BC alibabacloud-mongodb-instances-manage
Alibaba Cloud MongoDB full lifecycle management: create/query/scale/delete standalone, replica set, sharded cluster instances. Covers node management, security (whitelist/security group), public & SRV address, password reset, renewal, billing conversion, cloud disk reconfiguration, maintenance window, backup, version upgrade, HA switchover, account & tag management. Triggers: "MongoDB", "create MongoDB", "dds instance", "list instances", "MongoDB scaling", "add Mongos/Shard node", "MongoDB whitelist", "reset password", "allocate public address", "SRV address", "MongoDB renewal", "billing type conversion", "cloud disk reconfiguration", "delete MongoDB instance", "maintenance window", "restart MongoDB", "MongoDB backup", "upgrade MongoDB version", "HA switchover", "MongoDB tags", "MongoDB account"
As a process C 56/100 · Has gaps — weak spots: steps, result and completion, running it twice
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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Risky intent
intent-offensive-securityreferences/cli-installation-guide.md:35Offensive-security / dual-use content (legitimate for authorised testing; review intended use)> **Note:** Package manager installation (`dpkg`/`rpm`/`yum`) requires root privileges. Run as root or prepend your preferred privilege escalation method.
Files scanned: 8. 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 ≈ 7978 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 56/100
- 0Steps. Prose only: no discrete steps
- 30Running it twice. 33 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 (bash, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 7978 tokens
- 100Failures and branches. 7 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 28 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 806: 120–800 characters recommended
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 21 example trigger phrases
- +4Structure: 32 headings
- +4Has examples (24 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.