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.
A dense, well-organized reference skill that respects the reader's competence and gives concrete tool signatures with parameter semantics and fallback behavior for the core use cases. The main gaps are missing example calls for the agent-booster and prompt-injection tools and a few absent verification steps.
Suggestions
Add a one-line example call for agent_booster_edit_file and prompt_injection_scan (e.g., argument shape and expected response fields) so all six tools have executable guidance, not just the three sublinear/partitioning tools.
Include a brief verification step after 'npm install <package>' (e.g., re-run ecosystem_status to confirm the package is now active) to close the install-retry loop.
Add one or two natural trigger synonyms for the partitioning/ranking use cases (e.g., 'distribute tasks across teams', 'min-cut', 'most depended-on files') so users phrasing the need differently still reach the skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and dense — a selection table plus short per-topic sections where every line carries tool names, parameters, defaults, env vars, or fallback behavior. It assumes Claude's competence (e.g., 'The cut is the weakest seam, not a balanced split' adds non-obvious domain semantics rather than re-explaining known concepts), so nothing is clearly trimmable. | 5 / 5 |
Actionability | Three core tools have full copy-paste-ready signatures with parameters and defaults — 'swarm_partition_tasks({ tasks, dependencies: [{ from, to, weight }] })', 'sublinear_pagerank({ edges, damping: 0.85, top: 10 })', 'sublinear_solve({ matrix, vector, method })' — plus concrete env vars and 'npm install <package>'. However, the agent_booster_* tools and prompt_injection_scan are described only in prose or the table without example calls, leaving minor gaps. | 4 / 5 |
Workflow Clarity | Selection is clearly sequenced via the 'When to use what' table, and result interpretation has explicit checkpoints: 'Treat decision: "block" as a stop', 'libraryRejected says why', 'converged: false means no engine met the tolerance', and 'ecosystem_status first if a result reports a builtin engine you did not expect'. Minor gaps remain, such as no verification step after installing a missing package. | 4 / 5 |
Progressive Disclosure | The skill is under 50 lines with no bundle files and no need for external references; it is well-organized into a selection table and clearly headed per-topic sections (Prompt injection, Task partitioning, PageRank and linear solves, Embeddings and routing, Checks), which fits the guideline for a top structure score for short self-contained skills. | 5 / 5 |
Total | 18 / 20 Passed |