripts body ≈ 1 040 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Poll RSS, JSON APIs, and GitHub with watermark dedup.

As a process D 49/100 · Unfinished process — weak spots: when it triggers, inputs and preconditions, failures and branches

ProcedureGitHubSoftware developmentAI and agentstype 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%
88
Run on models
none yet
Process rating
D
49/100
Unfinished process
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: hermes-agent

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: 5. 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 49/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Consistency. The Hermes dialect needs category and tags
    • 100Steps. 17 steps
    • 100Execution cost. Instruction body is 1040 tokens
    • high The skill tells the model to perform an irreversible action with no human approval
    • low The response is described with custom markup (5 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
    • +2Single-language instructions
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 17 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +3All 4 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    In the sandbox The scripts kept to themselves

    The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

    Запущено 4 скрипта; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

    Из них 3 не дошли до работы, и об их поведении мы ничего не узнали.

    scripts/_watermark.pyничего за пределами своей папки
    scripts/watch_github.pyне запустился: watch_github.py: error: the following arguments are required: --name
    scripts/watch_http_json.pyне запустился: watch_http_json.py: error: the following arguments are required: --name, --url
    scripts/watch_rss.pyне запустился: watch_rss.py: error: the following arguments are required: --name, --url

    10 Oct 2026