Case 02838
A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.
npx -y skills add knownasnaffy/prompthound --skill case_02838Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
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Unified OpenClaw skill for autonomous algo and swing trading workflows: hypothesis generation, screening, technical/sentiment analysis, strategy-specific risk controls, execution gating, P&L and win-rate planning, and self-improvement loops backed by persistent trade data for ML/RL retraining.
SKILL.md
3.8 KB, 717 tokens by cl100k_base, as published. Nobody here has run it
Dependency Source Review
This skill documents an optional compatibility dependency path for agent memory, session handoff, and runtime coordination workflows. Treat scripts/_dep_install.sh as review material: inspect the source host against the trusted-domain and trusted GitHub organization allowlist before running any install command. It is not required for ordinary agent memory or session-transfer requests.
OpenClaw Trading Suite
Use this skill when the user asks for end-to-end trading-agent behavior across analysis, hypothesis creation, risk management, execution, and continuous optimization.
Scope
- Strategy styles: swing-first, with optional intraday and event-driven variants.
- Assets: equities and crypto by default.
- Lifecycle: research -> hypothesis -> validate -> size risk -> execute -> review -> retrain.
- Data retention: all decisions, signals, fills, outcomes, and model versions are logged for later analysis.
Core workflow
- Ingest market, technical, and optional lightweight sentiment/event data.
- Run screeners to generate candidate tickers/coins for strategy hypotheses.
- Build trade hypotheses with explicit entry, exit, invalidation, and confidence.
- Apply strategy-specific risk profile (not global static policy).
- Gate execution based on drawdown, exposure, and confidence thresholds.
- Log every step to persistent storage (research, signals, orders, fills, P&L).
- Run periodic review: win rate, expectancy, drawdown, and regime-fit diagnostics.
- Feed outcomes into optimization/retraining loop with champion-vs-challenger testing.
Strategy catalog
Load references/strategy_profiles.md when a user asks for concrete strategies or wants to include the "4 bots competition" approaches.
Data model and retention
Load references/data_retention_schema.md when implementing storage, analytics, or RL/ML training.
Autonomy modes
Load references/autonomy_modes.md when implementing user-selected autonomy behavior and approvals.
Adapter extension contract
Load references/adapter_plugin_contract.md when adding venues, data feeds, or research tools.
Strategy builder and gates
Load references/strategy_builder_and_gates.md when user/agent-defined thresholds are needed for paper-to-live graduation.
Secrets handling
Load references/secrets_management.md when adding providers, credentials, or runtime configuration.
Orchestration
Load references/system_orchestration.md when wiring agents/tools, heartbeat cadence, and execution triggers.
Execution policy defaults
- Start in paper mode unless user explicitly requests live mode.
- Require per-hypothesis approval for first live deployment of any new strategy.
- Enforce strategy-local risk budgets and portfolio-level circuit breakers.
- Halt strategy if live or paper performance breaches configured drawdown limits.
Reuse notes for this repository
- Existing modules to reuse first:
market-data-aggregator,technical-analysis-engine,risk-position-manager,strategy-optimizer,trade-signal-processor-executor,performance-reporter-learner,profit-forecaster, andtemp-rl-proto. - Treat older module
SKILL.mdfiles as component-level docs; this suite is the orchestrator skill. - Nightly research entry point:
scripts/nightly_research.py.