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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.

44

Quality

44%

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

Quality

Content

22%

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

This skill provides a high-level skeleton for multi-agent optimization but lacks the concrete, actionable content needed to be useful. The instructions are entirely abstract with no executable code, specific tools, or measurable guidance. While it attempts progressive disclosure through 16 sub-skill references, the references are unverifiable, poorly organized, and the main body provides insufficient standalone value.

Suggestions

Add at least one concrete, executable code example in the main SKILL.md (e.g., a profiling snippet or cost-tracking setup) so the skill is actionable even without sub-files.

Replace the vague 4-step workflow with specific commands, tool invocations, and explicit validation checkpoints (e.g., 'Run `python profile_agents.py --baseline` and verify latency < threshold before proceeding').

Group the 16 sub-skill links into 3-4 logical categories (e.g., Profiling, Optimization, Cost Management, Workflows) with brief descriptions of when each is needed.

Remove the 'Role: AI-Powered Multi-Agent Performance Engineering Specialist' header and emoji decorations—these waste tokens and add no actionable value.

DimensionReasoningScore

Conciseness

The main body is relatively brief, but includes unnecessary role-play framing ('Role: AI-Powered Multi-Agent Performance Engineering Specialist') and emoji decoration. The instructions themselves are lean but quite generic, and the 'Knowledge Modules' section is essentially a table of contents with 16 sub-skill links, which is borderline excessive for a top-level overview.

2 / 3

Actionability

The instructions are entirely abstract ('Establish baseline metrics', 'Profile agent workloads', 'Apply orchestration changes') with no concrete code, commands, specific tools, or executable examples. There is nothing copy-paste ready or specific enough for Claude to act on without the sub-skill files.

1 / 3

Workflow Clarity

The four-step workflow is vague and lacks any validation checkpoints, specific commands, or feedback loops. 'Validate improvements with repeatable tests and rollbacks' is hand-wavy with no concrete mechanism described. For a skill involving potentially destructive orchestration changes, this is insufficient.

1 / 3

Progressive Disclosure

The skill does attempt progressive disclosure by linking to 16 sub-skill files, but no bundle files were provided so we cannot verify these references exist. The 16 links are excessive and poorly organized—there's no grouping or signaling about when to use which sub-skill. The numbered list reads more like a dump than a curated navigation structure.

2 / 3

Total

6

/

12

Passed

Description

67%

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 has a solid structure with both 'what' and 'when' clauses, which is its main strength. However, the capabilities listed lean toward buzzwords ('coordinated profiling', 'cost-aware orchestration') rather than concrete, specific actions. The trigger terms cover the domain but could be more natural and varied to better match how users would phrase their requests.

Suggestions

Replace abstract terms like 'coordinated profiling' and 'cost-aware orchestration' with more concrete actions, e.g., 'profile agent execution times, distribute tasks across agents, optimize API costs across agent pipelines'.

Expand trigger terms in the 'Use when' clause with more natural user language, e.g., 'Use when agents are slow, tasks need load balancing, agent pipelines have bottlenecks, or multi-agent costs need reduction'.

DimensionReasoningScore

Specificity

Names the domain (multi-agent systems) and lists some actions (coordinated profiling, workload distribution, cost-aware orchestration), but these are somewhat abstract and buzzword-heavy rather than concrete, actionable tasks like 'extract text' or 'fill forms'.

2 / 3

Completeness

Clearly answers both 'what' (optimize multi-agent systems with profiling, workload distribution, cost-aware orchestration) and 'when' (Use when improving agent performance, throughput, or reliability) with an explicit 'Use when...' clause.

3 / 3

Trigger Term Quality

Includes some relevant terms like 'multi-agent systems', 'agent performance', 'throughput', and 'reliability', but these are somewhat technical. Missing common natural variations users might say like 'agents are slow', 'scaling agents', 'agent bottleneck', 'load balancing', or 'multi-agent coordination'.

2 / 3

Distinctiveness Conflict Risk

The multi-agent focus provides some distinctiveness, but terms like 'performance', 'throughput', and 'reliability' are generic enough to overlap with general performance optimization or system monitoring skills. 'Cost-aware orchestration' adds some niche specificity but could still conflict with cost optimization or orchestration skills.

2 / 3

Total

9

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
Dokhacgiakhoa/antigravity-ide
Reviewed

Table of Contents

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