What does this skill do, and when should you use it?
Monetary Sentinel is a skill inside olaxbt/ai-market-maker (AGPL-3.0), located at src/agents/operator/1.1_monetary_sentinel/SKILL.md. It synthesizes the Fear & Greed index, FRED macro indicators (VIX, fed funds, 10y yield, trade-weighted USD, SPX, WTI), and DefiLlama stablecoin supply plus all-chain TVL to classify the macro regime as Risk-On or Risk-Off and produce a liquidity score. Data is strictly read as-of the bar date with no later prints, making it backtest-safe. It does not execute trades; it is a macro-state judgment layer meant to feed larger trading systems.
- Exposes analyze(ticker, market_data, nexus_context) as a Tier-0 contract and answers direct queries like "What's the current macro regime for BTC?" or "Is there a liquidity crisis signal?"
- Reads Fear & Greed (0–100), FRED data (VIX, fed funds, 10y yield, trade-weighted USD, SPX, WTI), and DefiLlama lag-1 stablecoin supply and all-chain TVL with 7d change, strictly as-of the bar date with no later prints
- Falls back to OHLCV window return/vol as a liquidity proxy when those sources are missing
- Interprets by rules: VIX ≥32 or F&G ≤25 → Risk-Off; rising fed funds or strong DXY → tighter dollar liquidity, lean Risk-Off; stablecoin or TVL 7d ≤ −8% → on-chain drain, Risk-Off; F&G ≥75 with falling VIX and expanding stablecoins → Risk-On
- Quotes printed VIX / DXY / fed funds / 7d TVL change in reasoning instead of inventing later data
- Query via /monetary_sentinel?ticker=BTC/USDT, which returns the macro regime state, liquidity score, and reasoning
- A quant trader checks whether BTC is in a Risk-Off macro regime before sizing a position
- A backtest researcher needs a macro overlay aligned to historical bar dates to avoid lookahead bias
- A crypto fund team monitors stablecoin supply and TVL 7d changes to detect on-chain liquidity drains
- A developer building on the ai-market-maker multi-agent pipeline needs a macro-state upstream node
- A macro analyst asks a direct question like "Is there a liquidity crisis signal?" and wants reasoning citing concrete printed indicators
- Users who need trade execution — the skill only outputs regime state and a score
- Users who need a standalone install outside the ai-market-maker monorepo — standalone steps are undocumented and it depends on FRED, DefiLlama, and Fear & Greed availability (DefiLlama is lag-1)
- Strategies requiring statistically validated thresholds — the fixed −8%, 32, 75 rules have no documented statistical validation
How do you install this skill?
- This is a static source-only review; nothing was executed and confidence is low.
- Live mode depends on overseas services (FRED, DefiLlama) and an LLM API; mainland-China reachability is unverified — offline use requires the repo's pinned CSVs.
- Silent fallback to an OHLCV liquidity proxy when sources are missing degrades regime quality; documentation does not adequately flag this.
- Macro thresholds (e.g., VIX≥32, TVL 7d≤−8%) are hard-coded heuristics without methodological validation or backtest attribution; do not treat outputs as trading advice.
- Publisher identity is unverified and skill-level versioning/maintenance path is unclear; verify the latest commits before use.
- Network access
- Local filesystem
Fear & Greed index dataFRED data (VIX, fed funds, 10y yield, DXY, SPX, WTI)DefiLlama lag-1 stablecoin/TVL dataHistoricalNexusProvider macro CSVs in backtest mode
The skill ships as part of the ai-market-maker monorepo: git clone https://github.com/olaxbt/ai-market-maker.git, install uv, run uv sync --extra dev, and configure .env (Binance Testnet and OpenAI keys optional per README). Optionally run the full platform with docker compose -f docker-compose.prod.yml up --build -d. The skill file lives at src/agents/operator/1.1_monetary_sentinel/SKILL.md. Steps to install this sub-skill standalone as an OpenClaw skill are not documented in the source.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/olaxbt/ai-market-maker.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/src/agents/operator/1.1_monetary_sentinel" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- What's the current macro regime for BTC?
- Is there a liquidity crisis signal?
