stock-copilot-pro

qverisai/open-qveris-skills · updated Apr 8, 2026

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$npx skills add https://github.com/qverisai/open-qveris-skills --skill stock-copilot-pro
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Global Multi-Source Stock Analysis with QVeris.

skill.md

Stock Copilot Pro

Global Multi-Source Stock Analysis with QVeris.

SEO Keywords

OpenClaw, stock analysis skill, AI stock copilot, China A-shares, Hong Kong stocks, US stocks, quantitative analysis, fundamental analysis, technical analysis, sentiment analysis, industry radar, morning evening brief, watchlist, portfolio monitoring, QVeris API, THS iFinD, Caidazi, Alpha Vantage, Finnhub, X sentiment, investment research assistant

Supported Capabilities

  • Single-stock analysis (analyze): valuation, quality, technicals, sentiment, risk/timing
  • Multi-stock comparison (compare): cross-symbol ranking and portfolio-level view
  • Watchlist/holdings management (watch): list/add/remove for holdings and watchlist
  • Morning/Evening brief (brief): holdings-focused daily actionable briefing
  • Industry hot-topic radar (radar): multi-source topic aggregation for investable themes
  • Multi-format output: markdown, json, chat
  • OpenClaw LLM-ready flow: structured data in code + guided narrative in SKILL.md

Data Sources

  • Core MCP/API gateway: qveris.ai (QVERIS_API_KEY)
  • CN/HK quote and fundamentals:
    • ths_ifind.real_time_quotation
    • ths_ifind.financial_statements
    • ths_ifind.company_basics
    • ths_ifind.history_quotation
  • CN/HK news and research:
    • caidazi.news.query
    • caidazi.report.query
    • caidazi.search.hybrid.list
    • caidazi.search.hybrid_v2.query
  • Global news sentiment:
    • alpha_news_sentiment
    • finnhub.news
  • X/Twitter sentiment and hot topics:
    • qveris_social.x_domain_hot_topics
    • qveris_social.x_domain_hot_events
    • qveris_social.x_domain_new_posts
    • x_developer.2.tweets.search.recent

What This Skill Does

Stock Copilot Pro performs end-to-end stock analysis with five data domains:

  1. Market quote / trading context
  2. Fundamental metrics
  3. Technical signals (RSI/MACD/MA)
  4. News and sentiment
  5. X sentiment

It then generates a data-rich analyst report with:

  • value-investing scorecard
  • event-timing anti-chasing classification
  • safety-margin estimate
  • thesis-driven investment framework (drivers/risks/scenarios/KPIs)
  • multi-style playbooks (value/balanced/growth/trading)
  • event radar with candidate ideas from news and X
  • scenario-based recommendations
  • standard readable output (default) + optional full evidence trace (--evidence)

Key Advantages

  • Deterministic tool routing via references/tool-chains.json
  • Evolution v2 parameter-template memory to reduce recurring parameter errors
  • Strong fallback strategy across providers and markets
  • US/HK/CN market-aware symbol handling
  • Structured outputs for both analyst reading and machine ingestion
  • Safety-first handling of secrets and runtime state

Core Workflow

  1. Resolve user input to symbol + market (supports company-name aliases, e.g. Chinese name -> 600089.SH).

  2. Search tools by capability (quote, fundamentals, indicators, sentiment, X sentiment).

  3. Route by hardcoded tool chains first (market-aware), then fallback generic capability search.

    • For CN/HK sentiment, prioritize caidazi channels (report/news/wechat).
    • For CN/HK fundamentals, prioritize THS financial statements (income/balance sheet/cash flow), then fallback to company basics.
  4. Before execution, try evolution parameter templates; if unavailable, use default param builder.

  5. Run quality checks:

    • Missing key fields
    • Data recency
    • Cross-source inconsistency
  6. Produce analyst report with:

    • composite score
    • safety margin
    • event-driven vs pullback-risk timing classification
    • structured thesis (driver/risk/scenario/KPI)
    • event radar (timeline/theme) and candidate ideas
    • style-specific execution playbooks
    • market scenario suggestions
    • optional parsed/raw evidence sections when --evidence is enabled
  7. Preference routing (public audience default):

    • If no preference flags are provided, script returns a questionnaire first.
    • You can skip this with --skip-questionnaire.

