BC greenhelix-agent-workforce-orchestration
Agent Workforce Orchestration: Hybrid Human+AI Teams. Build agent-led workforce orchestration: capability matching, escrow-based payments for AI agents and human gig workers, unified reputation scoring, SLA enforcement, dispute resolution, and compliance reporting. Includes detailed Python code examples for every pattern.
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 25584 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "type" - note
frontmatter-keyunknown frontmatter key "price_usd" - note
frontmatter-keyunknown frontmatter key "content_type" - note
frontmatter-keyunknown frontmatter key "executable" - note
frontmatter-keyunknown frontmatter key "install" - note
frontmatter-keyunknown frontmatter key "credentials"
Process rating: all ten parameters 53/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 10Execution cost. Instruction body is 25584 tokens: crowds the task out of the window
- 30Running it twice. 17 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 100Steps. 37 steps
- 100Consistency. Name and required fields are in place
- 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
- +2Single-language instructions
- +3Description length 323: enough signal without eating the budget
- +4Structure: 56 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (36 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.