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AC alibabacloud-odps-information-schema

Query MaxCompute (ODPS) Information Schema metadata views. Tenant-level (SYSTEM_CATALOG.INFORMATION_SCHEMA.*, recommended) or project-level (Information_Schema.*, deprecated). NL→SQL for IS views: tables, columns, partitions, tasks_history, tunnels_history, table_privileges, users, user_roles, quota_usage, etc. NOT for: DDL/DML, listing tables via MCP, running ad-hoc SQL, general MaxCompute questions.

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 14 files · 1 script body ≈ 4 460 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 63/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

ProcedureInfrastructureAI and agentsSales and CRMtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 14. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 63/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 4 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4460 tokens
  • 100Steps. 28 steps
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 404: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (11 of 11)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: suspicious
This skill is mostly a legitimate MaxCompute metadata helper, but it documents broader SQL and write-operation paths that conflict with its stated read-only Information Schema scope.
LLM: suspicious (high) · 28 May 2026