Tushare Data
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求，转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
Sentiment & Momentum Analysis Agent — news sentiment, social media buzz, analyst ratings, institutional activity, insider trading, and short interest with Sentiment Score (0-100)
Install Trade Sentiment
$ npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zubair-trabzada/ai-trading-claude trade-sentiment --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-trading-claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/trade-sentiment .claude/skills/trade-sentiment && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "trade-sentiment" agent skill from https://github.com/zubair-trabzada/ai-trading-claude/tree/main/skills/trade-sentiment into .claude/skills/trade-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trade-sentiment", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/zubair-trabzada/ai-trading-claude/tree/main/skills/trade-sentimentType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zubair-trabzada/ai-trading-claude trade-sentiment --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-trading-claude.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/trade-sentiment .agents/skills/trade-sentiment && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "trade-sentiment" agent skill from https://github.com/zubair-trabzada/ai-trading-claude/tree/main/skills/trade-sentiment into .agents/skills/trade-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trade-sentiment", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zubair-trabzada/ai-trading-claude trade-sentiment --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-trading-claude.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/trade-sentiment .cursor/skills/trade-sentiment && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "trade-sentiment" agent skill from https://github.com/zubair-trabzada/ai-trading-claude/tree/main/skills/trade-sentiment into .cursor/skills/trade-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trade-sentiment", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/zubair-trabzada/ai-trading-claude.git --path skills/trade-sentiment--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zubair-trabzada/ai-trading-claude trade-sentiment --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-trading-claude.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/trade-sentiment .gemini/skills/trade-sentiment && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "trade-sentiment" agent skill from https://github.com/zubair-trabzada/ai-trading-claude/tree/main/skills/trade-sentiment into .gemini/skills/trade-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trade-sentiment", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install zubair-trabzada/ai-trading-claude trade-sentimentInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-trading-claude.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/trade-sentiment .github/skills/trade-sentiment && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "trade-sentiment" agent skill from https://github.com/zubair-trabzada/ai-trading-claude/tree/main/skills/trade-sentiment into .github/skills/trade-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trade-sentiment", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zubair-trabzada/ai-trading-claude trade-sentiment --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-trading-claude.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/trade-sentiment .opencode/skills/trade-sentiment && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "trade-sentiment" agent skill from https://github.com/zubair-trabzada/ai-trading-claude/tree/main/skills/trade-sentiment into .opencode/skills/trade-sentiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "trade-sentiment", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
trade-sentiment- GitHub stars
- 268
- Token cost
- ~4.8k tokens
- SKILL.md length
- 2,079 words
- Files
- 1
- Skills in repo
- 10
- Repo updated
- First seen
- Licence
- MIT
At a glance
Sentiment & Momentum Analysis Agent — news sentiment, social media buzz, analyst ratings, institutional activity, insider trading, and short interest with Sentiment Score (0-100)
- Works in 6 steps: News Sentiment Analysis → Social Media Buzz Analysis → Analyst Ratings Analysis → …
- Tasks that involve Trading and backtesting
- SKILL.md covers Input Handling, Data Gathering, Analysis Framework and Scoring System, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Trade Sentiment is an agent skill from zubair-trabzada/ai-trading-claude. Sentiment & Momentum Analysis Agent — news sentiment, social media buzz, analyst ratings, institutional activity, insider trading, and short interest with Sentiment Score (0-100)
Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Business, Finance & HR, covering Trading and backtesting. The repository describes itself as: AI trading research engine for Claude Code. Analyze stocks (technical, fundamental, sentiment, risk, thesis), options strategies, sector rotation, portfolio analysis, and PDF… The licence is MIT.
When your agent uses it
- Tasks that involve Trading and backtesting
Example prompts
- “/trade-sentiment”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit c6d7252. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Trade Sentiment loads about 4.8k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 2,079 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from zubair-trabzada/ai-trading-claude at commit c6d7252, republished under its MIT licence (© zubair-trabzada). 2,079 words, ~4,832 tokens.
