SKILLEMALL.ai
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AC sec-finance

Fetch structured financial data and filing metadata from SEC EDGAR and SEC XBRL companyfacts for US-listed companies, especially Chinese issuers. Use when the task is quarterly/annual financial analysis, CIK resolution, filing lookup, or extracting metrics such as revenue, net income, and EPS from SEC data. Do not use for downloading static IR PDFs; use ir-pdf-downloader for document discovery/download.

ClawHub Agent Skills author: Lu Wang v0.1.0 MIT-0 4 files · 1 script body ≈ 356 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Fetch structured financial data and filing metadata from SEC EDGAR and SEC XBRL companyfacts for US-listed companies, especially Chinese issuers.

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

ProcedureData 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 4. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 356 tokens
    • 100Running it twice. No mutating operations

    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 406: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    The skill does what it claims, but it automatically weakens HTTPS certificate checks when fetching SEC financial data, which can let tampered data be accepted without clear user control.
    LLM: suspicious (high) · VirusTotal: · 14 Sept 2026