Content
86%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 guide that covers decision criteria, briefing structure, output format, and error recovery (bloated-report follow-up) with concrete specifics throughout. Its only weaknesses are a lightly padded motivational section and the absence of an explicit answer-verification checkpoint before acting on the subagent's distilled result.
Suggestions
Trim or compress the "Why this matters" section (context-window economics and the colleague/archive analogy) to one or two lines — Claude already knows this, and the tokens compete with operational guidance.
Add an explicit validation checkpoint after receiving the report, e.g., 'if the evidence paths look incomplete or the answer conflicts with what you know, send a follow-up to the same subagent before acting on it'.
State in one line how to judge when a report is 'bloated' (e.g., a size or content signal), so the existing feedback loop has a concrete trigger condition.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The bulk of the body is tight operational guidance — the brief checklist, the single-vs-parallel rule, the output spec — but there is minor trimmable over-explanation: the "Why this matters" section spends a paragraph on context-window economics Claude already knows ("Your context window is your most valuable and limited resource") plus a colleague/archive analogy. This matches the 4 anchor ("Efficient; minor instances of over-explanation that could be trimmed"); it is well above 3 (no padded sections dominating) and below 5 (the analogy and 'why' paragraph don't strictly earn their tokens). | 4 / 5 |
Actionability | For an instruction-only skill the guidance is fully concrete: an exact brief structure (exact question, where to look, read-only expectation, output format), specific model choices ("use `gpt-6-luna-medium` for simple search, and `gpt-6-luna-xhigh` for more involved requests"), a concrete output spec with example path/symbol formats (`src/session.rs:142`, `fn reconnect`), worked good/bad-fit examples, and a specific recovery action for bloated reports. Per the rubric's code-vs-instruction note, this is the instruction-only equivalent of copy-paste-ready coverage of common cases. | 5 / 5 |
Workflow Clarity | The process is clearly sequenced (when to use → spawn → brief → receive → follow up → work from the answer) and includes an explicit feedback loop ("If a report comes back bloated, send a focused follow-up to the *same* subagent asking it to tighten the answer"). It stops short of the 5 anchor because there is no explicit checkpoint for verifying the returned answer is correct/complete before acting on it — the 'after you get the answer' section assumes the distilled result suffices — leaving a minor validation gap. | 4 / 5 |
Progressive Disclosure | No bundle files exist and none are needed: the skill is a single self-contained ~65-line document with well-labeled sections (Why / When to use it / How to do it / After you get the answer), each subsection clearly headed, and no content inlined that belongs in a separate reference file. Navigation is trivial and the split is appropriate for the skill's size, matching the well-organized single-file case; it is above the 4 anchor, which requires minor organization gaps that this body doesn't have. | 5 / 5 |
Total | 18 / 20 Passed |