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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).

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#Community

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

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#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.

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#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.

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#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.

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#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 using --platform all. Cursor Cloud Agents, remote SSH sessions, and other remote environments do not receive local user skills from ~/.cursor/skills; place the skill in the repository's .cursor/skills/openmobius-skill/ for those environments.

WorkBuddy is not CodeBuddy. Its public documentation does not define a fixed filesystem directory that a third-party installer can write to for automatic discovery, so --platform all intentionally excludes it. Choose the workflow that matches your goal:

Build this repository's local-import ZIP with:

python3 scripts/build_workbuddy_package.py \
  --output /tmp/openmobius-skill-workbuddy.zip

The WorkBuddy Skill format currently accepts .zip packages up to 3 MB. The builder uses the conservative 3,000,000-byte boundary and fails before replacing an existing artifact if the package is too large. Local installation is complete only when the import succeeds and the Skill appears under Installed. A successful Open Platform parse or submission does not by itself mean that the Skill is installed locally or published in the marketplace.

--platform workbuddy --target-dir <path> is available only for developer staging and validation. It does not register a locally discoverable WorkBuddy Skill and is not the normal installation route.

To fit that package-size boundary without discarding attributable knowledge, the ZIP uses a checksummed compact corpus that reconstructs all 2,144 School projections and all 18,645 exact-source evidence records. It supports lexical BM25/exact-alias retrieval with hard School and source filters using the system Python alone. A manifest binds the compact corpus, School registry, and alias map by size and SHA-256, allowing the standard retriever to run even when a read-only host forbids first-run lock-file creation. The canonical fused-card layer, vector index, embedding cache, and model seed are intentionally omitted; canonical and auto/hybrid/semantic routes fail closed in the WorkBuddy package. The ZIP does not bundle Python, create a virtual environment, or install packages. Script-backed features require Python 3.10+. WorkBuddy 4.6.3 and later can detect missing Python/Node.js from Settings and offer one-click installation; verify that the installed Python still meets this Skill's 3.10+ minimum. If no suitable Python launcher is available, script-backed knowledge/market operations must be reported unavailable. Q&A and text-market workflows need only the standard library, while PNG rendering and image annotation additionally require host-provided Playwright/Chromium or Pillow, respectively.

The implementation follows the common Agent Skills specification and each host's official documentation: Claude Code, Codex, OpenClaw, Hermes, Cursor, WorkBuddy local Skill guide, WorkBuddy Skill format/marketplace, WorkBuddy Open Platform, and WorkBuddy changelog.

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#Features

#Knowledge base — 726 concepts + 1282 cases

Distilled from 300+ teaching videos and live lessons across 12 curated source collections, cross-merged with model-audited term fusion and content-level deduplication. Across concept and case cards there are 14 top-level School/category labels: ICT, SMC, Price Action, Indicator Based, ChanLun (缠论), Risk Management, General, Order Flow, Volume Analysis/VSA, Elliott Wave, Wyckoff, The Strat, On-chain, and Market Structure. The registry exposes 15 retrievable labels in total: those 14 top-level labels plus the non-canonical, source-evidence-derived Scalping category. Each concept card carries: identification rules, trading implications, common mistakes, related concepts, per-source definitions. Each case card carries: market context, key observation, analysis steps, lessons, and source time-range provenance. Retrieved via local ChromaDB + multilingual nomic-embed-text-v1.5 — no API key needed for retrieval.

The index exposes three layers with different roles:

School and evidence content is included only when it can be safely attributed. Ambiguous cross-School fused rules are skipped and counted, not guessed. See knowledge-base architecture for the data model, registry, schemas, and rebuild process.

Each School/evidence record now has an embedding of its own scoped document; vectors are cached by exact content hash and model identity, so incremental updates embed only misses. v2 queries default to hybrid retrieval: BM25 and semantic candidates are generated inside the hard-filtered scope and fused by reciprocal rank, while exact canonical-term/alias matches remain first. Use --search-mode lexical for a model-free search or --search-mode semantic for the vector-only baseline.

#Real-time data + 60+ indicators

Crypto (Binance, Bybit, OKX, Hyperliquid), China A-shares, Hong Kong stocks, US stocks, forex. Each indicator carries built-in analysis dimensions (summary_focus) that the agent reads to structure its answer rather than dumping raw numbers.

