Phoenix Iterate
v1.0.1AI-driven quantitative strategy iteration workflow — a complete loop of briefing, hypothesis, code generation, constitutional scan, backtest submission, fore...
⭐ 0· 101·0 current·0 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
medium confidencePurpose & Capability
Name/description match the actual behavior: the skill coordinates strategy briefings, code scans, backtest submission/monitoring (via a companion skill), diagnostics, and lesson recording. Required binary (python3) is appropriate. It declares the companion skills it depends on, which is coherent for backtest submission and forensic analysis.
Instruction Scope
SKILL.md instructs the agent to run the included orchestrator.py subcommands, run a code scanner against strategy files, and invoke sibling CLIs (../backtest-poller/cli.py and ../qc-deep-feature-forensics/deep_forensics.py). Those actions are within the stated purpose. Note: the skill reads/writes local project files (memory_dir results, lessons.json, bans.json) and expects companion skills to perform networked backtests — review those companion skills' behavior and what data is sent externally.
Install Mechanism
No install spec (instruction-only) reduces installer risk. There is a requirements.txt (pandas>=1.5.0) but no automatic installer; the user must provide dependencies. No downloads or external installers are embedded in the skill.
Credentials
The skill requests no environment variables or credentials. It writes to local directories (./memory, ./results by default) which is appropriate for storing lessons and scan bans. It delegates any credentialed network access (backtest submission) to companion skills, so it itself does not require unrelated secrets.
Persistence & Privilege
always is false and the skill does not request elevated or persistent platform-wide privileges. It persists its own memory files (lessons.json, bans.json) within its configured memory_dir, which is consistent with its purpose.
Assessment
This skill appears internally consistent for orchestrating quantitative strategy iterations. Before installing: (1) review the full orchestrator.py (the provided snippet is large but truncated here) to confirm it makes no unexpected network calls or subprocess invocations; (2) install and inspect the companion skills (backtest-poller and qc-deep-feature-forensics) because those will likely require credentials and perform networked backtests — do not hand over secrets unless you trust those companion packages; (3) be aware the skill will create and modify local files (memory/lessons.json, bans.json, results) under your project directory; (4) ensure pandas is available in your environment. If you need higher assurance, share the rest of orchestrator.py (or run a code review) so I can re-evaluate with the complete source.Like a lobster shell, security has layers — review code before you run it.
latestvk972brvnbx860kvavrfzmszsrs833jsq
License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🔥 Clawdis
Binspython3