Content
71%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.
The body is a well-organized, highly actionable reference: every optimizer has a ready-to-use config block, parameter defaults, tuning guidance, and a decision tree for selection. Its weaknesses are the inlined textbook theory and correlation-matrix example that inflate the token budget without adding system-specific knowledge, and the absence of an explicit end-to-end workflow with validation checkpoints on the generated configuration.
Suggestions
Move the textbook theory sections (MPT/BL formulas, all-weather environment table, risk-budgeting math) into a one-level-deep reference file (e.g. references/theory.md) and keep only a one-line orientation plus links in SKILL.md — this trims conciseness padding and improves progressive disclosure at once.
Trim or relocate the illustrative China correlation matrix to a reference file, keeping only the 'Key patterns' bullets that drive optimizer choice.
Add a short end-to-end workflow with a validation checkpoint, e.g. '1. Pick optimizer via the decision tree 2. Write config.json 3. Verify weights sum to <= 1.0 and count >= 3 instruments before finalizing' — this adds the missing validation step to workflow clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The system-specific material (optimizer configs, parameter defaults, tuning guidance, output format) is dense and earns its tokens, but roughly a quarter of the body restates textbook concepts Claude already knows — the MPT definition ('maximize expected return for a given level of risk'), the Black-Litterman posterior formula, risk-contribution math, and the all-weather environment table. Mostly efficient with a noticeable slab of known-material explanation, fitting anchor 3 better than 2 (the padding is tabular and compact, not padded prose) and clearly below 4. | 3 / 5 |
Actionability | Copy-paste-ready `config.json` blocks for all five optimizers, per-parameter tables with defaults and concrete tuning thresholds ('<30 easily overfits', γ tuning per data frequency), a fully worked output-format template with example numbers, and an executable-shaped rebalancing snippet. The primary deliverable (optimizer configuration) is covered by specific, complete examples for every common case. The Python fragment referencing `signal_engine.py` is illustrative rather than standalone, but the actionable core — the config — is complete. | 5 / 5 |
Workflow Clarity | The optimizer-selection decision tree provides a clear, branching selection sequence and the Output Format section defines the exact deliverable, with the Notes section flagging failure modes (overfitting, lookback bounds, leverage constraint). Not 5 because there is no explicit end-to-end workflow (gather data → select → configure → validate) and no validation checkpoint on the produced configuration — the weights-sum-≤-1 check appears only as a cautionary note; not 3 because sequencing and checkpoints are substantially present rather than implicit. | 4 / 5 |
Progressive Disclosure | The body is well-sectioned with clear headers, but it is a single ~310-line file with no bundle files at all: ~90 lines of portfolio theory (MPT/BL derivations, all-weather environments) and a full correlation-matrix example are inlined material that would sit better in one-level-deep reference files. This matches anchor 3 — structure present, but content that should be separate is inline — better than 4, since no reference split exists for content this long. | 3 / 5 |
Total | 15 / 20 Passed |