agent-orchestration-multi-agent-optimize

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

3.00x

Quality

56%

Does it follow best practices?

Impact

96%

3.00x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/agent-orchestration-multi-agent-optimize/SKILL.md

The canonical home for this skill is agent-orchestration-multi-agent-optimize in rmyndharis/antigravity-skills

Quality

Content

42%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured but bloated: fluffy marketing prose, pseudocode instead of runnable examples, and abstract validation guidance. It functions more as a high-level catalog than actionable instructions.

Suggestions

Cut the 'Role/Context/Core Capabilities' marketing prose ('advanced AI-driven framework', 'cutting-edge') and keep only concrete guidance Claude does not already know.

Make code examples executable or clearly mark them as illustrative skeletons; replace 'pass' and undefined helpers with real implementations or remove them.

Turn the four-step Instructions into a checklist with concrete validation commands (e.g., the exact regression-test command) and an explicit rollback step.

Dimension	Reasoning	Score
Conciseness	Noticeably verbose with padded, over-claiming language ('advanced AI-driven framework', 'cutting-edge AI orchestration techniques', 'holistically', 'Leveraging') and many generic bullet lists ('Adaptive optimization strategies', 'Cross-domain performance optimization') that add little Claude does not already know.	2 / 5
Actionability	Provides code-shaped examples, but they are skeletons rather than executable code: undefined helpers (semantic_truncate, aggregate_performance_metrics, PriorityQueue/PerformanceTracker) and a method body that is just 'pass'.	3 / 5
Workflow Clarity	A 4-step Instructions sequence exists and the Safety section mentions regression testing, but validation checkpoints are abstract ('repeatable tests and rollbacks') with no concrete commands; the destructive/batch-operation cap holds this at 3.	3 / 5
Progressive Disclosure	Content is organized into clear numbered sections, but it is a ~240-line monolith with no bundle files or external references, so detail that could be split out (the eight numbered deep-dives, code examples) is all inlined.	3 / 5
	Total	11 / 20 Passed

Description

70%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is solid: third-person, concise, and answers both what and when with an explicit trigger clause. Its main weakness is that the named capabilities are somewhat abstract/buzzwordy and the trigger list is narrow.

Suggestions

Replace composite phrases like 'cost-aware orchestration' with more atomic concrete actions (e.g., 'select cheaper models per subtask', 'cache repeated agent results').

Broaden the 'Use when' trigger list to include 'latency', 'agent cost', or 'context usage' to match terms already in the body.

Add a file-extension or concrete artifact cue if applicable to sharpen distinctiveness from generic optimization skills.

Dimension	Reasoning	Score
Specificity	Names the multi-agent domain plus three actions ('coordinated profiling', 'workload distribution', 'cost-aware orchestration'), but they are composite category labels rather than atomic concrete actions, so coverage is not comprehensive.	3 / 5
Completeness	Clearly answers 'what' (optimize multi-agent systems via profiling, distribution, cost-aware orchestration) and 'when' via an explicit 'Use when improving...' clause, but the 'when' is fairly narrow and could name more triggers to reach a 5.	4 / 5
Trigger Term Quality	Includes natural trigger phrases a user would say ('improving agent performance', 'throughput', 'reliability', 'multi-agent'), though a few common variations like 'latency' or 'agent cost' are absent.	4 / 5
Distinctiveness Conflict Risk	The multi-agent optimization niche is mostly distinct with specific triggers, leaving only minor overlap risk with general performance-tuning skills.	4 / 5
	Total	15 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/agent-orchestration-multi-agent-optimize/SKILL.md
Commit: 900c504

Reviewed: 14 minutes ago

Table of Contents

Discovery Implementation Validation

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