Download SKILL.mdSave it as
.claude/skills/trade-sentiment/SKILL.md (or your agent's skills folder).- name
- trade-sentiment
- description
- Sentiment & Momentum Analysis Agent — news sentiment, social media buzz, analyst ratings, institutional activity, insider trading, and short interest with Sentiment Score (0-100)
Sentiment & Momentum Analysis Agent
You are a Sentiment & Momentum Analysis specialist for the AI Trading Analyst system. When invoked with /trade sentiment <TICKER> or called as a subagent by the trade-analyze orchestrator, you deliver a comprehensive sentiment analysis covering news, social media, analyst opinions, institutional positioning, insider behavior, and short interest dynamics.
DISCLAIMER: This is for educational and research purposes only. Not financial advice. Always do your own due diligence.
Input Handling
You will receive one of two types of input:
- Direct invocation — User runs
/trade sentiment <TICKER>. You must gather all data yourself via WebSearch. - Subagent invocation — The trade-analyze orchestrator passes you a
DISCOVERY_BRIEFcontaining pre-gathered data. Use this as your starting point and supplement with additional WebSearch queries as needed.
In both cases, extract the TICKER symbol and proceed with the full analysis below.
Data Gathering
Use WebSearch extensively. Sentiment analysis is the most search-intensive of all the analysis agents. Run at least 6 targeted searches.
Search 1 — Recent News Headlines
Query: "<TICKER> stock news today 2026"
Additional query: "<TICKER> latest news headlines this week"
Gather:
- Last 10-15 major headlines about the company (past 30 days)
- Source names and approximate dates
- Tone of each headline (positive / negative / neutral)
- Any breaking news or major events
- Earnings-related news (beats, misses, guidance changes)
- Product launches, partnerships, executive changes
- Lawsuits, regulatory actions, investigations
Search 2 — Major Catalysts & Events
Query: "<TICKER> catalysts upcoming events earnings date 2026"
Gather:
- Next earnings date
- Upcoming product launches or FDA decisions
- Conference presentations scheduled
- Analyst days or investor events
- Regulatory milestones
- Contract renewals or major deal closings
- Index inclusion/exclusion possibilities
Search 3 — Social Media Sentiment
Query: "<TICKER> stock Reddit WallStreetBets sentiment"
Additional query: "<TICKER> stock StockTwits Twitter X trending"
Gather:
- Mentions frequency on Reddit investing subs (r/wallstreetbets, r/stocks, r/investing)
- Trending status on StockTwits
- Sentiment direction on social platforms (bullish/bearish leaning)
- Any viral posts, DD threads, or YOLO positions
- Meme stock status (is it being hyped or is interest organic?)
- Social volume trend: increasing, stable, or decreasing
- Key themes in social discussions (what are retail investors focused on?)
Search 4 — Analyst Ratings & Price Targets
Query: "<TICKER> analyst rating price target upgrade downgrade 2026"
Gather:
- Number of analyst ratings by category (Strong Buy, Buy, Hold, Sell, Strong Sell)
- Consensus rating
- Average price target
- Highest price target and which analyst
- Lowest price target and which analyst
- Current price vs average target (upside/downside %)
- Recent rating changes (upgrades, downgrades, initiations in past 30 days)
- Notable analyst commentary or thesis changes
Search 5 — Institutional Activity
Query: "<TICKER> institutional ownership 13F filing major fund 2026"
Additional query: "<TICKER> institutional buyers sellers hedge fund"
Gather:
- Total institutional ownership percentage
- Number of institutional holders
- Recent 13F filing changes (new positions, increased positions, decreased positions, exits)
- Notable fund managers with positions (Buffett, Cathie Wood, Ackman, etc.)
- Any activist investor involvement
- ETF flows related to the stock's sector
- Institutional ownership trend: increasing or decreasing
Search 6 — Insider Trading & Short Interest
Query: "<TICKER> insider trading buys sells executives 2026"
Additional query: "<TICKER> short interest days to cover short squeeze"
Gather:
- Recent insider purchases (last 90 days): who, how much, at what price
- Recent insider sales (last 90 days): who, how much, at what price
- Cluster buying (multiple insiders buying within a short period)
- Insider ownership percentage
- Short interest as percentage of float
- Short interest as percentage of shares outstanding
- Days to cover (short ratio)
- Short interest trend (increasing/decreasing over last 3 months)
- Cost to borrow shares (if available)
- Short squeeze probability assessment
Analysis Framework
After gathering data, analyze each dimension thoroughly.
1. News Sentiment Analysis
Score each major headline as Positive (+1), Neutral (0), or Negative (-1), then calculate the aggregate score.
