AD multi-agent-deployment
Deploy production-grade multi-agent fleets in OpenClaw with battle-tested scripts, cloud deployment templates, and shared memory infrastructure. Use when: (1) Deploying multiple specialized AI agents with routing, (2) Setting up shared memory between agents, (3) Deploying to cloud platforms (DigitalOcean, AWS, GCP, K8s), (4) Building agent teams for business workflows, (5) Moving from single-agent to production multi-agent setups. Includes working Python scripts, cloud deployment configs, and troubleshooting guides based on real deployments.
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-agent-memory-dumpassets/templates/SOUL.mdAgent memory / workspace files bundled with the skill (2) — likely a workspace dump with personal data or tokensassets/templates/SOUL.md, HEARTBEAT.md
Files scanned: 20. 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 44/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 37 mutating operations with no state check
- 40Consistency. Frontmatter name (multi-agent-deployment) differs from the folder (skill-multi-agent-deployment)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 41 steps
- 100Execution cost. Instruction body is 3584 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 12 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
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 547: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (14 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 5 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.