Content
63%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A rich, largely executable geopolitical-risk skill with strong domain content, concrete code, and a clear phased workflow. Its weaknesses are monolithic structure with no progressive disclosure and a length that exceeds what a SKILL.md overview should carry.
Suggestions
Split the body into one-level-deep reference files (e.g., HOTSPOTS.md, ASSET_MAPPING.md, DATA_SOURCES.md) and keep SKILL.md as a concise overview with clearly signaled links, to raise progressive_disclosure.
Trim or externalize the per-hotspot structural repetition and verbose docstrings to tighten conciseness toward a lean overview.
Add explicit validation/feedback steps in the data and backtest workflows (e.g., verify the GPR download succeeded and columns exist before computing signals) to push workflow_clarity toward 5.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 916-line body is information-dense and largely avoids explaining concepts Claude already knows, but the sheer volume and per-hotspot structural repetition could be tightened or moved out, so it is not a 4. | 3 / 5 |
Actionability | Provides mostly executable, copy-paste-ready Python (load_gpr_index, gpr_signal, oil_war_premium, query_gdelt_events, portfolio_stress_test) with specific tickers and URLs; minor gaps remain in illustrative functions (crisis_vol_strategy) and placeholder constants ('your@email.com', beta estimates 'should be updated'). | 4 / 5 |
Workflow Clarity | Clear phased sequence (Phase 1 positioning, Phase 2 trading, Phase 3 mean reversion) reinforced by the GREEN/YELLOW/ORANGE/RED signal levels and checklists; lacks explicit validate-then-retry feedback loops in the data workflows, keeping it below 5. | 4 / 5 |
Progressive Disclosure | Well-organized with clear section headers, but it is a monolithic single file with no bundle/reference files; large blocks (six hotspot deep-dives, asset-class mappings, data-source APIs) are inlined rather than split into one-level-deep references. | 3 / 5 |
Total | 14 / 20 Passed |