AC phoenix-shield
Self-healing backup and update system with intelligent rollback. Protects against failed updates by automatically monitoring system health post-update and recovering from backups when needed. Features canary deployment testing, health baselines, smart rollback, and 24/7 automated monitoring. Use when performing critical system updates, managing production deployments, or ensuring high availability of services. Prevents downtime through pre-flight checks, integrity verification, and automatic recovery workflows.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 2 more places: ClawHub, ClawHub
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 51/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (phoenix-shield) differs from the folder (phoenix-sheld)
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 34 steps
- 100Execution cost. Instruction body is 1935 tokens
- 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
- -2localhost URLs: will not work for another user
- -212 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 516: enough signal without eating the budget
- +4Structure: 34 headings
- +3Step-by-step instructions: 34 items
- +4Has examples (20 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.