#Two chart-generation paths

#Intent routing + analysis-profile routing

The SKILL.md description field triggers on natural-language questions. The skill first identifies the user's intent, then resolves the independently selected analysis profile before executing the workflow.

Intent and analysis profile are separate: the same Q&A workflow can run in strict, augment, or compare mode, while the capability gate decides whether a requested market workflow has a native analyzer. An annotation follow-up inherits the prior route; changing a selector explicitly causes a fresh analysis rather than relabeling the previous result.

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#Roadmap

Knowledge base

• ICT/SMC coverage completion — Rounds 1–2 distilled the ICT trunk plus SMC supplements from 300+ teaching videos; upcoming rounds complete ICT sub-schools (Inner Circle Mentorship, Silver Bullet, Power of 3 variants) and full SMC coverage.
• Fundamental knowledge base — interpretation methodologies for news, policy reads, economic releases (CPI / NFP / FOMC) and earnings seasons.
• Multi-school coverage deepening — Wyckoff, VSA/Volume Analysis, Price Action, ChanLun (缠论), and other Schools are already retrievable; upcoming rounds deepen their coverage, provenance, and attribution quality.
• Native multi-school analyzers — knowledge scope is not the same as a market-analysis engine. New School-native profiles and overlays will be added before market compare expands beyond Q&A.

Indicators & tools

• Expanded SMC indicator coverage — the built-in SMC structural indicator covers BOS/CHoCH, Order Blocks, FVGs, equal H/L, premium- discount zones and strong/weak pivot labels today. Upcoming: Killzone windows, Stop Run / Inducement events, and per-event probability scoring as computable signals.

Access surfaces

• Non-CLI entry points — chat-bot integrations for users who don't run a coding agent, so the knowledge base is reachable without the CLI.

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#Architecture

OpenMobius-skill/
├── SKILL.md                          # main entry (LLM reads this)
├── SKILL.body.md                     # shared body (platform-neutral)
├── platforms/                        # per-platform frontmatter
│   └── claude-code.yaml / codex.yaml / openclaw.yaml / hermes.yaml / cursor.yaml / workbuddy.yaml
├── agents/
│   └── openai.yaml                      # Codex UI and invocation metadata
├── workflows/
│   ├── qna.md / analyze.md / annotate.md / klines.md  # intent workflows
│   └── analysis_profiles.md          # route contract + capability gate
├── scripts/                          # CLI tools
│   ├── kb_retrieve.py                # hard-scoped hybrid retrieval
│   ├── kb_klines.py                  # API client + feature extraction
│   ├── kb_draw_annotation.py         # PIL annotation
│   ├── kb_phase_b_to_c.py            # analysis JSON → annotated PNG
│   ├── build_knowledge_v2.py         # audit/export School + source-evidence records
│   ├── build_index.py                # build all three vector collections
│   ├── export_v2_embedding_seed.py   # publish verified native-vector shards
│   ├── build_workbuddy_package.py    # deterministic WorkBuddy local-import ZIP builder
│   ├── evaluate_retrieval.py         # reproducible retrieval benchmark
│   ├── kb_doctor.py                  # env health check
│   ├── chart_render/                 # lightweight-charts + headless chromium
│   └── _lib/                         # embedder + retriever
├── evals/                            # versioned retrieval cases + baseline reports
├── knowledge_base/                   # cards + registry + schemas + release seed
├── install.py                        # cross-platform installer
└── README.md / INSTALL.md

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#Update / Uninstall

# Update
python3 install.py --update
python3 install.py --update --rebuild-index    # also rebuild vector index

# Uninstall the entire self-contained platform install (.venv + index included)
python3 install.py --uninstall
python3 install.py --uninstall --platform all  # Linux/macOS: all five local hosts

# Full purge (also delete shared chromium + nomic caches — these may be
# used by other projects on your machine, so confirm before running)
python3 install.py --uninstall --purge --yes-i-know

--full is still accepted for backward compatibility, but is deprecated and has no effect: standard uninstall already removes .venv and the vector index.

See INSTALL.md for all flags.

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#Troubleshooting

.venv/bin/python scripts/kb_doctor.py

Reports the state of: venv, deps, nomic model, vector index, CJK fonts, skill registration, API connectivity.

Common issues:

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#License

Apache 2.0 — see LICENSE. Third-party components: see ATTRIBUTION.md.

#Contributing

Issues and PRs welcome at https://github.com/MobiusQuant/OpenMobius-skill/issues.