SKILLEMALL.ai
GitHub ↗ RU Sign in

AC find-china-cdc-health-report

Locate, verify, optionally download, and extract five official China CDC report types: China influenza weekly reports, acute respiratory infectious-disease sentinel weekly reports, national COVID-19 monthly updates, global infectious-disease event risk assessments, and China key-infectious-disease/public-health-emergency risk assessments. Use for a named report type plus week, month, issue, publication/reference date, latest, official PDF, report content, table, chart, summary, or comparison. Do not use for medical advice, prediction, third-party reports, or unregistered sources.

ClawHub Agent Skills author: miaoshou.dev v1.0.3 MIT-0 21 files · 2 scripts body ≈ 1 937 tokens Open the sourceclawhub.ai↗ analyzed 13 d ago

Locate, verify, optionally download, and extract five official China CDC report types: China influenza weekly reports, acute respiratory infectious-disease…

As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerData and analyticstype 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
97/100
safety, quality, tests
Safety 60%
98
Quality 40%
95
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Inputs and preconditions w 11
30
the three weakest of ten parameters · all ten

How to improve

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token references/output-schema.md:17
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "report_id": "infl…917",
      quoted
    • low Secrets in code secret-high-entropy-token references/report-schema.md:8
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "report_id": "infl…917",
      quoted

    Files scanned: 21. 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
    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (find-china-cdc-health-report) differs from the folder (cdc-data)
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 100Steps. 44 steps
    • 100Execution cost. Instruction body is 1937 tokens
    • 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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 586: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 44 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (9 of 10)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: clean
    This skill is narrowly built to find and process official China CDC reports, with no evidence of hidden persistence, credential use, or unrelated data access.
    LLM: benign (high) · VirusTotal: · 14 Sept 2026