SKILLEMALL.ai

AC hkex-li-daily-factor-monitor

Fetch the latest HKEXnews "Daily Targeted Leverage Factor" announcement(s) for HKEX-listed Leveraged & Inverse (L&I) products, extract each product's daily targeted leverage factor from the announcement PDF, and emit a human-readable Telegram digest — product rows in a monospace code block grouped by applicable date, with source PDF links below — ready to display in the OpenClaw channel. Use for daily manual runs or a scheduled cron check of the next trading day's L&I factors. Agent-native: curl + jq for discovery, pdftotext for PDF text, the agent reasons out the factor table. PDFs are downloaded per-run and deleted after parsing (HKEX PDFs are copyrighted).

ClawHub Agent Skills author: larryjoe v1.0.0 MIT-0 8 files · 1 script body ≈ 4 885 tokens Open the sourceclawhub.ai analyzed 3 d ago

Fetch the latest HKEXnews "Daily Targeted Leverage Factor" announcement(s) for HKEX-listed Leveraged & Inverse (L&I) products, extract each product's daily…

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

ProcedureTelegramPDFSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 8. 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 13 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4885 tokens
    • 85Steps. 45 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • high The skill tells the model to perform an irreversible action with no human approval
    • low The response is described with custom markup (7 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)
    • +3Output format is not stated: the model decides each time
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 667: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 45 items
    • +4Has examples (14 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: clean
    This skill is a disclosed HKEX data monitor with an optional, user-confirmed schedule and no evidence of hidden data access, credential use, or exfiltration.
    LLM: benign (high) · VirusTotal: · 8 Aug 2026