Stock Research
- Repo stars 640
- License MIT
- Author updated Live
- Author repo honeclaw
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 94 / 100 · audit passed
- Author / version / license
- @B-M-Capital-Research · MIT
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
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。
---
name: Stock Research
description: Canonical Hone equity-research skill covering single-stock analysis, valuation framing, and crit…
category: other
runtime: no special runtime
---
# Stock Research output preview
## PART A: Task fit
- Use case: Canonical Hone equity-research skill covering single-stock analysis, valuation framing, and criteria-based screening This is the canonical equity-research entrypoint for Hone. Use it for three closely related user intents: Prefer keeping these modes inside one skill so the model does not have to choose between overlapping prompt variants. makes outbound n….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Stock Research Skill / Tool Guide / Mode Selection” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Canonical Hone equity-research skill covering single-stock analysis, valuation framing, and criteria-based screening This is the canonical equity-research entrypoint for Hone. Use it for three closely related user intents: Prefer keeping these modes inside one skill so the model does not have to choose between overlapping prompt variants. makes outbound n…”.
- **02** When the source has headings, the agent prioritizes “Stock Research Skill / Tool Guide / Mode Selection” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; usually needs no extra API key.
## Running Rules
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “Stock Research Skill / Tool Guide / Mode Selection”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: Stock Research
description: Canonical Hone equity-research skill covering single-stock analysis, valuation framing, and crit…
category: other
source: B-M-Capital-Research/honeclaw
---
# Stock Research
## When to use
- Canonical Hone equity-research skill covering single-stock analysis, valuation framing, and criteria-based screening T…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Stock Research Skill / Tool Guide / Mode Selection” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "Stock Research" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Stock Research Skill / Tool Guide / Mode Selection
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | may access external network resources
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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
snapshotfor the baseline data - Decide whether to add
web_searchfor 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_visualizationwith the concrete numbers you already fetched
Valuation Mode
- Resolve the ticker first; do not attempt valuation without confirming the company
- Fetch
financials; addquoteorsnapshotif you also need current market context - Use
web_searchfor 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 targetedweb_searchto form an initial candidate set - Narrow the result to 3-5 names and fetch
snapshotfor 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
Decide Fit First
Design Intent
How To Use It
Boundaries And Review