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CB acedatacloud

Manage your AceDataCloud account through the management API (platform.acedata.cloud). Use when connecting scoped OAuth account access, or when the user wants to check their balance / remaining credits, look up API call (usage) records and spend, list or create or delete API keys (credentials), list subscribed services, list/create/pay recharge orders, manage platform tokens, view referral/affiliate earnings, manage article/video blog drafts, review threads and peer-reviewed publication via MCP / REST, or (admins) publish an announcement. Also covers the PUBLIC catalog & docs (no token needed): service detail & pricing, API list & OpenAPI specs, datasets, integrations, full-text documentation search, and the model catalog with per-model credit pricing. This is the self-service "console" API — distinct from the data-generation APIs (image/video/music/search).

AceDataCloud/Skills Agent Skills author: AceDataCloud 2 files · 1 script body ≈ 7 434 tokens Open the sourcegithub.com↗ analyzed 5 h ago

Manage your AceDataCloud account through the management API (platform.acedata.cloud). Use when connecting scoped OAuth account access, or when the user wants…

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions

IntegrationStripeData and analyticsAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
81/100
safety, quality, tests
Safety 60%
85
Quality 40%
74
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
30
Tools and files w 18
60
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • medium Exfiltration net-redirectable-api-key scripts/acedatacloud.py:27
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment
  • medium Exfiltration net-credential-use SKILL.md:55
    Credential used in a network call (verify the destination is the intended service)
    curl -H "authorization: Bearer $ACEDATACLOUD_PLATFORM_TOKEN" \
  • medium Exfiltration net-credential-use SKILL.md:139
    Credential used in a network call (verify the destination is the intended service)
    curl -H "authorization: Bearer $ACEDATACLOUD_PLATFORM_TOKEN" \

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7434 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "connections"
  • note edit-residue the text marks something as outdated (lines 300): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 66/100

  • 0Result and completion. Does not say what the result is
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 7434 tokens
  • 100Steps. 69 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (6 tags): a typed call is more reliable

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)
  • +3Description length 869: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 69 items
  • +4Has examples (10 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.