Content
85%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 highly actionable, framework-dense rulebook: executable code for every subsystem, exact defaults, explicit error-recovery feedback loops, and a validation harness. Its weaknesses are duplicated preset/model guidance repeated across three sections and a monolithic single-file layout that inlines several reference-grade sections instead of splitting them into bundled reference files.
Suggestions
Split reference-grade sections into bundled files (e.g., references/runtime-security.md, references/llmquery.md, references/test-harness.md) and keep SKILL.md as a tight overview with one-level-deep links, matching how the skill already links external examples.
Merge the preset guidance currently repeated in "Use These Defaults", "Context Policy Presets", and "Choosing Presets, Prompt Level, And Model Size" into a single decision table (task type -> preset/budget/model) to eliminate triplicated rules.
Fold the "Do Not Generate" section into the "RLM Actor Code Rules" section as negative examples next to their positive counterparts, since every entry there restates an earlier rule.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense, imperative, framework-specific rules with no generic-concept padding (e.g., it never explains what an agent or runtime is), assuming Claude's competence throughout. It falls short of anchor 5 because preset guidance is repeated across "Use These Defaults", "Context Policy Presets", and "Choosing Presets, Prompt Level, And Model Size", and actor-code rules recur in "Do Not Generate" — trimming the duplication would tighten it. | 4 / 5 |
Actionability | Fully executable guidance throughout: a copy-paste-ready canonical agent config, runtime security recipes (new AxJSRuntime({...}) with exact option shapes), a complete runnable agent.test(...) harness with imports, and concrete actor-turn JavaScript examples. Exact defaults are stated for every option (e.g., "maxSubAgentCalls ... Default is 100"), matching anchor 5. | 5 / 5 |
Workflow Clarity | The mental model gives an explicit ordered pipeline (distiller -> executor -> responder), actor turns are a clear per-turn workflow with feedback loops ("Errors ... appear in Action Log; inspect them and fix the code on the next turn"; "If a result starts with [ERROR], inspect or branch on it"), and agent.test(...) provides an explicit validation checkpoint before full runs. The delegation decision guide acts as a checklist — matching anchor 5. | 5 / 5 |
Progressive Disclosure | Sections are clearly headed and the skill links out to full working examples ("Fetch these for full working code"), but the 505-line body inlines content that clearly belongs in separate reference files — the runtime security options reference, the llmQuery API rules, the test-harness guide, and the option layout could each be a one-level-deep reference. With no bundle files at all, this matches anchor 3 (structure exists; content that should be separate is inline). | 3 / 5 |
Total | 17 / 20 Passed |