AB mumo
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. Requires a mumo platform API key (mmo_live_*) registered with `openclaw mcp set mumo`.
As a process B 77/100 · Nearly there — weak spots: result and completion, inputs and preconditions
How to improve
- 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
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low Risky intent
intent-offensive-securityplaybooks/red-team.md:1Offensive-security / dual-use content (legitimate for authorised testing; review intended use)# Red Team
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low Risky intent
intent-offensive-securityplaybooks/red-team.md:15Offensive-security / dual-use content (legitimate for authorised testing; review intended use)Red team rounds produce two kinds of findings:
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low Risky intent
intent-offensive-securityREADME.md:69Offensive-security / dual-use content (legitimate for authorised testing; review intended use)Either way, the skill ships the canonical `SKILL.md`, four cognitive-shape playbooks (contested decision, design review, uncertainty expansion, red team), and reference docs for claim-map reading, sni
Files scanned: 15. 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 77/100
- 0Result and completion. Does not say what the result is
- 30Inputs and preconditions. Does not say what the process needs to start
- 70Execution cost. Instruction body is 4192 tokens
- 100Tools and files. No external tools needed
- 100Steps. 47 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 8 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 20 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
- +1No license
- +2Single-language instructions
- +3Description length 665: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 47 items
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.