openmobius-skill

OpenMobius-skill

Multi-school trading knowledge for AI coding agents — strict ICT/SMC market analysis by default

A SKILL.md-standard skill bringing 2008 curated knowledge cards, real-time market data, technical indicators, and chart generation to Claude Code, Codex, OpenClaw, Hermes, Cursor, and WorkBuddy.

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🤖 AI agents — when a user asks you to install this skill, follow README_AGENT.md for the step-by-step procedure (pre-flight checks, install command, verification, error handling).

Community

• Join the OpenMobius Discord community
• Join the OpenMobius Telegram community

Overview

Drop this skill into your AI coding agent and ask trading questions in plain language. Knowledge Q&A is grounded inside the selected School/source scope. Supported current-market requests pass a capability gate before adding fresh, current-turn data and computed structure; unsupported routes stop instead of silently borrowing another School's analysis.

Quick start

OPENMOBIUS_SRC="$(mktemp -d "${TMPDIR:-/tmp}/openmobius-src.XXXXXX")"
git clone https://github.com/MobiusQuant/OpenMobius-skill.git "$OPENMOBIUS_SRC"
cd "$OPENMOBIUS_SRC"
python3 install.py --platform claude-code     # or codex / openclaw / hermes / cursor
# On Linux/macOS, `all` installs all five local-path hosts. WorkBuddy uses local ZIP import.

cd "${TMPDIR:-/tmp}"
rm -rf -- "$OPENMOBIUS_SRC"                    # ✓ exact mktemp directory only

On Windows, clone into a writable directory and run py -3 install.py --platform claude-code (or use .\install.ps1).

The installer copies source files into ~/.claude/skills/openmobius-skill/ (or your chosen platform's skills dir), then in that directory:

1. Creates .venv/ and installs dependencies
2. Downloads Playwright chromium (~280 MB, into your OS's user-global cache)
3. Downloads the pinned nomic-embed-text-v1.5 weights (~547 MB / 522 MiB, into your HuggingFace cache)
4. Loads bundled canonical vectors and the verified release seed for independent School/evidence vectors, then builds and verifies all three collections. Only locally changed or missing documents are embedded and cached.
5. Generates the platform-specific SKILL.md
6. Runs a health check

Each installer-managed local copy is self-contained: it owns its own .venv and _index. The clone is just a one-shot source bundle.

First run: dependency/model downloads plus seed-accelerated index build · Subsequent runs: unchanged records use the release seed or local embedding cache

After install, try capability discovery plus the default, scoped, and composed routes directly:

# Default: strict ICT/SMC
"What is Liquidity Sweep?"
"How is ETH 4h looking?"

# Discover the installed capabilities (no market-data request)
"What analysis models can I use?"

# Select an exact School or source
"Explain 中枢 using ChanLun only"
"Explain Order Block using Wuyuan's SMC material only"

# Compose or compare
"Compare ICT and ChanLun definitions of market structure"
"Use SMC as primary and Wuyuan as a reference to analyze BTC 1h"

Other Schools can scope knowledge Q&A when attributable material exists. Current-market analysis additionally requires a native analyzer; unsupported routes stop before market-data or chart work. Capability-discovery questions read the installed registry dynamically and report native market profiles, Q&A-only lenses, knowledge categories, and the available composition modes as separate concepts; they do not fetch market data or describe a category as an analysis model.

Prerequisites: Python 3.10+. See INSTALL.md for details.

Analysis lenses and routing

The skill resolves both the user's intent and analysis route before knowledge retrieval, market-data calls, or drawing:

User request
  → choose intent: Q&A / chart analysis / annotation / K-line analysis
  → resolve mode + lens + School/source scope
  → capability gate
  → scoped knowledge retrieval
  → supported market analyzer and fresh data, when required
  → grounded answer and chart, or an explicit capability-gap response

Default: when neither the current request nor an established conversation route supplies a lens, School, source, exclusion, or composition mode, the route is strict ICT/SMC. A current explicit selector overrides an inherited preference and is never silently widened or replaced.

Composition modes

Current capability boundary

The complete installed taxonomy is deliberately broader than the native market-analysis models:

• Native market-analysis lens: ICT, SMC (one ict_smc analyzer).
• Q&A knowledge lenses: 缠论, Price Action, Order Flow, Volume Analysis, Elliott Wave, Wyckoff, The Strat.
• Knowledge categories, not analysis models: Indicator Based, Risk Management, General, On-chain, Market Structure.
• Evidence-only category: Scalping; it occurs in attributable source evidence but is not a top-level canonical-card School.

Unknown selectors, empty strict scopes, empty School/source intersections, missing exact-filter support, missing native analyzers, and market compare requests fail closed when they affect a required strict or primary branch: the skill explains the gap and does not fall back to canonical, another School/source, or default ICT/SMC. In augment, an unavailable secondary may be omitted or labelled knowledge-only while a supported primary continues.

Follow-up annotation inherits the prior analysis route. A new explicit School/source/profile requires re-evaluating the analysis; existing levels are not merely relabelled. See the full route contract and capability matrix for aliases, precedence, partial-support rules, and exact failure behavior.

Platform support

python3 install.py --platform <name>

Each local-path platform install is fully self-contained (its own .venv, its own _index). The nomic model and Playwright chromium live in your OS's user-global cache, shared across platforms — so installing on N platforms doesn't N× the download.

OPENCLAW_STATE_DIR and HERMES_HOME override the corresponding roots when set. The current OpenClaw and Hermes adapters/manifests target Linux and macOS; on Windows, select Claude Code, Codex, or Cursor explicitly instead of