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AA economic-calendar-fetcher

Fetch upcoming economic events and data releases using FMP API. Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases, and other market-moving economic indicators for specified date ranges (default: next 7 days). The script outputs raw JSON or text; the assistant filters, assesses impact, and generates the Markdown report.

tradermonty/claude-trading-skills Agent Skills author: tradermonty 7 files · 4 scripts body ≈ 4 099 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Fetch upcoming economic events and data releases using FMP API.

As a process A 80/100 · Runs to the end — no weak spots found

IntegrationPersonal productivityData and analyticstype 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
A
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
98
Run on models
none yet
Process rating
A
80/100
Runs to the end
Tools and files w 18
60
Result and completion w 14
60
Inputs and preconditions w 11
70
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: claude-trading-skills

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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 21, 136, 344, 371): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 80/100

    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4099 tokens
    • 85Steps. 135 steps, 3 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 3 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

    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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 362: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 135 items
    • +3Output format is stated explicitly
    • +4Has examples (11 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 98.