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Ganglion Synth City
by
tensorlink-dev
· GitHub ↗
· v0.1.2
300
Downloads
0
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0
Active Installs
3
Versions
Install in OpenClaw
/install skill-8
Description
Mine Bittensor Subnet 50 (Synth) with Ganglion. Covers price-path simulation, CRPS scoring, volatility estimation, backtesting, and multi-asset forecasting.
Usage Guidance
This SKILL.md is largely coherent for SN50 mining and simulation work, but it implies on-chain actions (e.g., calling `set_weights`) without declaring or explaining any wallet/credential requirements. Before installing or using: 1) confirm how chain submissions are signed and where private keys/API credentials are stored (the skill does not declare any env vars); 2) ensure you trust and control any ganglion binary/skill that will perform transactions; 3) verify the truncated documentation (the SKILL.md appears cut off) and ask the publisher how credentials are handled and whether any other skills/tools are required; 4) if you will supply wallet keys, prefer short-lived or scoped credentials and review any agent/skill autonomy settings before enabling automatic invocation.
Capability Analysis
Type: OpenClaw Skill
Name: skill-8
Version: 0.1.2
The skill bundle provides technical documentation and domain knowledge for mining Bittensor Subnet 50 (Synth) using the Ganglion tool. It contains mathematical formulas for CRPS scoring, asset coefficients, and example API workflows for price forecasting, with no evidence of malicious intent, data exfiltration, or prompt injection attacks in SKILL.md.
Capability Assessment
Purpose & Capability
Name and description (SN50 mining, price-path simulation, CRPS scoring, backtesting) align with the SKILL.md content. Required binaries (ganglion or curl) are plausible for interacting with Ganglion/HTTP endpoints. Minor mismatch: the skill references on-chain operations (e.g., `set_weights` on chain) but the manifest declares no credentials or wallet-related env vars.
Instruction Scope
SKILL.md is detailed and stays within the stated domain (data fetching from Pyth, path simulation, CRPS scoring, backtesting). It references using the `ganglion` skill/CLI for API/chain operations. It does not instruct reading unrelated system files or environment variables, but it does imply submitting weights on-chain without explaining how keys or signing are handled.
Install Mechanism
No install spec and no code files — instruction-only skill — so nothing is written to disk. This is lowest-risk from install perspective.
Credentials
The skill declares no required env vars or credentials, yet the instructions mention chain interactions (`set_weights` on chain) and use of a Ganglion tool which typically needs signing/wallet credentials. Absence of declared credentials or guidance on how signing is performed is an incoherence and potential operational risk (where would private keys come from?).
Persistence & Privilege
always is false and there are no install scripts or claims to modify other skills or system-wide config. Skill is user-invocable only and does not request permanent presence.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install skill-8 - After installation, invoke the skill by name or use
/skill-8 - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.2
- Skill renamed to "ganglion-synth-city" with updated description, reflecting integration with Ganglion.
- Documentation overhauled for clarity: covers SN50 mining, price-path simulation, CRPS scoring, volatility estimation, backtesting, and multi-asset forecasting on Bittensor Subnet 50.
- Details on validator-miner workflow, submission formats, competition structure, scoring pipeline, and asset-specific parameters added.
- New tables summarize competition parameters, per-asset coefficients, reference sigma values, and available tools.
- Includes step-by-step example workflows for both local and remote (API) use cases.
- Provides search strategies, known pitfalls, and bootstrap/project setup commands.
v0.1.1
- Renamed project to "synth-city" and updated metadata schema and formatting.
- Updated description and documentation to focus on an agentic research assistant/pipeline for Bittensor Subnet 50.
- Added details on pipeline stages, agent roles, and remote setup (including SSH tunnel and API key instructions).
- Provided specific API endpoints for all user operations, including experiment management, component registry, and market data.
- Clarified environment variable requirements and security considerations.
- Deprecated/removed legacy content specific to "ganglion-synth-city" in favor of the new synth-city-focused workflow.
v0.1.0
Initial release of Synth City — a Ganglion mining skill specifically for Bittensor Subnet 50 (Synth).
- Provides detailed domain knowledge and workflow for SN50 probabilistic price-forecasting competitions.
- Includes specification of submission format, CRPS scoring pipeline, evaluation tools, and recommended modeling strategies.
- Lists supported assets, emission rules, and per-asset coefficient impacts.
- Documents all relevant Ganglion tools for simulation, data retrieval, volatility estimation, and backtesting.
- Outlines example workflows, pitfalls to avoid, and bootstrap instructions for new miners.
Metadata
Frequently Asked Questions
What is Ganglion Synth City?
Mine Bittensor Subnet 50 (Synth) with Ganglion. Covers price-path simulation, CRPS scoring, volatility estimation, backtesting, and multi-asset forecasting. It is an AI Agent Skill for Claude Code / OpenClaw, with 300 downloads so far.
How do I install Ganglion Synth City?
Run "/install skill-8" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Ganglion Synth City free?
Yes, Ganglion Synth City is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Ganglion Synth City support?
Ganglion Synth City is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Ganglion Synth City?
It is built and maintained by tensorlink-dev (@tensorlink-dev); the current version is v0.1.2.
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