Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agiprolabs/claude-trading-skills --skill sentiment-analysisgit clone --depth 1 https://github.com/agiprolabs/claude-trading-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agiprolabs/claude-trading-skills/sentiment-analysis)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/sentiment-analysis"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/sentiment-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/sentiment-analysis"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/sentiment-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.02458 |
| Opus 5 | $0.00015 | $0.01229 |
| Sonnet 5 | $0.00006 | $0.00492 |
| Haiku 4.5 | $0.00003 | $0.00246 |
Grade A, and why
sentiment-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Source | Data Type | Access |
|---|---|---|
| Twitter/X | Post text, engagement, follower counts | API (paid tiers) |
| Subreddit posts, comments, upvotes | Reddit API | |
| Telegram | Channel messages, member counts | Bot API or scraping |
| Discord | Server activity, message volume | Bot integration |
| News | Headlines, article text | NewsAPI, RSS feeds |
| CoinGecko | Community stats, developer activity | Free API |
| Alternative.me | Fear & Greed Index | Free API |
| On-chain | Funding rates, exchange flows | Exchange 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:
mention_velocity = mentions_last_hour / baseline_hourly_mentions
# > 3.0 = trending, > 10.0 = viral
Sentiment Polarity — Positive vs negative tone:
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):
| Range | Label | Typical Signal |
|---|---|---|
| 0-24 | Extreme Fear | Potential accumulation zone |
| 25-44 | Fear | Below-average sentiment |
| 45-55 | Neutral | No strong directional bias |
| 56-74 | Greed | Above-average sentiment |
| 75-100 | Extreme Greed | Potential distribution zone |
Social Volume — Total mentions across platforms:
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 270 lines · 29 tokens per session scan A db62b12dda79
sentiment-analysis is a skill published in the GitHub repository agiprolabs/claude-trading-skills (354 stars, last pushed 8d ago), licensed MIT. It adds 29 tokens to every session and 2,458 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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