Stock Research

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Repo stars 640
License MIT
Author repo honeclaw

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Canonical Hone equity-research skill covering single-stock analysis, valuation framing, and criteria-based screening This is the…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Stock Research Skill”, “Tool Guide”, “Mode Selection”, “Research Mode”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name Stock Research directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Stock Research Skill / Tool Guide / Mode Selection” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Stock Research Skill

This is the canonical equity-research entrypoint for Hone.

Use it for three closely related user intents:

Single-company research
Valuation framing for a named company
Criteria-based stock screening that returns a short comparison list

Prefer keeping these modes inside one skill so the model does not have to choose between overlapping prompt variants.

Tool Guide

Tool call	Purpose
`data_fetch(data_type="snapshot", symbol="ticker")`	Recommended. Fetch a snapshot with price action plus company overview
`data_fetch(data_type="quote", symbol="ticker")`	Fetch detailed real-time quote data such as price, change, and volume
`data_fetch(data_type="profile", symbol="ticker")`	Fetch company details such as business description, industry, and CEO
`data_fetch(data_type="financials", symbol="ticker")`	Fetch financial statements or valuation-relevant fundamentals
`data_fetch(data_type="gainers_losers")`	Broader market scan when a screening request needs candidates
`data_fetch(data_type="sector_performance")`	Sector strength context for screening or relative positioning
`web_search(query="...")`	Search for news, analyst views, and recent events

Mode Selection

Choose the mode from the user's request before fetching data:

Research mode: the user asks about one company, ticker, fundamentals, technicals, or recent developments
Valuation mode: the user asks whether a company looks rich, cheap, stretched, fairly priced, or wants a valuation bridge / peer view
Screening mode: the user asks for a shortlist that matches factors such as AI, dividend yield, value, growth, or momentum

Research Mode

Identify the ticker mentioned by the user. If it is unclear, search first with data_fetch(data_type="search", symbol="...")
Call snapshot for the baseline data
Decide whether to add web_search for news or causes
Output a combined answer covering price action, fundamentals, recent events, and risks
If the user explicitly asks for a chart, trend line, comparison visual, or the answer would be materially clearer as a chart, hand off to chart_visualization with the concrete numbers you already fetched

Valuation Mode

Resolve the ticker first; do not attempt valuation without confirming the company
Fetch financials; add quote or snapshot if you also need current market context
Use web_search for the latest operating updates, guidance changes, or peer-comparison context
Explain the valuation through assumptions, peer multiples, and business quality, and state which conditions would make the company look richer, more balanced, or more compelling relative to peers
Do not collapse the result into a simplistic categorical verdict with no assumptions attached

Screening Mode

Extract the user's explicit criteria before naming companies
Use gainers_losers, sector_performance, or targeted web_search to form an initial candidate set
Narrow the result to 3-5 names and fetch snapshot for each final candidate
Return a comparison shortlist with why each name matches the screen, plus the main risk or diligence gap for each one
Do not output a blunt recommendation list without comparison logic or caveats

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Other
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 94 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @B-M-Capital-Research · MIT
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Detected network behavior: External requests
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview stock-research.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

## Stock Research Skill

This is the canonical equity-research entrypoint for Hone.

Use it for three closely related user intents:

1. Single-company research
2. Valuation framing for a named company
3. Criteria-based stock screening that returns a short comparison list

Prefer keeping these modes inside one skill so the model does not have to choose between overlapping prompt variants.

### Tool Guide

| Tool call | Purpose |
|---------|------|
| `data_fetch(data_type="snapshot", symbol="ticker")` | Recommended. Fetch a snapshot with price action plus company overview |
| `data_fetch(data_type="quote", symbol="ticker")` | Fetch detailed real-time quote data such as price, change, and volume |
| `data_fetch(data_type="profile", symbol="ticker")` | Fetch company details such as business description, industry, and CEO |
| `data_fetch(data_type="financials", symbol="ticker")` | Fetch financial statements or valuation-relevant fundamentals |
| `data_fetch(data_type="gainers_losers")` | Broader market scan when a screening request needs candidates |
| `data_fetch(data_type="sector_performance")` | Sector strength context for screening or relative positioning |
| `web_search(query="...")` | Search for news, analyst views, and recent events |

### Mode Selection

Choose the mode from the user's request before fetching data:

- **Research mode**: the user asks about one company, ticker, fundamentals, technicals, or recent developments
- **Valuation mode**: the user asks whether a company looks rich, cheap, stretched, fairly priced, or wants a valuation bridge / peer view
- **Screening mode**: the user asks for a shortlist that matches factors such as AI, dividend yield, value, growth, or momentum

### Research Mode

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Stock Research Skill → Tool Guide → Mode Selection → Research Mode → Valuation Mode → Screening Mode
terms    -> Research mode · Valuation mode · Screening mode · This is the canonical equity-research entrypoint for Hone. · 1. Single-company research 2. · Prefer keeping these modes inside one skill so the model does not have to choose between overlapping prompt variants. · | Tool call | Purpose | |---------|------| | datafetch(datatype="snapshot", symbol="ticker") | Recommended. · 1. Identify the ticker mentioned by the user.
files/cmd -> datafetch(datatype="snapshot", symbol="ticker") · datafetch(datatype="quote", symbol="ticker") · datafetch(datatype="profile", symbol="ticker") · datafetch(datatype="financials", symbol="ticker") · datafetch(datatype="gainerslosers") · datafetch(datatype="sectorperformance") · websearch(query="...") · datafetch(datatype="search", symbol="...")
body sha256 -> 64ed8b9867f4