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Agent skill

Sentiment Analysis

by agiprolabs in agiprolabs/claude-trading-skills

Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

MITAuto-check passedBackend & APIs

Install Sentiment Analysis

skills CLI
$ npx skills add agiprolabs/claude-trading-skills --skill sentiment-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agiprolabs/claude-trading-skills sentiment-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sentiment-analysis .claude/skills/sentiment-analysis && rm -rf skills-src

Use ~/.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/

Facts

Skill name
sentiment-analysis
GitHub stars
410
Token cost
~2.4k tokens
SKILL.md length
554 words
Files
5 (incl. scripts, references)
Skills in repo
68
Repo updated
First seen
Licence
MIT

At a glance

Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

  • Tasks that involve Customer feedback analysis
  • SKILL.md covers When to Use This Skill, Core Concepts, Integration With Other Skills and Practical Workflow, plus 2 more sections
  • Runs Python scripts from its folder
  • Tasks that involve Smart contracts

What it does

Sentiment Analysis is an agent skill from agiprolabs/claude-trading-skills. Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/data_sources.md`, `references/scoring_methods.md` and `scripts/keyword_sentiment.py`).

It sits in Backend & APIs, covering Customer feedback analysis, Smart contracts and Influencer and creator marketing. It works with Reddit. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.

When your agent uses it

  • Tasks that involve Customer feedback analysis
  • Tasks that involve Smart contracts
  • Tasks that involve Influencer and creator marketing

Example prompts

  • “/sentiment-analysis”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 981e1d7. 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

    Ships 2 files in scripts/ (Python), which the agent can run.

    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

Sentiment Analysis loads about 2.4k tokens when it runs, and up to ~6.5k if it reads all its reference files. Until then it costs about 41 tokens; SKILL.md has 554 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.5k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 554 words, ~2,388 tokens.

Download SKILL.mdSave it as .claude/skills/sentiment-analysis/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
sentiment-analysis
description
Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

Sentiment Analysis

Extract and quantify market sentiment from social media, news feeds, and on-chain data to identify crowd positioning and potential contrarian opportunities.

When to Use This Skill

  • Gauge crowd sentiment before entering or exiting a position
  • Detect euphoria/panic extremes that precede reversals
  • Monitor social mention velocity for early trend detection
  • Track influencer activity around specific tokens
  • Build composite sentiment scores for systematic strategies

Core Concepts

Sentiment Data Sources
SourceData TypeAccess
Twitter/XPost text, engagement, follower countsAPI (paid tiers)
RedditSubreddit posts, comments, upvotesReddit API
TelegramChannel messages, member countsBot API or scraping
DiscordServer activity, message volumeBot integration
NewsHeadlines, article textNewsAPI, RSS feeds
CoinGeckoCommunity stats, developer activityFree API
Alternative.meFear & Greed IndexFree API
On-chainFunding rates, exchange flowsExchange APIs

See references/data_sources.md for complete API details, rate limits, and access patterns for each source.

Sentiment Metrics

Mention Velocity — Rate of token mentions over time:

python
mention_velocity = mentions_last_hour / baseline_hourly_mentions
# > 3.0 = trending, > 10.0 = viral

Sentiment Polarity — Positive vs negative tone:

python
polarity = (positive_count - negative_count) / total_count
# Range: -1.0 (all negative) to +1.0 (all positive)

Fear & Greed Index — Composite market mood (0-100):

RangeLabelTypical Signal
0-24Extreme FearPotential accumulation zone
25-44FearBelow-average sentiment
45-55NeutralNo strong directional bias
56-74GreedAbove-average sentiment
75-100Extreme GreedPotential distribution zone

Social Volume — Total mentions across platforms:

python
social_volume_z = (current_volume - mean_30d) / std_30d
# z > 2.0 suggests unusual activity
On-Chain Sentiment Proxies

On-chain data reveals what participants are doing, not just saying:

Funding Rates — Perpetual futures cost of carry:

python
# Positive funding = longs pay shorts (bullish crowding)
# Negative funding = shorts pay longs (bearish crowding)
funding_sentiment = -1.0 * normalize(funding_rate, -0.1, 0.1)
# Inverted: high positive funding is contrarian bearish

Long/Short Ratio — Proportion of leveraged positions:

python
ls_ratio = long_accounts / short_accounts
# > 2.0 = crowded long, < 0.5 = crowded short
ls_sentiment = -1.0 * normalize(ls_ratio, 0.5, 2.0)