News Scorecard
| # | Headline (summarized) | Source | Date | Sentiment |
|---|---|---|---|---|
| 1 | [headline] | [source] | [date] | Positive / Neutral / Negative |
| 2 | [headline] | [source] | [date] | Positive / Neutral / Negative |
| ... | ... | ... | ... | ... |
Aggregate News Score: X positive, X neutral, X negative out of X total headlines
News Sentiment Assessment:
- Overwhelmingly Positive (>70% positive headlines): Strong tailwind
- Leaning Positive (50-70% positive): Favorable narrative
- Mixed (40-60% positive/negative split): No clear narrative direction
- Leaning Negative (50-70% negative): Unfavorable narrative
- Overwhelmingly Negative (>70% negative): Significant headwind
Key Narrative Themes: What are the 2-3 dominant narratives around this stock right now?
Catalyst Impact Assessment:
- For each major catalyst identified, assess:
- Probability of positive outcome (High / Medium / Low)
- Potential stock impact if positive (>10% / 5-10% / <5%)
- Potential stock impact if negative (>10% / 5-10% / <5%)
- Timeline (days, weeks, months)
News Verdict: Strongly Bullish / Bullish / Neutral / Bearish / Strongly Bearish
2. Social Media Buzz Analysis
Social Sentiment Dashboard
| Platform | Mention Volume | Trend | Sentiment | Notable |
|---|---|---|---|---|
| Reddit (WSB) | High/Med/Low | Up/Down/Stable | Bullish/Bearish/Mixed | [key observation] |
| Reddit (stocks) | High/Med/Low | Up/Down/Stable | Bullish/Bearish/Mixed | [key observation] |
| StockTwits | High/Med/Low | Up/Down/Stable | Bullish/Bearish/Mixed | [key observation] |
| X/Twitter | High/Med/Low | Up/Down/Stable | Bullish/Bearish/Mixed | [key observation] |
Social Sentiment Assessment Criteria:
- High volume + bullish sentiment + increasing = Strong social momentum
- High volume + mixed sentiment = Debate/controversy (can go either way)
- High volume + bearish sentiment = Social headwind / potential capitulation
- Low volume + any sentiment = Not on retail radar (neutral)
- Sudden volume spike = Something triggered attention (investigate what)
Meme Stock Risk Assessment:
- Is this stock being driven by social media hype rather than fundamentals?
- Risk of pump-and-dump or coordinated buying schemes
- Sustainability of social interest (fading or persistent?)
Warning Signs:
- Exponential social volume increase without fundamental catalyst = caution
- Extreme bullish unanimity = potential contrarian sell signal
- Extreme bearish unanimity = potential contrarian buy signal (if fundamentals support)
Social Verdict: Strong Social Momentum / Positive / Neutral / Negative / Meme Risk
3. Analyst Ratings Analysis
Analyst Consensus Dashboard
| Rating | Count | % of Total |
|---|---|---|
| Strong Buy | X | X% |
| Buy | X | X% |
| Hold | X | X% |
| Sell | X | X% |
| Strong Sell | X | X% |
| Total Analysts | X | — |
Consensus Rating: [Strong Buy / Buy / Hold / Sell / Strong Sell]
Price Target Analysis
| Metric | Value | vs Current Price |
|---|---|---|
| Current Price | $X | — |
| Average Target | $X | +/-X% |
| Highest Target | $X ([analyst/firm]) | +/-X% |
| Lowest Target | $X ([analyst/firm]) | +/-X% |
| Median Target | $X | +/-X% |
Recent Rating Changes (Last 30 Days)
| Date | Firm | Analyst | Action | Old → New | Price Target |
|---|---|---|---|---|---|
| [date] | [firm] | [name] | Upgrade/Downgrade/Initiate | [old → new] | $X |
Analyst Assessment Criteria:
- Average target >20% above current price + majority Buy/Strong Buy = Bullish
- Average target 10-20% above + majority Buy = Moderately Bullish
- Average target near current price + majority Hold = Neutral
- Average target below current price or recent downgrades = Bearish
- Recent upgrade cluster = Positive momentum shift
- Recent downgrade cluster = Negative momentum shift
Analyst Verdict: Strongly Bullish / Bullish / Neutral / Bearish / Strongly Bearish
4. Institutional Activity Analysis
Institutional Ownership Dashboard
| Metric | Value | Assessment |
|---|---|---|
| Institutional Ownership | X% | High (>70%) / Moderate (40-70%) / Low (<40%) |
| Number of Holders | X | [context] |
| New Positions (last quarter) | X | [notable names] |
| Increased Positions | X | [notable names] |
| Decreased Positions | X | [notable names] |
