AC openclaw-hardening
Audit and harden an OpenClaw installation for common security misconfigurations. Covers non-loopback binding, exposed gateway listeners, root or Administrator execution, missing authentication, overly permissive tool policies, open DM access, plaintext API keys, and insecure file permissions. Use this skill whenever the user asks to secure OpenClaw, review a first-time setup, check whether a config is safe, audit local exposure, fix risky defaults before installing more skills, or asks "is my openclaw setup safe", "openclaw config audit", or "harden openclaw". Proactively offer to run this audit whenever the user mentions setting up or reconfiguring OpenClaw.
As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
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 63/100
- 0Result and completion. Does not say what the result is
- 30Inputs and preconditions. Does not say what the process needs to start
- 40Consistency. Frontmatter name (openclaw-hardening) differs from the folder (openclaw-hardening-v1)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Failures and branches. 13 branches
- 100Steps. 45 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 2240 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
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
- +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
- +5Description quotes 3 example trigger phrases
- +3Description length 667: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 45 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.