Exchange Flows — Net deposits/withdrawals:

python
net_flow = exchange_inflows - exchange_outflows
# Positive net flow (deposits) = bearish (selling pressure)
# Negative net flow (withdrawals) = bullish (accumulation)
flow_sentiment = -1.0 * normalize(net_flow, -threshold, threshold)
Keyword-Based Sentiment Scoring

A simple, LLM-free approach using curated word lists:

python
BULLISH_KEYWORDS = {
    "moon": 2, "bullish": 2, "pump": 1, "breakout": 2,
    "buy": 1, "long": 1, "accumulate": 2, "undervalued": 2,
    "gem": 1, "rocket": 1, "ath": 1, "rally": 2,
}
BEARISH_KEYWORDS = {
    "dump": 2, "bearish": 2, "crash": 2, "scam": 3,
    "rug": 3, "sell": 1, "short": 1, "overvalued": 2,
    "dead": 2, "rekt": 1, "ponzi": 3, "exit": 1,
}

def score_text(text: str) -> float:
    """Score text from -1.0 (bearish) to +1.0 (bullish)."""
    words = text.lower().split()
    bull_score = sum(BULLISH_KEYWORDS.get(w, 0) for w in words)
    bear_score = sum(BEARISH_KEYWORDS.get(w, 0) for w in words)
    total = bull_score + bear_score
    if total == 0:
        return 0.0
    return (bull_score - bear_score) / total

See references/scoring_methods.md for the full methodology, temporal decay weighting, and composite score construction.

Composite Sentiment Score

Combine multiple signals into a single score:

python
def composite_sentiment(
    social_polarity: float,    # -1.0 to +1.0
    mention_velocity: float,   # 0 to inf
    fear_greed: int,           # 0 to 100
    funding_rate: float,       # -0.1 to +0.1
    weights: dict | None = None,
) -> float:
    """Compute weighted composite sentiment score (-100 to +100).

    Args:
        social_polarity: Average polarity of social mentions.
        mention_velocity: Current velocity vs baseline.
        fear_greed: Fear & Greed index reading.
        funding_rate: Current perpetual funding rate.
        weights: Optional custom weights.

    Returns:
        Composite score from -100 (extreme fear) to +100 (extreme greed).
    """
    w = weights or {
        "social": 0.30,
        "velocity": 0.15,
        "fear_greed": 0.30,
        "funding": 0.25,
    }
    # Normalize each component to -1.0 to +1.0
    s_social = social_polarity
    s_velocity = min(mention_velocity / 10.0, 1.0)  # Cap at 10x
    s_fg = (fear_greed - 50) / 50.0  # 0-100 -> -1 to +1
    s_funding = -10.0 * funding_rate  # Contrarian: high funding = bearish
    s_funding = max(-1.0, min(1.0, s_funding))

    raw = (
        w["social"] * s_social
        + w["velocity"] * s_velocity
        + w["fear_greed"] * s_fg
        + w["funding"] * s_funding
    )
    return round(raw * 100, 1)
Contrarian Signals

Extreme sentiment readings often precede reversals:

ConditionInterpretation
Composite < -70Extreme fear — historically a buying zone
Composite > +70Extreme greed — historically a selling zone
Velocity > 10x + polarity > 0.6Euphoric spike — fade potential
Velocity > 10x + polarity < -0.6Panic spike — bounce potential
Funding > 0.05% + LS ratio > 2.0Crowded long — liquidation risk
Funding < -0.05% + LS ratio < 0.5Crowded short — squeeze risk

Key principle: Sentiment is most useful at extremes. Neutral readings (composite between -30 and +30) have low predictive value.

Show full SKILL.md (206 more words)Show less
Influencer Tracking

Monitor high-follower accounts for early signal detection:

python
def influencer_signal(
    posts: list[dict],
    min_followers: int = 50_000,
    lookback_hours: int = 24,
) -> dict:
    """Detect influencer activity around a token.

    Args:
        posts: List of posts with 'followers', 'timestamp', 'sentiment'.
        min_followers: Minimum follower count to qualify as influencer.
        lookback_hours: Time window in hours.