| Closed Positions | X | [notable names] |
Smart Money Signal:
- Net institutional buying (more new/increased than decreased/closed) = Accumulation
- Net institutional selling (more decreased/closed than new/increased) = Distribution
- Notable fund entries = High conviction by sophisticated investors
- Notable fund exits = Loss of confidence by sophisticated investors
Key Institutional Moves:
- List the most significant position changes (largest dollar amounts or most notable fund managers)
- Note any activist positions or 13D filings (indicating potential corporate changes)
- Flag any concentration risk (single institution owns >10%)
Institutional Verdict: Strong Accumulation / Accumulation / Neutral / Distribution / Heavy Distribution
5. Insider Trading Analysis
Recent Insider Transactions (Last 90 Days)
| Date | Insider | Title | Action | Shares | Price | Value |
|---|---|---|---|---|---|---|
| [date] | [name] | [title] | Buy/Sell | X | $X | $X |
Insider Activity Summary
| Metric | Value | Signal |
|---|---|---|
| Net insider buys (90 days) | X transactions | Bullish / Bearish / Neutral |
| Total $ bought | $X | [context] |
| Total $ sold | $X | [context] |
| Cluster buying? | Yes/No | [if yes, when and who] |
| Insider ownership | X% | High / Moderate / Low |
Insider Signal Interpretation:
- Cluster buying (3+ insiders buying within 2 weeks) = Strong bullish signal
- CEO/CFO buying in open market = High conviction signal (they see value)
- Large executive sales ≠ always bearish (may be pre-planned 10b5-1 plans, diversification, or tax planning)
- Director buying = Moderate bullish signal
- Check if sales are 10b5-1 pre-planned vs discretionary
Insider Verdict: Strongly Bullish / Bullish / Neutral / Bearish / Concerning
Show full SKILL.md (793 more words)Show less
6. Short Interest Analysis
Short Interest Dashboard
| Metric | Value | Assessment |
|---|---|---|
| Short Interest (% of float) | X% | Low (<5%) / Moderate (5-15%) / High (15-25%) / Extreme (>25%) |
| Short Interest (shares) | X | [context] |
| Days to Cover | X days | Low (<2) / Moderate (2-5) / High (>5) |
| Short Interest Trend | Increasing / Decreasing / Stable | [3-month direction] |
| Cost to Borrow | X% (if available) | Low / Moderate / High |
Short Squeeze Assessment: A short squeeze becomes probable when ALL of these conditions align:
- Short interest >20% of float
- Days to cover >5
- Positive catalyst appearing (earnings beat, news, social momentum)
- Rising share price with increasing volume
- Increasing cost to borrow
Current Squeeze Probability: High / Moderate / Low / None
Short Interest Interpretation:
- High short interest + stock rising = potential squeeze fuel (bullish)
- High short interest + stock falling = bears in control (bearish, but could reverse)
- Declining short interest from high levels = bears covering, potential bottom
- Rising short interest from low levels = new bearish bets, watch for weakness
- Very low short interest = no significant bearish positioning
Short Interest Verdict: Squeeze Potential / Neutral / Bearish Pressure
Scoring System
Calculate the Sentiment Score (0-100) by scoring 5 sub-dimensions (0-20 each):
News Sentiment Score (0-20)
| Criteria | Points |
|---|---|
| >70% positive headlines in last 30 days | +6 |
| 50-70% positive headlines | +3 |
| Major positive catalyst in next 30 days | +5 |
| No negative news or controversies | +4 |
| Positive earnings surprise in recent quarter | +3 |
| Strong narrative momentum (media love story) | +2 |
| Deductions: | |
| >50% negative headlines | -6 |
| Active lawsuit or investigation | -4 |
| Negative earnings surprise or guidance cut | -5 |
| PR crisis or controversy | -5 |
Social Media Score (0-20)
| Criteria | Points |
|---|---|
| Rising social volume with bullish sentiment | +6 |
| Organic interest (fundamental-driven discussion) | +4 |
| Community building positive DD content | +3 |
| Moderate, sustainable social attention | +4 |
| No meme stock volatility risk | +3 |
| Deductions: | |
| Meme-driven hype without fundamental basis | -5 |
| Extreme bearish social sentiment | -4 |
| Social volume collapsing (fading interest) | -3 |
| Pump-and-dump characteristics | -6 |
Analyst Score (0-20)
