AC agent-superpowers
Your agent says 'done' — but did it check? Superpowers turns any OpenClaw agent into a disciplined engineer. Verification iron law (evidence before claims), three-agent code review (build → verify spec → verify quality), systematic debugging (4-phase root cause, three-strike rule), brainstorming gates (design before code), and anti-over-engineering rules. Use when: (1) coding tasks of any complexity, (2) debugging failures, (3) about to claim work is complete, (4) spawning sub-agents, (5) planning features, (6) reviewing code. Inspired by top coding agent methodologies, adapted for OpenClaw multi-agent architecture.
As a process C 56/100 · 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: 7. 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 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 18 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (read, git) that frontmatter does not declare
- 70Failures and branches. 5 branches
- 100Steps. 75 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3074 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 13 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
- +1No license
- +2Single-language instructions
- +3Description length 623: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 75 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.