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agent-orchestration-improve-agent

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

46

Quality

49%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./skills/antigravity-agent-orchestration-improve-agent/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

48%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 skill lays out a coherent, well-sequenced optimization workflow with genuinely concrete thresholds for rollback and success, but it buries that value under lengthy explanations of techniques Claude already knows and pseudocode commands against undefined tooling. Splitting detail into reference files and cutting the concept tutorials would markedly improve it.

Suggestions

Cut or drastically compress sections explaining known concepts — 2.1 chain-of-thought, 2.2 few-shot structure, 2.4 constitutional AI, 4.1 semver, 4.2 rollout stages — keeping only the agent-specific application, e.g. 'add self-verification checkpoints to the agent's system prompt'.

Replace placeholder/pseudocode blocks with real executable guidance: either document the actual commands for the referenced tools (context-manager, prompt-engineer, parallel-test-runner) or reframe as concrete steps Claude can perform directly, and drop the unfilled '[X%]' baseline template.

Move the metric taxonomies (3.3), test categories (3.1), and human evaluation protocol (3.4) into reference files (e.g. references/metrics.md, references/testing.md) and keep a lean overview with clearly signaled one-level-deep links in SKILL.md.

DimensionReasoningScore

Conciseness

The body spends substantial tokens explaining concepts Claude already knows — chain-of-thought reasoning, few-shot example design, constitutional AI self-critique, semantic versioning, and alpha/beta/canary rollout — plus padded bullet lists like 'Good Example / Input / Reasoning / Output' scaffolding, matching the 'noticeably verbose; several unnecessary explanations' anchor.

2 / 5

Actionability

There are concrete, specific elements (rollback triggers with thresholds like 'Success rate drops >10%', success criteria like '≥15% improvement', statistical thresholds), but the core instructions are pseudocode against unspecified tools ('Use: context-manager / Command: analyze-agent-performance $ARGUMENTS --days 30', 'Use: prompt-engineer / Technique: chain-of-thought-optimization') and templates use unfilled placeholders ('[X%]', '[Y]', '[1-10]'), matching the 'some concrete guidance but incomplete; pseudocode instead of executable code' anchor.

3 / 5

Workflow Clarity

The four-phase sequence (analysis → prompt improvements → testing/validation → staged rollout) is clearly ordered with most checkpoints present: A/B testing with statistical significance requirements, staged rollout percentages, a 7-day monitoring window, and explicit rollback triggers and process. It falls short of 5 only because validation steps reference tools and procedures without concrete executable commands, leaving minor gaps.

4 / 5

Progressive Disclosure

No bundle files (references/, scripts/, assets/) exist, and the entire ~350-line body is inline. Section headers provide real structure, but large blocks that belong in separate reference files — the metric taxonomies (3.3), test categories (3.1), and evaluation protocols (3.4) — are inlined, matching the 'some structure but content that should be separate is inline' anchor.

3 / 5

Total

12

/

20

Passed

Description

50%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 communicates a clear domain and purpose but relies on abstract category labels rather than concrete actions, and entirely lacks a 'when to use' trigger clause. It is serviceable but generic, sitting at the midpoint on nearly every dimension.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user wants to improve, debug, or optimize an existing agent's performance, accuracy, or reliability, or when running A/B tests or evals on agent prompts.'

Replace abstract categories with concrete actions, e.g. 'Analyzes agent failure modes and metrics, rewrites prompts with chain-of-thought and few-shot techniques, and runs A/B tests with rollback plans.'

Include natural synonyms users would say ('optimize agent', 'agent evals', 'prompt tuning') to improve trigger term coverage and distinctiveness from agent-creation skills.

DimensionReasoningScore

Specificity

The description names the domain ('existing agents') and a few action categories ('performance analysis, prompt engineering, and continuous iteration'), but these are high-level abstractions rather than concrete operations, matching the anchor for domain plus 1-2 actions without comprehensive coverage.

3 / 5

Completeness

The 'what' is stated clearly ('systematic improvement of existing agents...'), but no 'Use when...' clause or equivalent trigger guidance exists anywhere in the description, which per the judging guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

Some relevant keywords are present ('agents', 'performance', 'prompt engineering'), but natural user phrasings such as 'optimize my agent', 'improve agent accuracy', 'evals', or 'A/B test' are missing, matching the anchor for relevant keywords without common variations or synonyms.

3 / 5

Distinctiveness Conflict Risk

'Improvement of existing agents' is somewhat specific and distinguishable from agent-creation skills, but 'agents' is a broad term and the description could overlap with general prompt-writing or agent-building skills, matching the 'somewhat specific but could still overlap' anchor.

3 / 5

Total

12

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
boisenoise/skills-collections
Reviewed

Table of Contents

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