Command Surface

Primary script: scripts/stock_copilot_pro.mjs

  • Analyze one symbol:
    • node scripts/stock_copilot_pro.mjs analyze --symbol AAPL --market US --mode comprehensive
    • node scripts/stock_copilot_pro.mjs analyze --symbol "<company-name>" --mode comprehensive
  • Compare multiple symbols:
    • node scripts/stock_copilot_pro.mjs compare --symbols AAPL,MSFT --market US --mode comprehensive
  • Manage watchlist:
    • node scripts/stock_copilot_pro.mjs watch --action list
    • node scripts/stock_copilot_pro.mjs watch --action add --bucket holdings --symbol AAPL --market US
    • node scripts/stock_copilot_pro.mjs watch --action remove --bucket watchlist --symbol 0700.HK --market HK
  • Generate brief:
    • node scripts/stock_copilot_pro.mjs brief --type morning --format chat
    • node scripts/stock_copilot_pro.mjs brief --type evening --format markdown
  • Run industry radar:
    • node scripts/stock_copilot_pro.mjs radar --market GLOBAL --limit 10

OpenClaw scheduled tasks (morning/evening brief and radar)

To set up morning brief, evening brief, or daily radar in OpenClaw, use only the official OpenClaw cron format and create jobs via the CLI or Gateway cron tool. Do not edit ~/.openclaw/cron/jobs.json directly.

  • Reference: the jobs array in config/openclaw-cron.example.json; each item is one cron.add payload (fields: name, schedule: { kind, expr, tz }, sessionTarget: "isolated", payload: { kind: "agentTurn", message: "..." }, delivery).
  • Example (morning brief): openclaw cron add --name "Stock morning brief" --cron "0 9 * * 1-5" --tz Asia/Shanghai --session isolated --message "Use stock-copilot-pro to generate morning brief: run brief --type morning --max-items 8 --format chat" --announce. To deliver to Feishu, add --channel feishu --to <group-or-chat-id>.
  • Incorrect: using the legacy example format (e.g. schedule as string, command, delivery.channels array) or pasting the example into jobs.json will cause Gateway parse failure or crash.

CN/HK Coverage Details

  • Company-name input is supported and auto-resolved to market + symbol for common names.
  • Sentiment path prioritizes caidazi (research reports, news, wechat/public-account channels).
  • Fundamentals path prioritizes THS financial statements endpoints, and always calls THS company basics for profile backfill:
    • revenue
    • netProfit
    • totalAssets
    • totalLiabilities
    • operatingCashflow
    • industry
    • mainBusiness
    • tags

Output Modes

  • markdown (default): human-readable report
  • json: machine-readable merged payload
  • chat: segmented chat-friendly output for messaging apps
  • summary-first: compact output style via --summary-only

Preference & Event Options

  • Preference flags:

    • --horizon short|mid|long
    • --risk low|mid|high
    • --style value|balanced|growth|trading
    • --actionable (include execution-oriented rules)
    • --skip-questionnaire (force analysis without preference Q&A)
  • Event radar flags:

    • --event-window-days 7|14|30
    • --event-universe global|same_market
    • --event-view timeline|theme

Dynamic Evolution

  • Runtime learning state is stored in .evolution/tool-evolution.json.
  • One successful execution can update tool parameter templates.
  • Evolution stores param_templates and sample_successful_params for reuse.
  • Evolution does not decide tool priority; tool priority is controlled by tool-chains.json.
  • Use --no-evolution to disable loading/saving runtime learning state.

Safety and Disclosure

  • Uses only QVERIS_API_KEY.
  • Calls only QVeris APIs over HTTPS.
  • full_content_file_url fetching is kept enabled for data completeness, but only HTTPS URLs under qveris.ai are allowed.
  • Does not store API keys in logs, reports, or evolution state.
  • Runtime persistence is limited to .evolution/tool-evolution.json (metadata + parameter templates only).
  • Watchlist state is stored at config/watchlist.json (bootstrap from config/watchlist.example.json).
  • OpenClaw scheduled tasks: see config/openclaw-cron.example.json. Create jobs with the official format (schedule.kind, payload.kind, sessionTarget, etc.) via openclaw cron add or the Gateway cron tool; do not paste or merge the example JSON into ~/.openclaw/cron/jobs.json (schema mismatch can cause Gateway parse failure or crash). Set delivery.channel and delivery.to for your channel (e.g. feishu).
  • External source URLs remain hidden by default; only shown when --include-source-urls is explicitly enabled.
  • No package installation or arbitrary command execution is performed by this skill script.
  • Research-only output. Not investment advice.

Single Stock Analysis Guide

When analyzing analyze output, act as a senior buy-side analyst and deliver a professional but not overlong report.

