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Prediction Stack Orchestrator
by
kingmadellc
· GitHub ↗
· v1.1.0
· MIT-0
296
Downloads
0
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0
Active Installs
3
Versions
Install in OpenClaw
/install prediction-stack-orchestrator
Description
Three-agent pipeline orchestrator (Kalshalyst, Eval, Executor) for automated Kalshi prediction market trading with validation loops and retry logic
Usage Guidance
Do not install or run this skill in a sensitive environment without clarification. Before proceeding, ask the publisher to: (1) explicitly list required environment variables (Kalshi API key, model API key/credentials, any DB or secrets) and explain why each is needed; (2) justify why the skill needs to read prompt-lab/home files and provide a minimal, documented config surface; (3) remove or explain any system-prompt override text in SKILL.md; (4) explain the monitor/server.py behavior and whether you should run it (it probes processes, reads local files, and serves them via an open HTTP API). If you must test, run it in an isolated sandbox or VM, audit server.py and SKILL.md fully, and never supply broad credentials to an untrusted or opaque skill.
Capability Analysis
Type: OpenClaw Skill
Name: prediction-stack-orchestrator
Version: 1.1.0
The bundle implements a complex trading orchestrator for Kalshi markets. The primary concern is the 'monitor/server.py' script, which acts as a local web server that exposes sensitive system information, including active process lists (via 'ps aux'), recent logs, and financial configuration files (Kelly criterion parameters and ensemble weights) over an unauthenticated HTTP API bound to all interfaces (0.0.0.0:3333). While these features align with the stated goal of providing a dashboard, the lack of authentication and the use of 'Access-Control-Allow-Origin: *' create a significant information disclosure risk and a broad attack surface on the host machine.
Capability Assessment
Purpose & Capability
The skill claims to orchestrate Kalshi trading (needs model access, Kalshi SDK, and API keys) but the registry metadata declares no required environment variables, no config paths, and no primary credential. The SKILL.md and included files explicitly reference Claude Opus, Kalshi SDK execution, Kelly sizing, and reading prompt-lab files (ensemble_weights.json, kelly_config.json, market_filter.json). Requiring access to local prompt-lab data and trading APIs is consistent with the feature, but failing to declare those credentials/config paths is a mismatch and reduces transparency.
Instruction Scope
SKILL.md instructs the agent to orchestrate markets, validate model outputs, manage retries, and execute trades, and it references reading local config/state (prompt-lab, home files, retry history). The pre-scan flagged a 'system-prompt-override' pattern in SKILL.md, which suggests the skill may try to manipulate agent/system prompts. The instructions (and the included server.py) also direct the code to inspect running processes (ps/pgrep) and read various files in PROMPT_LAB or the user's home directory—actions that reach beyond pure orchestration and could access sensitive local state.
Install Mechanism
There is no install spec (instruction-only), which reduces automatic disk writes and privileged installs. However, the bundle includes a monitor web UI and monitor/server.py that, if executed by the user or agent, will read local files and probe system processes and serve them over HTTP (CORS-enabled). Absence of an install step means nothing is auto-installed, but the provided code could be run manually and would then perform system introspection.
Credentials
The skill does not declare any required credentials but plainly requires/assumes access to external APIs and local configs: Claude model access (Claude Opus), Kalshi SDK/API credentials to place trades, and local prompt-lab files (eval results, ensemble weights, kelly configs). The lack of declared env vars like KALSHI_API_KEY or MODEL_API_KEY is disproportionate and obscures what secrets the skill will need or attempt to read. Additionally, server.py reads files under the user's home and PROMPT_LAB and exposes them via an HTTP API, which could expose sensitive data if run.
Persistence & Privilege
always is false and the skill is user-invocable (normal). The skill does not request persistent 'always' inclusion. However, the included monitor/server.py exposes system/process/config information via an HTTP JSON API (with Access-Control-Allow-Origin: *). If a user runs this server on a host containing secrets, it could leak them to local network clients. The skill does not attempt to modify other skills' configs in the files reviewed.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install prediction-stack-orchestrator - After installation, invoke the skill by name or use
/prediction-stack-orchestrator - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
v1.1.0: unified stack release
v1.0.1
v1.0.1 — 3-agent orchestrator for the OpenClaw Prediction Stack
v1.0.0
v1.0.0 — 3-agent orchestrator for the OpenClaw Prediction Stack
Metadata
Frequently Asked Questions
What is Prediction Stack Orchestrator?
Three-agent pipeline orchestrator (Kalshalyst, Eval, Executor) for automated Kalshi prediction market trading with validation loops and retry logic. It is an AI Agent Skill for Claude Code / OpenClaw, with 296 downloads so far.
How do I install Prediction Stack Orchestrator?
Run "/install prediction-stack-orchestrator" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Prediction Stack Orchestrator free?
Yes, Prediction Stack Orchestrator is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Prediction Stack Orchestrator support?
Prediction Stack Orchestrator is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Prediction Stack Orchestrator?
It is built and maintained by kingmadellc (@kingmadellc); the current version is v1.1.0.
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