- /monetary_sentinel?ticker=BTC/USDT
In backtest mode it relies on data/macro/*.csv via HistoricalNexusProvider; otherwise it uses the live Nexus market_overview. If those files/endpoints are missing, it falls back to OHLCV stubs. Example query: /monetary_sentinel?ticker=BTC/USDT returns macro regime state + liquidity score + reasoning. You can also ask directly: "Is there a liquidity crisis signal?". When reasoning, quote the printed VIX / DXY / fed funds / 7d TVL change and never invent later data.
What are this skill's strengths and limitations?
- Explicit data sources (Fear & Greed, FRED, DefiLlama) with a strict as-of-bar-date, no-later-prints discipline that is backtest-safe
- Transparent, auditable interpretation rules (e.g., VIX ≥32 or F&G ≤25 → Risk-Off) with mandatory citation of printed values
- Multiple fallbacks: OHLCV proxies when macro CSVs or live endpoints are missing, reducing environment dependence
- Clean dual interface: structured Tier-0 analyze contract plus natural-language direct queries
- It does not execute trades — output is only regime state and score, so it must be paired with other components
- Depends on external data sources (FRED, DefiLlama, Fear & Greed) for availability and timeliness, and DefiLlama data is lag-1
- Fixed thresholds (−8%, 32, 75) are simple rules; the source provides no evidence they were validated statistically
- No standalone installation or test procedure is documented for this skill in isolation; setup depends on the whole monorepo
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Monetary Sentinel this page | 57 · Use with care | ★ 2.1k | 2mo ago | AGPL-3.0 |
| Liquidity Order Flow Analyst (4.2) | 47 · Use with care | ★ 2.1k | 2mo ago | AGPL-3.0 |
| TradingView MCP AI Trading Intelligence | 50 · Use with care | ★ 5.1k | 11d ago | MIT |
| OpenNews Financial News Aggregator | 47 · Use with care | ★ 2.4k | 2mo ago | MIT |
| Policy Orchestrator | 45 · Not recommended | ★ 2.1k | 2mo ago | AGPL-3.0 |
The source names no direct competitor. Within the same repo it complements the research desks (Market Scan, Technical TA, Statistical Alpha, Sentiment), owning the macro-regime dimension while they cover theirs.
How did FollowSkills review this skill?
Read-only macro-analysis agent with no external writes or credential needs; data sources (FRED, DefiLlama, Fear & Greed) are disclosed and a no-look-ahead ('never later prints') constraint is explicit, giving high data-flow transparency. Deducted for: no source-verification/hash disclosure for external feeds, and insufficient user-facing notice of degraded behavior when silently falling back to OHLCV proxies.
SKILL.md and persona.md output contracts are consistent (tier0/v1, macro_regime_state, Liquidity_Score); few-shot examples are self-consistent and fallback paths for missing files/endpoints are declared; the repo has CI and a pytest suite. Deducted for: static review cannot reproduce key paths, and failure-feedback quality on abnormal input is unverified — capped at 10 per anchors.
Trigger conditions are clear (macro regime / liquidity crisis queries), the scenario (BTC crypto macro regime classification) and output contract are well defined. Deducted for: no explicit non-fit boundaries or capability limits; core data sources (FRED, DefiLlama, LLM APIs) are overseas services with questionable mainland-China reachability — pinned CSV fallback mitigates but live-mode dependence remains.
SKILL.md + persona.md layering is clean; README thoroughly documents the data layers, MANIFEST hashes and the AGPL-3.0 license; repo-level maintenance signals (CI, SECURITY.md) exist. Deducted for: no skill-level version number or changelog, no FAQ or known-limitation section, maintenance ownership unclear (publisher unverified).
Goal is clear — output a macro regime state plus liquidity score with checkable reasoning, which beats manually aggregating several feeds. Deducted for: static review cannot verify output correctness or regime-classification quality; the reported backtest returns attach to the whole strategy, not this skill, so comparative-benefit evidence is limited — kept below the static cap.
There is a real CI workflow, a pytest suite (with nexus-context assertions), pinned data/ with MANIFEST hashes, and good fact/inference separation. Deducted for: tests cover context construction rather than this skill's core judgment path, and there is no third-party execution evidence — capped at 5 per anchors.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
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