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AC ai-newsletter-daily

Generate a daily AI news newsletter for a Chinese audience from fresh web sources. Return the newsletter body and article summaries in Simplified Chinese.

ClawHub Hermes author: Jeff Yang v1.0.0 MIT-0 2 files body ≈ 834 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 57/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

GeneratorWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
82
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Consistency w 8
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: BRAVE_API_KEY, FIRECRAWL_API_KEY — verify each one is needed for the stated purpose
    required_environment_variables: BRAVE_API_KEY, FIRECRAWL_API_KEY

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 155 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)

Process rating: all ten parameters 57/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Consistency. Frontmatter name (ai-newsletter-daily) differs from the folder (ai-newsletter-chn-for-hermes)
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 6 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 43 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 834 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
  • +4No input/output examples
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 43 items
  • +3Output format is stated explicitly
  • +1License stated

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

External checks

ClawHub: clean
The skill artifacts describe ClawHub and Convex maintenance workflows with powerful but disclosed, purpose-aligned commands and user-control guidance.
LLM: benign (medium) · VirusTotal: · 29 May 2026