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Install in OpenClaw
/install predictclaw
Description
Predict.fun skill with a PolyClaw-style CLI for markets, wallet funding, trading, positions, and hedging.
Usage Guidance
This skill largely does what it claims (markets, wallet status, vault bootstrap, trading, optional hedge analysis), but exercise caution before giving it secrets. Specific points to consider:
- Only provide the minimal environment variables for the mode you intend to use. Use fixture or read-only templates first to verify CLI behavior without secrets.
- The registry metadata lists many private keys and an LLM API key as required, but the docs state these are conditional per mode — treat the manifest's long 'required' list as an over-declaration and don't paste all keys into .env. Prefer supplying keys only when you run a flow that needs them.
- The skill can run an external subprocess (erc-mandated-mcp) and, after explicit confirmation, broadcast on-chain transactions and backfill ~/.openclaw/skills/predictclaw/.env. Review lib/mandated_mcp_bridge.py and scripts/* to confirm exactly what the subprocess receives before running any '--confirm' deployment steps.
- OPENROUTER_API_KEY enables hedge/LLM features. If you enable hedge analysis, understand that data (market context, positions) may be sent to an external LLM provider. Avoid sending private keys or other secrets to the LLM and consider using a dedicated (limited-scope) API key.
- Audit the npm package @erc-mandated/mcp and the brew 'uv' formula you will install (check publisher and release source) before one-click installs. Because vault bootstrap touches funds, ensure prerequisites and package provenance are trustworthy.
- Test in the 'template.env' (fixture) or 'template.readonly.env' modes first. Only run bootstrap/confirm commands when you intentionally want to perform chain-side actions and after confirming the code paths involved.
If you want, I can point out exact files and functions that spawn subprocesses or write .env so you can review them before installing.
Capability Analysis
Type: OpenClaw Skill
Name: predictclaw
Version: 0.1.34
PredictClaw is a legitimate and well-architected trading tool for the predict.fun platform. It handles sensitive information, including blockchain private keys and API tokens, but implements robust security practices such as automated secret redaction in logs and error messages (found in lib/config.py and lib/api.py). The skill utilizes an external MCP (Model Context Protocol) runtime via subprocess execution (lib/mandated_mcp_bridge.py) to manage 'mandated vaults'; while this is a high-privilege capability, it is used strictly for the stated purpose, employs safe command parsing with shlex, and requires explicit user confirmation for on-chain actions. Design documentation within the bundle specifically addresses risk reduction by avoiding automatic package installations or silent environment modifications.
Capability Assessment
Purpose & Capability
Name and description align with the code and docs: this is a predict.fun CLI for markets, wallets, vault bootstrap, trading, and optional hedge analysis. Requiring 'uv' and an external MCP runtime (erc-mandated-mcp) is coherent for the documented vault flows. However, the registry-level 'requires.env' list enumerates a long set of private keys and other variables (EOA, privy, authority, executor, bootstrap private keys, OPENROUTER_API_KEY, etc.) as required unconditionally, while SKILL.md & README repeatedly state these are conditional per chosen mode. That global 'required' listing is disproportionate to the described purpose and could mislead users into thinking they must provide all secrets up front.
Instruction Scope
Runtime instructions operate by reading environment variables and an installed .env in the skill directory, invoking local CLI commands (uv run python scripts/predictclaw.py ...), and calling out to the mandated MCP subprocess for vault actions. The SKILL.md documents preview/confirm/execute patterns, warns about explicit confirmations for broadcasts, and describes optional OpenRouter use for hedge analysis. The instructions do read and write the skill's .env (backfill behavior) and spawn external processes, which is expected for this functionality but increases risk if secrets are provided unnecessarily. SKILL.md does not instruct indiscriminate host file reads or exfiltration, but the codebase includes vault bootstrap/broadcast flows that will perform on-chain actions when explicitly confirmed.
Install Mechanism
Install specs are simple: brew formula 'uv' and an npm package '@erc-mandated/mcp' that provides the 'erc-mandated-mcp' launcher. Both are standard package registry mechanisms (homebrew and npm). No opaque direct downloads, shorteners, or arbitrary extract-from-URL steps were declared in the manifest.