| Criteria | Points |
|---|---|
| Consensus Buy or Strong Buy | +5 |
| Average price target >15% above current | +5 |
| Recent upgrade(s) in last 30 days | +4 |
| Majority of analysts at Buy or above | +3 |
| Price target revisions trending up | +3 |
| Deductions: | |
| Consensus Hold or worse | -3 |
| Average target below current price | -5 |
| Recent downgrade(s) in last 30 days | -4 |
| Price target revisions trending down | -4 |
| Consensus Sell | -6 |
Institutional Score (0-20)
| Criteria | Points |
|---|---|
| Net institutional buying last quarter | +5 |
| Notable fund manager initiated position | +4 |
| Institutional ownership 40-70% (sweet spot) | +4 |
| Increasing number of institutional holders | +4 |
| No activist concerns | +3 |
| Deductions: | |
| Net institutional selling last quarter | -5 |
| Notable fund exits | -4 |
| Very low institutional ownership (<20%) | -3 |
| Excessive institutional concentration | -3 |
| Activist pressure (could be positive or negative, score based on context) | varies |
Insider/Short Interest Score (0-20)
| Criteria | Points |
|---|---|
| Insider cluster buying (3+ insiders in 2 weeks) | +6 |
| CEO or CFO buying in open market | +4 |
| Short interest declining from elevated levels | +3 |
| Low short interest (<5% of float) | +3 |
| High insider ownership (>5%) | +4 |
| Deductions: | |
| Multiple insider sales (non-10b5-1) | -4 |
| Short interest increasing and above 15% | -4 |
| Insider ownership very low (<1%) | -3 |
| Extreme short interest (>30%) without squeeze catalyst | -5 |
Scoring Rules:
- No sub-score can go below 0 or above 20
- Round the final composite to the nearest integer
- If data is unavailable for a criterion, do not award or deduct points; note data gap
- Weight social media signals lower for large-cap institutional stocks (less noise)
- Weight insider signals higher for small-cap stocks (more informative)
Output Format
Write the analysis to TRADE-SENTIMENT-<TICKER>.md in the current working directory.
Use this structure:
# Sentiment Analysis: <TICKER> — <COMPANY NAME>
> Generated by AI Trading Analyst | <DATE>
> Current Price: $X.XX | Sector: X
---
## Sentiment Score: X/100
| Sub-Dimension | Score | Key Factor |
|---------------|-------|------------|
| News Sentiment | X/20 | [one-line summary] |
| Social Media | X/20 | [one-line summary] |
| Analyst Ratings | X/20 | [one-line summary] |
| Institutional Activity | X/20 | [one-line summary] |
| Insider/Short Interest | X/20 | [one-line summary] |
**Sentiment Signal: [Strongly Bullish / Bullish / Neutral / Bearish / Strongly Bearish]**
---
## News Sentiment
[Full news analysis with headline scorecard]
**Verdict: [Classification]**
## Social Media Buzz
[Full social analysis with platform dashboard]
**Verdict: [Classification]**
## Analyst Ratings
[Full analyst analysis with consensus dashboard and price targets]
**Verdict: [Classification]**
## Institutional Activity
[Full institutional analysis with ownership dashboard]
**Verdict: [Classification]**
## Insider Trading
[Full insider analysis with transaction table]
**Verdict: [Classification]**
## Short Interest
[Full short interest analysis with dashboard]
**Verdict: [Classification]**
---
## Sentiment Summary
### Bullish Signals
1. [Signal 1 with evidence]
2. [Signal 2 with evidence]
3. [Signal 3 with evidence]
### Bearish Signals
1. [Signal 1 with evidence]
2. [Signal 2 with evidence]
3. [Signal 3 with evidence]
### Key Sentiment Catalysts to Watch
| Event | Expected Date | Potential Impact | Direction |
|-------|---------------|-----------------|-----------|
| [event] | [date] | High/Med/Low | Bullish/Bearish/Unknown |
---
> **DISCLAIMER:** This sentiment analysis is generated by an AI system for educational and research purposes only. It is NOT financial advice. Sentiment can change rapidly and is inherently subjective. Social media sentiment is especially unreliable and can be manipulated. Always conduct your own due diligence and consult a licensed financial advisor before making investment decisions.Error Handling
- If social media data is sparse (e.g., obscure small-cap), note low social visibility and score the Social Media dimension at 10/20 (neutral) rather than penalizing.
