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Runs a multi-model deliberation across models from different labs (Claude, GPT, Gemini, Grok, DeepSeek, Kimi, and more) via mumo's MCP server, returning full responses plus typed cross-model reactions. Use when independent perspectives are needed on architecture/product decisions, design and plan review before implementation, pre-launch pressure tests, tradeoffs with multiple defensible framings, or explicit user requests for a mumo panel. Especially valuable for pre-implementation review of anything touching auth, security, tokens, payments, data exposure, or migrations.

ClawHub Hermes author: Eric H v0.6.3 MIT-0 15 files body ≈ 4 058 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

Runs a multi-model deliberation across models from different labs (Claude, GPT, Gemini, Grok, DeepSeek, Kimi, and more) via mumo's MCP server, returning full…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
93
Quality 40%
78
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Consistency w 8
0
Inputs and preconditions w 11
30
the three weakest of ten parameters · all ten

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 · 3

✓ No critical or high findings

Medium and low: 3
  • medium Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: MUMO_API_KEY — verify each one is needed for the stated purpose
    required_environment_variables: MUMO_API_KEY
  • low Risky intent intent-offensive-security playbooks/red-team.md:1
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    # Red Team
  • low Risky intent intent-offensive-security playbooks/red-team.md:15
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Red team rounds produce two kinds of findings:

Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 578 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "required_environment_variables"

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 0Consistency. Frontmatter name (mumo) differs from the folder (mumo-hermes)
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 60Tools and files. Uses tools (git) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4058 tokens
  • 85Steps. 47 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 5 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low 19 top-level sections: this looks like several domains in one skill

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)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 578: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 47 items
  • +4Reference files are cited in the instructions (6 of 6)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: clean
This skill is a disclosed multi-model review helper that sends user-provided prompts to mumo's MCP service, with no evidence of hidden execution, persistence, or data theft.
LLM: benign (high) · VirusTotal: · 9 Sept 2026