Retail Hype Tracker (Skill 3.1)
Detects whether retail FOMO is building on a trading pair, using social sentiment, funding rates and divergence warnings, so you don't buy the top of a hype cycle.
Read-only sentiment analysis with no external side effects or sensitive-data writes; SKILL.md discloses data sources (Nexus, funding rates, Twitter/Reddit) and a fallback path, so data flow is fairly transparent. Deducted for: no least-privilege scoping, no user-confirmation or rollback discussion, and undisclosed handling of external API credentials — risks visible but safeguards incomplete.
SKILL.md and persona.md are consistent, the tier0/v1 JSON output contract is explicit, and a funding-rate-only fallback when Nexus is disabled is declared. Deducted for: the FOMO composite formula lacks weights or computation details; no reproducing tests for this skill; failure feedback on abnormal input is undescribed — happy path plausible but edge cases thin.
Scenario is clear (retail FOMO/divergence monitoring) with direct query examples and an output contract, so semantic triggering is reasonably precise. Deducted for: thin non-fit boundaries and degraded-mode behavior; core dependencies (Nexus SocialSentiment API, Twitter/Reddit) are overseas services largely unreachable from mainland-China networks — disclosed here as a caution.
Docs are well-layered (SKILL.md + persona.md) with dependency notes, output examples and few-shots; repo carries AGPL-3.0. Deducted for: no per-skill version, changelog or known-limitations section; maintenance responsibility and update path rest on an unverified publisher; vague phrases like 'Twitter/Reddit (when available)' hide assumptions.
Goal is well defined (FOMO level + divergence warning + z-score) and output is structured JSON directly consumable downstream; few-shots show expected behavior. Deducted for: static review cannot verify any representative output; FOMO values (e.g. 88 vs 45) lack explainable computation, and marginal value over manually checking funding/social volume is not argued.
The repo has CI (lint + pytest) and test infrastructure, but no test covering the 3.1 skill path appears in the evidence; the sentiment zScore fixture is shared context, not key-path reproduction of this skill. Deducted for: all claims are author-authored with no third-party execution evidence and no cross-source corroboration.
- Core data sources (Nexus SocialSentiment API, Twitter/Reddit) are overseas services that may be unreachable from mainland-China networks; the fallback is funding-rate-only, materially degrading FOMO quality.
- FOMO composite weights and computation are undisclosed; output values lack explainability and should not be used directly as trading rationale.
- The skill has no independent version, changelog or known-limitations disclosure; publisher identity is unverified and long-term maintenance is unknown.
- This is a static source review — no code was executed and output correctness was not verified; confidence is low.
- Sentiment signals for crypto trading carry high risk; validate independently with the repo's Risk Guard and benchmarked backtests rather than acting on a single signal.
What does this skill do, and when should you use it?
Retail Hype Tracker is skill 3.1 inside the olaxbt/ai-market-maker repository (AIMM, an agentic AI hedge fund OS), located at src/agents/operator/3.1_retail_hype_tracker/. It quantifies retail euphoria on a ticker by combining Nexus SocialSentiment data, CEX perpetual futures funding rates, and Twitter/Reddit volume when available. A query like /retail_hype_tracker?ticker=BTC/USDT returns a FOMO level, a divergence warning, and a sentiment z-score. It depends on NexusDataClient for sentiment endpoints and degrades gracefully to a funding-rate-only proxy when Nexus is disabled. It suits crypto traders who want a sentiment signal inside the AIMM stack or their own research pipeline.
Runs analyze(ticker, market_data, nexus_context) and returns a Tier-0 contract result; pulls sentiment from the Nexus SocialSentiment API; reads funding rates from CEX perpetual futures; incorporates Twitter/Reddit volume when available; serves queries like /retail_hype_tracker?ticker=BTC/USDT returning FOMO level, divergence warning, and sentiment z-score; falls back to a funding-rate-only proxy when Nexus is disabled.
- A crypto swing trader watching BTC pump wants to know if the move is retail-FOMO-driven and near a euphoric top.
- An operator running the full AIMM system needs retail sentiment as one input to its multi-agent research desks.
- A perpetual futures trader worried about crowded longs uses funding-rate versus social-hype divergence as a caution check.
- A quant researcher wants a sentiment z-score as a factor or filter in backtests.
What are this skill's strengths and limitations?
- Combines multiple independent sentiment signals (social sentiment, funding rates, social volume) rather than one metric.
- Has a documented graceful fallback when Nexus is unavailable, so it doesn't break entirely.
- Structured output (Tier-0 contract, z-score, divergence warning) is easy to feed into other systems or backtests.
- Fully open source under AGPL-3.0 with auditable code.
- Core social signal depends on the Nexus SocialSentiment API; without it, only a crude funding-rate proxy remains.
- Twitter/Reddit data is only available "when available" — social coverage is not guaranteed.
- No evidence of skill-level testing; README's backtest figures describe the whole system, not this skill in isolation.
- One of 14 skills in a large monorepo with thin documentation for standalone deployment.
- AGPL-3.0 imposes source-offer obligations if you run it as a network service commercially.
How do you install this skill?
The skill ships inside the olaxbt/ai-market-maker monorepo at src/agents/operator/3.1_retail_hype_tracker/. README installation for the whole collection: git clone https://github.com/olaxbt/ai-market-maker.git and cd into it; pip install uv; install TA-Lib (e.g. conda install -y ta-lib -c conda-forge); run uv sync --extra dev and uv run pre-commit install; cp .env.example .env and add API keys (Binance Testnet + OpenAI recommended; Nexus Skills API optional). Note: the README documents OpenClaw installation for the whole openclaw package (claw install https://github.com/olaxbt/ai-market-maker), but not standalone installation of this one skill.
How do you use this skill?
Inside the full AIMM stack, query /retail_hype_tracker?ticker=BTC/USDT to get FOMO level + divergence warning + sentiment z-score. You can also ask natural-language questions like "Is retail getting euphoric on BTC?" or "Any divergence warning?". Programmatically, call analyze(ticker, market_data, nexus_context). If Nexus is disabled, the skill operates on the funding-rate-only proxy and social signals are unavailable.