agent-orchestration-improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
50
Quality
37%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agent/skills/agent-orchestration-improve-agent/SKILL.mdQuality
Discovery
32%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 identifies a clear domain (agent improvement) but relies on abstract terminology rather than concrete actions. The complete absence of a 'Use when...' clause significantly weakens its utility for skill selection, and the trigger terms used are more technical than what users would naturally say.
Suggestions
Add an explicit 'Use when...' clause with natural trigger terms like 'agent not working', 'improve my agent', 'agent performance issues', 'fix agent behavior', or 'optimize agent prompts'.
Replace abstract actions with concrete specifics: instead of 'performance analysis', say 'analyze agent logs and error patterns, identify failure modes, measure success rates'.
Include file type or artifact triggers if applicable, such as 'when working with agent configuration files, system prompts, or agent evaluation results'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (agent improvement) and some actions (performance analysis, prompt engineering, continuous iteration), but these are somewhat abstract rather than concrete specific actions like 'analyze error logs' or 'rewrite system prompts'. | 2 / 3 |
Completeness | Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2, and this is weaker than that threshold. | 1 / 3 |
Trigger Term Quality | Includes some relevant terms like 'agents', 'prompt engineering', and 'performance analysis', but missing common variations users might say like 'fix my agent', 'agent not working', 'improve prompts', 'debug agent', or 'optimize agent'. | 2 / 3 |
Distinctiveness Conflict Risk | The focus on 'agents' provides some specificity, but 'prompt engineering' and 'performance analysis' could overlap with general coding skills, debugging skills, or other AI-related skills. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill functions primarily as a navigation hub to 17 sub-skills rather than a standalone actionable guide. While the progressive disclosure and organization are excellent, the main content lacks concrete, executable guidance—the instructions are abstract and the actual 'how' is entirely delegated to sub-files. The workflow mentions safety considerations but doesn't provide inline validation steps or examples.
Suggestions
Add at least one concrete, executable example in the main skill (e.g., a sample baseline metrics collection script or a specific prompt improvement pattern)
Include inline validation checkpoints in the 4-step workflow (e.g., 'Before proceeding to step 3, verify baseline metrics show X')
Provide a minimal working example of one optimization technique directly in this file rather than deferring everything to sub-skills
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably efficient but includes some unnecessary framing (extended thinking block, emoji headers) and the 'Use this skill when' sections add moderate overhead. The core instructions are lean but could be tighter. | 2 / 3 |
Actionability | The skill provides only abstract, high-level guidance with no concrete code, commands, or executable examples. Instructions like 'Establish baseline metrics' and 'Apply prompt and workflow improvements' are vague and non-actionable without the sub-skills. | 1 / 3 |
Workflow Clarity | The 4-step workflow provides a clear sequence and mentions rollback/regression testing, but lacks explicit validation checkpoints between steps. The actual workflow details are deferred entirely to 17 sub-skills without inline guidance. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear overview and well-organized one-level-deep references to 17 sub-skills. Navigation is clear with numbered, categorized links. Content is appropriately split between overview and detailed modules. | 3 / 3 |
Total | 8 / 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.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- Dokhacgiakhoa/antigravity-ide
- Commit
3395991
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.