Clearstreet fundamentals deep dive
Skill clear-street/clearstreet-skills/skills/clearstreet-fundamentals-deep-dive
Generate a one-page research brief for a US stock or ETF using the Clear Street API — fundamentals (market cap, P/E, EPS, beta, sector, 52-week range), analyst consensus (rating + price-target distribution), earnings history (estimate vs actual + surprise %), and the latest quarterly income and cash-flow statements. Use when the user asks to "research", "deep-dive", "analyze", or "give me a one-pager on" a single ticker. For multi-name screens use clearstreet-screener; for an earnings calendar use clearstreet-earnings-radar; for free-form questions use clearstreet-omni-research.From its SKILL.md
npx -y skills add clear-street/clearstreet-skills --skill clearstreet-fundamentals-deep-diveAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
2.3 KB, 438 tokens by cl100k_base, as published. Nobody here has run it
Fundamentals Deep-Dive
Build a single-ticker research brief by joining several Clear Street API endpoints into one markdown page: snapshot, analyst consensus, earnings history, and the latest quarterly statements.
Prerequisites
- Python 3.9+ (standard library only)
CLEARSTREET_API_KEYset in the environment — see the repo README
Quick start
python3 scripts/deep_dive.py NVDA
Usage
deep_dive.py SYMBOL [--quarters N] [--json]
| Argument | Default | Description |
|---|---|---|
SYMBOL | (required) | Ticker (e.g. NVDA) or instrument UUID |
--quarters | 4 | Number of earnings quarters to display |
--json | off | Emit the raw merged JSON instead of markdown |
Examples
| Ask | Command |
|---|---|
| "Research NVDA for me" | deep_dive.py NVDA |
| "Deep-dive on Apple, last 8 quarters" | deep_dive.py AAPL --quarters 8 |
| "Income statement and cash flow for TSLA" | deep_dive.py TSLA |
Workflow
- Identify the ticker the user is asking about.
- Run
python3 scripts/deep_dive.py <SYMBOL>. - Present the markdown brief directly — it's formatted for chat, Slack, or a doc. Show only the fields the brief includes; if you add any instrument-identity details, omit option-only fields (expiry / strike) for stocks and ETFs, and don't surface the internal instrument id (UUID).
- For free-form follow-ups ("is it a buy?"), chain to
clearstreet-omni-research.
See reference.md for endpoint and field details.
Safety
Read-only. The script issues only GET requests and never touches order or position endpoints. Output is informational, not investment advice.
What ships with it: 3 files
15.0 KB alongside SKILL.md, 2 of them executable
scripts/
- clearstreet_client.pyruns3.8 KB
- deep_dive.pyruns8.9 KB
- reference.md2.4 KB
Gives 0 of the 12 instructions most research analysis skills give in 438 tokens
Counted across 1,213 of the 2,113 authors here whose files we hold, read 2026-09-06
- Cite sources for every important claimin 47 of 1213, across 38 files
- Separate facts from inferences and recommendationsin 21 of 1213, across 12 files
- Write findings to a markdown filein 19 of 1213
- Label every insight with a confidence levelin 18 of 1213, across 8 files
- Read product marketing context before asking questionsin 18 of 1213, across 8 files
- Rank themes by frequency and intensityin 16 of 1213, across 6 files
- Establish research mode before proceedingin 16 of 1213, across 6 files
- Segment survey responses by customer tier or tenurein 16 of 1213, across 6 files
- Categorize support tickets before analyzingin 16 of 1213, across 6 files
- Weight research sources from the last twelve monthsin 16 of 1213, across 6 files
- Use at least five data points per segmentin 15 of 1213, across 5 files
- Extract verbatim quotes for all research findingsin 15 of 1213, across 5 files
Said here and by no other author read
- Identify the requested ticker symbol
- Run the deep dive script with the ticker
- Present the markdown brief directly
- Omit internal instrument UUIDs
- Omit option-only fields for stocks and ETFs
- Chain to omni-research for follow-up questions
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.