Content
56%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, domain-dense reference with genuinely specific quantitative hooks, executable code, and a clear three-phase strategy workflow. Its weaknesses are pervasive general-knowledge padding across the hotspot sections and a complete absence of progressive disclosure — all ~900 lines are inlined in SKILL.md with no reference files.
Suggestions
Split the six hotspot deep-dives, the data-sources/API section, and the asset-class impact tables into reference files (e.g. references/hotspots.md, references/data-sources.md) and keep a concise overview with GPR signal levels and the three-phase workflow in SKILL.md.
Cut general-knowledge padding such as 'Strategic significance' background Claude already knows (exporter rankings, safe-haven currency mechanics) and keep only the skill-specific facts (tickers, thresholds, empirical magnitudes).
Make the strategy code genuinely executable: replace the descriptive `crisis_vol_strategy` dict with parameterized logic, and derive or clearly flag the hardcoded gold-model coefficients instead of embedding rough guesses.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The ~900-line body pads many sections with general knowledge Claude already has: "Russia is the world's largest natural gas exporter", "North Korea possesses nuclear weapons and ICBMs", "Gold (GLD/GC): geopolitical shock → immediate rally", and multi-paragraph 'Strategic significance' explanations for each hotspot. This matches the 2 anchor ('noticeably verbose; several unnecessary explanations or padded sections'); it is not a 3 because the padding is pervasive across all six hotspot sections rather than incidental. | 2 / 5 |
Actionability | Most guidance is executable and specific: real code for loading the GPR Index (URL included), a Bayesian disruption-probability update, GDELT/ACLED queries with parameters, and concrete tickers, thresholds ("GPR > 75th percentile", ">30% rerouting is high alert"), and stop rules. It stops short of 5 because some examples are descriptive rather than runnable — `crisis_vol_strategy(underlying, option_chain)` ignores its arguments and returns a hardcoded dict, and the gold premium model hardcodes rough coefficients ("β1 ≈ 800, β2 ≈ 15") without a fitting step. | 4 / 5 |
Workflow Clarity | The event-driven strategy is clearly sequenced as Phase 1 (early-warning signal detection with a GREEN/YELLOW/ORANGE/RED ladder and checklists), Phase 2 (crisis trading with triggers/stops), Phase 3 (mean reversion with explicit exit signals like "GPR Index falls >30% from the peak"), plus four numbered application scenarios. This matches the 4 anchor; it is not a 5 because there are no validation/feedback checkpoints (e.g., verifying data fetched correctly before trading on a signal), and not a 3 since sequence, triggers, and decision points are explicit throughout. | 4 / 5 |
Progressive Disclosure | The body is well-sectioned with clear headers, but it is a ~900-line monolith with no bundle files: the six hotspot deep-dives, the data-source/API reference, and the asset-class impact tables clearly belong in separate reference files. This fits the 3 anchor ('some structure but could be better organized; content that should be separate is inline'); it is not a 2 because structure and navigation within the file are good, and not a 4 because none of the bulk content is offloaded to references. | 3 / 5 |
Total | 13 / 20 Passed |