CtrlK
BlogDocsLog inGet started
Tessl Logo

agent-orchestration-improve-agent

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

62

1.54x
Quality

47%

Does it follow best practices?

Impact

82%

1.54x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./docs/v19.7/configuration/agent/skills_external/antigravity-awesome-skills-main/skills/agent-orchestration-improve-agent/SKILL.md
SKILL.md
Quality
Evals
Security

Evaluation results

85%

18%

Agent Performance Review: Support Bot Analysis

Performance baseline analysis and test suite design

Criteria
Without context
With context

Task Success Rate field

100%

100%

Average Corrections per Task field

100%

100%

Tool Call Efficiency field

0%

0%

User Satisfaction Score field

100%

100%

Response Latency field

100%

100%

Token Efficiency Ratio field

33%

100%

Instruction misunderstanding category

80%

100%

Output format errors category

20%

100%

Context loss category

100%

100%

Tool misuse category

100%

100%

Constraint violations category

100%

100%

Edge case handling category

0%

0%

Correction patterns analysis

85%

100%

Positive feedback patterns

80%

40%

Six test categories

33%

100%

Hallucination metric

40%

80%

30-day analysis period

100%

100%

67%

13%

Improving a Document Summarization Agent

Prompt engineering with chain-of-thought, few-shot examples, and role definition

Criteria
Without context
With context

Chain-of-thought step phrase

0%

22%

Self-verification checkpoint phrase

66%

66%

Good example Input field

60%

80%

Good example Reasoning field

0%

12%

Good example 'Why this works' field

85%

71%

Bad example 'Why this fails' field

62%

75%

Bad example 'Correct approach' field

85%

100%

Role: core purpose

80%

100%

Role: constraints section

20%

100%

Role: success criteria

60%

60%

Constitutional principle: factual accuracy

85%

85%

Constitutional principle: format validation

83%

83%

Constitutional principle: consistency

66%

83%

Critique-and-revise loop

50%

50%

Simple-to-complex ordering

20%

60%

94%

55%

Deploying an Improved Expense Reporting Agent

Agent versioning, staged rollout, and rollback planning

Criteria
Without context
With context

Version format compliance

0%

100%

MINOR for prompt improvements

40%

100%

Git-based prompt storage

100%

100%

Alpha stage at 5%

100%

100%

Beta stage at 20%

100%

100%

Canary progression to 50% then 100%

100%

87%

7-day monitoring window

0%

100%

Rollback trigger: success rate drop >10%

0%

100%

Rollback trigger: critical errors >5%

0%

16%

Rollback trigger: cost increase >20%

0%

100%

Rollback process: 5 steps

100%

100%

Success: 15% improvement threshold

0%

100%

Success: 25% corrections reduction

0%

100%

Success: cost within 5%

0%

100%

Repository
duclm1x1/Dive-Ai
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

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.