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AB stock-deep-research

Run a 5-dimension deep dive on a public stock, sector, or small portfolio — fundamentals and valuation from the platform's structured financial API, price history and mechanical indicators from yfinance, filings from SEC EDGAR — and hand back a period-stamped research note with bull / bear / base framing, a what-to-watch plan, and an audit log of every source. Trigger on "deep dive on TICKER", "research this stock", "stock analysis", "sector outlook", or "review my portfolio". Educational research only, not investment advice.

genspark Agent Skills author: Genspark 1 file body ≈ 6 656 tokens Open the sourcegenspark.ai↗ analyzed 4 d ago

As a process B 79/100 · Nearly there — weak spots: running it twice

IntegrationOutlookData and analyticsFinancetype and topics are labelled automatically from the skill text
Runs in: Genspark 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
B
79/100
Nearly there
Running it twice w 4
30
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 · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6656 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 79/100

  • 30Running it twice. 12 mutating operations with no state check
  • 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 6656 tokens
  • 100Steps. 74 steps
  • 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
  • 100Progress reporting. Reports progress
  • 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
  • low The response is described with custom markup (4 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

  • +4Description does not say when NOT to use the skill (false activations)
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 531: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 74 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.