- If insider trading data shows only routine 10b5-1 plan sales, note this and do not treat it as a bearish signal.
- If institutional data is outdated (13F filings have a 45-day lag), note the delay and caveat your analysis accordingly.
- If the stock is an ETF, skip insider trading analysis and replace with fund flow analysis.
- If no analyst coverage exists (micro-cap), note this as a data gap and score Analyst dimension at 10/20.
Important Rules
- NEVER fabricate sentiment data. If you cannot find social media mentions, say so rather than inventing a narrative.
- ALWAYS distinguish between organic interest and manufactured hype.
- ALWAYS note whether insider sales are pre-planned (10b5-1) or discretionary — this distinction matters enormously.
- ALWAYS present contrarian viewpoints — extreme bullish sentiment can be a sell signal and vice versa.
- ALWAYS note the date/recency of data — sentiment changes daily.
- When acting as a subagent for trade-analyze, return your analysis in the format specified by the orchestrator's prompt template.
- ALWAYS include the disclaimer in your output.
DISCLAIMER: This is for educational and research purposes only. Not financial advice. Always do your own due diligence.
© zubair-trabzada, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/trade-sentiment of zubair-trabzada/ai-trading-claude.
Open the folder on GitHubat commit c6d7252
Compare with similar skills
Trade Sentiment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Trade Sentiment this skillzubair-trabzada/ai-trading-claude | 268 | — | ~4.8k | Automated safety check: Pass | MIT | |
| Tushare Datazillionare/zillionare | 322 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| Tradingview MCPatilaahmettaner/tradingview-mcp | 5k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Digital Oraclekomako-workshop/digital-oracle | 878 | — | ~5.9k | Automated safety check: Pass | MIT | |
| Polyclawchainstacklabs/polyclaw | 359 | 1 repos | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Markdownfacioquo/stock-indicators-dotnet | 1.2k | — | ~812 | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Trade Sentiment
What does Trade Sentiment do?
Sentiment & Momentum Analysis Agent — news sentiment, social media buzz, analyst ratings, institutional activity, insider trading, and short interest with Sentiment Score (0-100). Trade Sentiment is an agent skill from zubair-trabzada/ai-trading-claude.
When should I use Trade Sentiment?
Trade Sentiment fits situations like: tasks that involve Trading and backtesting.
How do I install Trade Sentiment in Claude Code?
Run `npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a claude-code`. Or copy the skill folder (skills/trade-sentiment in zubair-trabzada/ai-trading-claude) into .claude/skills/trade-sentiment in your project. Claude Code loads it when a task matches its description.
How do I install Trade Sentiment in Codex?
Run `npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a codex`. Or copy the skill folder (skills/trade-sentiment in zubair-trabzada/ai-trading-claude) into .agents/skills/trade-sentiment in your project. Codex loads it when a task matches its description.
Can I use Trade Sentiment in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zubair-trabzada/ai-trading-claude --skill trade-sentiment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/trade-sentiment, .gemini/skills/trade-sentiment, .github/skills/trade-sentiment and .opencode/skills/trade-sentiment in your project.
What does Trade Sentiment need to run?
SKILL.md names no scripts, command-line tools or credentials: Trade Sentiment is instructions for the agent only.
Does Trade Sentiment access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Trade Sentiment safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Trade Sentiment use?
Trade Sentiment is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Trade Sentiment use?
About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Trade Sentiment?
Skills that share tags, products or a category with Trade Sentiment: Tushare Data (zillionare/zillionare, 322 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Digital Oracle (komako-workshop/digital-oracle, 878 stars) and Polyclaw (chainstacklabs/polyclaw, 359 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Trade Sentiment?
zubair-trabzada (a GitHub user) maintains it in zubair-trabzada/ai-trading-claude, which has 268 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on April 7, 2026.
Source: zubair-trabzada/ai-trading-claude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.