Required Output (7 Sections)

  1. Data Snapshot (required)
    • Start with a compact metrics table built from data fields.
    • Include at least: price/change, marketCap, PE/PB, profitMargin, revenue, netProfit, RSI, 52W range.
    • Example format:
| Metric | Value |
|--------|-------|
| Price | $264.58 (+1.54%) |
| Market Cap | $3.89T |
| P/E | 33.45 |
| P/B | 57.97 |
| Profit Margin | 27% |
| Revenue (TTM) | $394B |
| Net Profit | $99.8B |
| RSI | 58.3 |
| 52W Range | $164 - $270 |
  1. Key view (30 seconds)

    • One-line conclusion: buy/hold/avoid + key reason.
  2. Investment thesis

    • Bull case: 2 points (growth driver, moat/catalyst)
    • Bear case: 2 points (valuation/risk/timing)
    • Final balance: what dominates now.
  3. Valuation and key levels

    • PE/PB vs peer or history percentile (cheap/fair/expensive)
    • Key levels: current price, support, resistance, stop-loss reference
  4. Recommendation (required)

    • Different advice by position status:
      • No position
      • Light position
      • Heavy position / underwater
    • Each suggestion must include concrete trigger/price/condition.
  5. Risk monitor

    • Top 2-3 risks + invalidation condition (what proves thesis wrong).
  6. Data Sources (required)

    • End with a source disclosure line showing QVeris attribution and data channels actually used.
    • Include generation timestamp and list of source/tool names from payload metadata such as dataSources, meta.sourceStats, or data.*.selectedTool.
    • Example format:
> Data powered by [QVeris](https://qveris.ai) | Sources: Alpha Vantage (quote/fundamentals), Finnhub (news sentiment), X/Twitter (social sentiment) | Generated at 2026-02-22T13:00:00Z

Quality Bar

  • Avoid data dumping; each key number must include interpretation.
  • Every numeric claim must be grounded in actual payload values; do not fabricate numbers.
  • Keep concise but complete (target 250-500 characters for narrative).
  • Must include actionable guidance and time window.
  • Ticker and technical terms in English.

Daily Brief Analysis Guide

When analyzing brief output, generate an actionable morning/evening briefing for OpenClaw conversation.

Morning Brief

  1. Market overview: risk-on/off + key overnight move + today's tone, plus an index snapshot table from marketOverview.indices (index name, price, % change, timestamp)
  2. Holdings check: holdings that need action first, with per-holding price/% change/grade when available
  3. Radar relevance: which radar themes impact holdings
  4. Today's plan (required): specific watch levels / event / execution plan
  5. Data Sources (required): one-line QVeris attribution and channels used in this brief

Evening Brief

  1. Session recap: index + sector + portfolio one-line recap, with key index close/% change
  2. Holdings change: biggest winners/losers and why, with quantized move (%) where available
  3. Thesis check: whether thesis changed
  4. Tomorrow's plan (required): explicit conditions and actions
  5. Data Sources (required): one-line QVeris attribution and channels used in this brief

Quality Bar

  • Prioritize user holdings, not generic market commentary.
  • Quantify changes when possible (%, levels, counts).
  • Keep concise and decision-oriented.
  • Include a short source disclosure line at the end to improve traceability and credibility.

Hot Topic Analysis Guide

When analyzing radar output, cluster signals into investable themes and provide concise actionable conclusions.

Required Output (per theme)

  • Theme: clear, investable label
  • Driver: what changed and why now
  • Impact: beneficiaries/losers + magnitude + duration
  • Recommendation (required): concrete trigger or level
  • Risk note: key invalidation or monitoring signal
  • Source tag (required): include source label for each theme (for example: caidazi_report, alpha_news_sentiment, x_hot_topics)

Execution Rules

  • Cluster into 3-5 themes max.
  • Cross-verify sources; lower confidence for social-only signals.
  • Distinguish short-term trade vs mid-term allocation.
  • Keep each theme concise (<200 characters preferred).
  • End with a QVeris source disclosure line listing channels that contributed to this radar run.
how to use stock-copilot-pro

How to use stock-copilot-pro on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add stock-copilot-pro
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/qverisai/open-qveris-skills --skill stock-copilot-pro

The skills CLI fetches stock-copilot-pro from GitHub repository qverisai/open-qveris-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/stock-copilot-pro

Reload or restart Cursor to activate stock-copilot-pro. Access the skill through slash commands (e.g., /stock-copilot-pro) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.550 reviews
  • Alexander Zhang· Dec 28, 2024

    Useful defaults in stock-copilot-pro — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Pratham Ware· Dec 12, 2024

    stock-copilot-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chaitanya Patil· Dec 8, 2024

    Registry listing for stock-copilot-pro matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kiara Sharma· Dec 8, 2024

    Solid pick for teams standardizing on skills: stock-copilot-pro is focused, and the summary matches what you get after install.

  • Chen Kim· Dec 4, 2024

    I recommend stock-copilot-pro for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kaira Sanchez· Dec 4, 2024

    stock-copilot-pro has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Piyush G· Nov 27, 2024

    stock-copilot-pro reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Alexander Robinson· Nov 27, 2024

    stock-copilot-pro has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Xiao Choi· Nov 23, 2024

    stock-copilot-pro fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Xiao Haddad· Nov 23, 2024

    Solid pick for teams standardizing on skills: stock-copilot-pro is focused, and the summary matches what you get after install.

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