    Returns:
        Dict with influencer_count, avg_sentiment, total_reach.
    """
    cutoff = time.time() - (lookback_hours * 3600)
    relevant = [
        p for p in posts
        if p["followers"] >= min_followers and p["timestamp"] >= cutoff
    ]
    if not relevant:
        return {"influencer_count": 0, "avg_sentiment": 0.0, "total_reach": 0}
    return {
        "influencer_count": len(relevant),
        "avg_sentiment": sum(p["sentiment"] for p in relevant) / len(relevant),
        "total_reach": sum(p["followers"] for p in relevant),
    }

Integration With Other Skills

SkillIntegration Point
position-sizingReduce size in extreme greed, increase in extreme fear
risk-managementTighten stops when sentiment diverges from price
regime-detectionSentiment confirms or contradicts regime classification
feature-engineeringSentiment metrics as ML features
signal-classificationSentiment as input to signal scoring models
whale-trackingCombine whale activity with social sentiment
token-holder-analysisHolder growth/decline as sentiment proxy

Practical Workflow

1. Fetch fear/greed index          → Market-wide mood
2. Pull social data for token      → Token-specific sentiment
3. Score text with keyword method  → Polarity scores
4. Compute mention velocity        → Trending detection
5. Check on-chain proxies          → Funding, flows
6. Calculate composite score       → Single decision input
7. Flag contrarian signals         → Extreme readings
8. Integrate with position sizing  → Adjust allocation

Limitations and Warnings

  • Sentiment is noisy. Individual readings are unreliable — use trends and extremes.
  • Social data is gameable. Bot activity can inflate mention counts.
  • Keyword scoring is crude. It misses sarcasm, context, and nuance.
  • Lag exists. By the time sentiment is measurable, price may have moved.
  • Not financial advice. Sentiment data is for informational and analytical purposes only.
  • API access varies. Twitter/X API pricing has changed frequently. Budget accordingly.
  • Survivorship bias. Tokens that go to zero stop being discussed — absence of mentions is also a signal.

Files

References
  • references/data_sources.md — API details, rate limits, and access patterns for all sentiment data sources
  • references/scoring_methods.md — Keyword lists, composite scoring methodology, temporal decay, contrarian logic
Scripts
  • scripts/sentiment_scanner.py — Fetches live sentiment data from free APIs, computes composite scores, flags contrarian signals
  • scripts/keyword_sentiment.py — Standalone keyword-based text sentiment analyzer with synthetic demo data

© agiprolabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/sentiment-analysis of agiprolabs/claude-trading-skills.

  • SKILL.md
  • references/data_sources.md
  • references/scoring_methods.md
  • scripts/keyword_sentiment.py
  • scripts/sentiment_scanner.py

Open the folder on GitHubat commit 981e1d7

Compare with similar skills

Sentiment Analysis 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.

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Scrapecreators APIScrapeCreators/social-media-research-skills3.3k1 repos~4kAutomated safety check: NotesMIT
Influencer Discoverytigerless-labs/influencer-discovery211—~2.5kAutomated safety check: NotesNone

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Works with

Categories

Questions about Sentiment Analysis

What does Sentiment Analysis do?

Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking. Sentiment Analysis is an agent skill from agiprolabs/claude-trading-skills.

When should I use Sentiment Analysis?

Sentiment Analysis fits situations like: tasks that involve Customer feedback analysis; tasks that involve Smart contracts; tasks that involve Influencer and creator marketing.

How do I install Sentiment Analysis in Claude Code?

Run `npx skills add agiprolabs/claude-trading-skills --skill sentiment-analysis -a claude-code`. Or copy the skill folder (skills/sentiment-analysis in agiprolabs/claude-trading-skills) into .claude/skills/sentiment-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Sentiment Analysis in Codex?

Run `npx skills add agiprolabs/claude-trading-skills --skill sentiment-analysis -a codex`. Or copy the skill folder (skills/sentiment-analysis in agiprolabs/claude-trading-skills) into .agents/skills/sentiment-analysis in your project. Codex loads it when a task matches its description.

Can I use Sentiment Analysis 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 agiprolabs/claude-trading-skills --skill sentiment-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sentiment-analysis, .gemini/skills/sentiment-analysis, .github/skills/sentiment-analysis and .opencode/skills/sentiment-analysis in your project.

What does Sentiment Analysis need to run?

Going by SKILL.md and its folder, Sentiment Analysis needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Sentiment Analysis 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 Sentiment Analysis 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Sentiment Analysis use?

Sentiment Analysis 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 Sentiment Analysis use?

About 2.4k tokens (SKILL.md is roughly 9.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.1k tokens, read only when the agent opens those files.

What are the alternatives to Sentiment Analysis?

Skills that share tags, products or a category with Sentiment Analysis: Gmgn Track (GMGNAI/gmgn-skills, 604 stars), Customer Research (Nexus-JPF/note-companion, 870 stars), Octolens (sundial-org/awesome-openclaw-skills, 663 stars) and Scrapecreators API (ScrapeCreators/social-media-research-skills, 3.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sentiment Analysis?

agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.

Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.