Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is concise, concrete, and well-structured with actionable data models and executable setup, performing strongly on actionability and conciseness. The main gap is the absence of explicit validation/feedback checkpoints in the append-based workflows.
Suggestions
Add an explicit dedup/validation step to Fact Extraction, e.g. 'Before appending, check facts.jsonl for an existing identical or superseding fact; skip if present.'
Add a verify checkpoint to Weekly Synthesis, e.g. 'Confirm no active fact contradicts the new summary before overwriting summary.md.'
Consider a one-line note on how to recover from a malformed facts.jsonl entry, giving the batch append a small feedback loop.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and efficient with tight lists, a compact data-model spec, and minimal prose; it avoids explaining concepts Claude already knows and every section earns its place, matching the 'lean and efficient' anchor. | 3 / 3 |
Actionability | It provides a concrete JSONL data model with a full example object, exact filesystem paths, an executable 'mkdir -p life/areas/...' setup command, and specific step-by-step workflows — concrete and copy-paste ready. | 3 / 3 |
Workflow Clarity | The three workflows are clearly sequenced, but fact extraction and weekly synthesis are append/batch write operations with no explicit validation checkpoint (e.g. checking for duplicates before appending or verifying JSON), which caps workflow clarity at 2 per the rubric's batch-operations guidance. | 2 / 3 |
Progressive Disclosure | The file is a single self-contained, well-organized document with clear sections and no nested or external references, which suits this self-contained skill and meets the well-organized-sections bar for progressive disclosure. | 3 / 3 |
Total | 11 / 12 Passed |