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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.

Quality

70%

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tessl review fix ./skills/agent-orchestration-multi-agent-optimize/SKILL.md
SKILL.md
Quality
Evals
Security

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 reads like marketing-padded reference material rather than an executable skill: a fluffy Role section, buzzword-only sections, non-runnable code with undefined classes and a 'pass' stub, and a dangling 'Target Optimization: [user request]' line. The strongest parts are the 'Use/Do not use this skill when' sections and the four-step Instructions with a validation step, but everything needs tightening and grounding in runnable detail.

Suggestions

Delete the Role/Context/Core Capabilities section and merge buzzword-only sections 4, 6, 7, and 8 into the sections that carry real content — the 'AI-driven framework... cutting-edge techniques' prose and bullet lists like 'Intelligent result memoization' add tokens without actionable guidance.

Make the code executable or explicitly label it as illustrative: define or import the agents and helpers (DatabasePerformanceAgent, semantic_truncate, aggregate_performance_metrics, PerformanceTracker), import concurrent.futures, and implement CostOptimizer.select_optimal_model instead of leaving a 'pass' stub.

Turn the Instructions into a concrete workflow with explicit checkpoints (e.g., 'capture baseline metrics', 're-run benchmark after each change', 'roll back if throughput regresses >X%') and move detailed code examples and reference workflows into references/ files linked from a concise overview.

DimensionReasoningScore

Conciseness

Several sections are pure padding Claude does not need: the Role/Context section ('Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach') and sections 4, 6, 7, and 8, which are just buzzword bullet lists ('Predictive caching', 'Intelligent result memoization', 'Intelligent compromise selection'). This matches the anchor for noticeably verbose with several unnecessary or padded sections; it is above 1 because some sections (Instructions, code examples) do carry real content.

2 / 5

Actionability

Code examples look executable but are not: they reference undefined constructs (DatabasePerformanceAgent, aggregate_performance_metrics, semantic_truncate, PerformanceTracker) and CostOptimizer.select_optimal_model is a 'pass' stub. This matches the anchor for pseudocode instead of executable code with missing key details; it is above 2 because concrete skeletons and named techniques are present.

3 / 5

Workflow Clarity

The Instructions section gives a real four-step sequence ending in 'Validate improvements with repeatable tests and rollbacks', but validation checkpoints are implicit rather than built into the steps, and the Reference Workflows are generic four-item lists. This matches the anchor for steps listed with validation gaps; it is not 4 because no concrete checkpoint or feedback-loop detail is provided anywhere.

3 / 5

Progressive Disclosure

The body has consistent, well-organized section headers, but it is a ~240-line monolith with no references/ directory and no links to separate files — detailed code examples and reference workflows that belong in separate files are inlined. This matches the anchor for some structure with content that should be separate inline; the under-50-line simple-skill exception does not apply.

3 / 5

Total

11

/

20

Passed

Description

83%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.

A strong description: third person, concise, with an explicit 'Use when...' clause covering performance, throughput, and reliability triggers. The main room for improvement is more natural trigger variations (latency, cost, bottlenecks) and slightly more concrete capability wording.

DimensionReasoningScore

Specificity

The description lists several concrete capability areas — 'coordinated profiling, workload distribution, and cost-aware orchestration' — matching the anchor for several specific actions with minor gaps. It falls short of 5 because terms like 'cost-aware orchestration' remain somewhat abstract and coverage of the domain is not comprehensive.

4 / 5

Completeness

It explicitly answers both: what ('Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration') and when ('Use when improving agent performance, throughput, or reliability') with concrete trigger phrases. This directly matches the top anchor; a 4 would require the 'when' clause to be less explicit, which it is not.

5 / 5

Trigger Term Quality

'improving agent performance, throughput, or reliability' plus 'multi-agent systems' are phrases users would naturally say, giving good keyword coverage. It is not 5 because common variations like 'latency', 'bottlenecks', 'agent coordination', or 'cost' are missing.

4 / 5

Distinctiveness Conflict Risk

The 'multi-agent systems' niche with orchestration and profiling triggers is mostly distinct with only minor overlap risk against general performance-optimization or workflow skills. It is not 5 because 'optimization' broadly could collide with single-agent tuning or generic performance skills.

4 / 5

Total

17

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

rmyndharis/antigravity-skills

Path

skills/agent-orchestration-multi-agent-optimize/SKILL.md

Reviewed

Table of Contents

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