AC multi-agent-orchestrator
Design, build, and manage multi-agent teams that turn a solo operator into a 1000-person organization. Covers team role design (CEO/PM/Engineer/Analyst/Writer/Monitor), inter-agent communication patterns, task distribution, conflict resolution, and priority management. Based on real production experience running 13-agent teams on OpenClaw. Use when the user wants to build an agent team, orchestrate multiple AI agents, design agent roles, set up agent communication, create a one-person company with AI agents, implement multi-agent workflows, or scale from solo to team-of-agents. Triggers on multi-agent, agent team, agent orchestration, AI team, agent collaboration, agent roles, one-person company, solo founder scaling, agent communication, task delegation to agents, swarm intelligence.
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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 · 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 52/100
- 0Result and completion. Does not say what the result is
- 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
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (multi-agent-orchestrator) differs from the folder (friday-multi-agent-orchestrator)
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 13 steps
- 100Execution cost. Instruction body is 1317 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 795: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.