Credentials
The manifest lists many high-sensitivity environment variables (multiple PRIVATE_KEY-style variables, authority/executor/bootstrap keys, and OPENROUTER_API_KEY) as required. The documentation makes clear most of these are mode-specific and not needed for read-only or fixture modes. Declaring them all as required in the skill metadata is disproportionate and risks accidental over-sharing of secrets. Additionally, OPENROUTER_API_KEY is only needed for optional hedge LLM features but is presented among the required envs. There is no declared primary credential, which would help make intent clearer.
Persistence & Privilege
The skill writes and reads a .env file in its own installed skill directory and may backfill .env after an explicit bootstrap execute. It does not declare 'always: true' and does not appear to modify system-wide settings or other skills. The ability to write its own skill-scoped .env is expected for this kind of tool, but users should note the backfill behavior updates files under ~/.openclaw/skills/predictclaw/.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install predictclaw - After installation, invoke the skill by name or use
/predictclaw - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.34
Make predict-account + vault funding vault-first by default, keep Predict Account as trading identity, and align CLI/docs/tests with the vault deposit flow.
v0.1.33
Make predict-account + vault funding semantics vault-first, keep Predict Account as trading identity, and align docs/CLI/tests with the vault deposit flow.
v0.1.32
Reframe user-facing vault onboarding to four modes only, add canonical predict-account + vault templates, and demote mandated-vault to an internal bootstrap compatibility flow.
v0.1.31
Clean up the remaining overlay docs so existing-vault onboarding clearly shows the minimal path first and reserves advanced vault metadata for resolution failures only.
v0.1.30
Finish overlay onboarding cleanup so existing-vault users see the minimal config path first, while advanced vault metadata is clearly deferred until automatic resolution fails.
v0.1.29
Simplify overlay onboarding around a vault-presence-first flow: use an existing vault address as the primary path, auto-resolve metadata where possible, and direct no-vault users to bootstrap first.
v0.1.28
Simplify overlay onboarding: ask first whether the user already has a vault, prefer an existing vault address as the primary path, and route no-vault users to bootstrap first.
v0.1.27
Switch onboarding docs to a mode-first structure: choose wallet mode first, then show only the minimum fields for that mode.
v0.1.26
Promote predict-account + ERC_MANDATED_* as the recommended funded-trading path and pure mandated-vault as the recommended governance/control-plane path.
v0.1.25
Align metadata wording with the published runtime surfaces after the setup-path risk reduction changes.
v0.1.24
Reduce setup-path scan risk by making mandated MCP detection safe by default, removing automatic .env edits and automatic global install from the default path, and declaring conditional runtime requirements more explicitly.
v0.1.23
Make setup safe by default: remove automatic npm install and automatic .env edits from the default path, document the external MCP runtime explicitly, and return manual env guidance for bootstrap flows.
v0.1.22
Clarify that funding-address answers must follow the active wallet mode: deposit address for predict-account/overlay, vault-first for pure mandated-vault control-plane flows.
v0.1.21
Reconcile tracked positions against remote order truth, bind overlay funding sessions to trade identity, and add public wallet continuation commands for funding and follow-up execution.
v0.1.20
Clarify funded overlay guidance so wallet status and deposit no longer suggest an unnecessary funding step when no top-up is required.
v0.1.19
Rename the EOA env contract, clarify overlay funding guidance, make manual top-up targets explicit, ignore local publish artifacts, and highlight overlay funding next steps.
v0.1.18
Clarify Predict Account overlay routing and bump the packaged release version.
v0.1.17
Add structured vault permission summaries, preview-only vault share redemption diagnostics, and clearer OpenClaw-safe vault security guidance.
v0.1.16
Auto-bridge execute-mode broadcast env, align bootstrap signer prediction with runtime execution, and clarify mainnet bootstrap flow for OpenClaw.
v0.1.15
Add default vault auto bootstrap with preview/confirm flow, MCP execute, .env backfill, and isolated mandated MCP setup verification.
Metadata
Frequently Asked Questions
What is Predictfunclaw?
Predict.fun skill with a PolyClaw-style CLI for markets, wallet funding, trading, positions, and hedging. It is an AI Agent Skill for Claude Code / OpenClaw, with 440 downloads so far.
How do I install Predictfunclaw?
Run "/install predictclaw" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Predictfunclaw free?
Yes, Predictfunclaw is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Predictfunclaw support?
Predictfunclaw is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Predictfunclaw?
It is built and maintained by walioo (@walioo); the current version is v0.1.34.
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