AC task-decomp
Plan, track, and learn from complex multi-step tasks. Decomposes requests into dependency-aware subtasks with parallel execution, progress tracking, and a learning loop that improves future plans from past outcomes. Use when: (1) A request involves 3+ steps with dependencies, (2) Work spans multiple tools or sub-agents, (3) User says 'plan this out', 'break this down', or 'how should we approach this', (4) Managing a project, workflow, or multi-phase build, (5) Coordinating parallel workstreams. Improves over time — completed plans feed back into planning intelligence. NOT for: simple one-shot requests, single tool calls, or conversational exchanges.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
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: 2. 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 59/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 38 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1246 tokens
- low 11 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
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 658: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 38 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.