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AB earnings-calendar

This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 5 739 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API.

As a process B 78/100 · Nearly there — weak spots: progress reporting

ProcedurePersonal productivityData and analyticsWriting and documentstype 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
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
84
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-password-literal references/fmp_api_guide.md:127
    Hard-coded password / key literal (may be an example)
    GET /api/v3/earning_calendar?apikey=KEY&from=2025-11-03&to=2025-11-09
  • low Secrets in code secret-password-literal scripts/fetch_earnings_fmp.py:299
    Hard-coded password / key literal (may be an example)
    api_key = sys.argv[3]

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5739 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 78/100

  • 0Progress reporting. Says nothing while it works
  • 60Tools and files. Uses tools (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 5739 tokens
  • 85Steps. 165 steps, 3 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +3Description length 543: enough signal without eating the budget
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 165 items
  • +3Output format is stated explicitly
  • +4Has examples (28 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: 84.