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ai-agents-architect

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.

Install with Tessl CLI

npx tessl i github:duclm1x1/Dive-Ai --skill ai-agents-architect
What are skills?

69

Does it follow best practices?

Validation for skill structure

SKILL.md
Review
Evals

Evaluation results

96%

48%

Product FAQ Assistant Agent

ReAct loop safeguards

Criteria
Without context
With context

Iteration limit defined

0%

100%

Loop exits on limit

0%

100%

Thought-Action-Observation structure

50%

100%

Tool errors surfaced

100%

100%

Execution is traced/logged

0%

80%

Robust output parsing

40%

80%

Terminates when stuck

37%

100%

Graceful degradation message

75%

100%

README safeguards documented

80%

100%

catalog.json used

100%

100%

Without context: $0.7304 · 2m 45s · 36 turns · 40 in / 10,724 out tokens

With context: $0.9467 · 3m 35s · 40 turns · 332 in / 13,584 out tokens

87%

-4%

Customer Support Agent Toolset

Tool specification and curation

Criteria
Without context
With context

Parameter schema present

100%

100%

Descriptive docstrings

90%

70%

Example calls in registry

20%

50%

Tools scoped to the task

100%

100%

Total tool count reasonable

100%

100%

Tool errors returned

100%

100%

TOOL_REGISTRY implemented

100%

100%

Tool selector implemented

100%

100%

Selector actually filters

100%

58%

mock_data.json used

100%

100%

Without context: $0.6618 · 2m 24s · 31 turns · 35 in / 9,869 out tokens

With context: $0.6108 · 2m 35s · 27 turns · 29 in / 9,804 out tokens

91%

34%

Automated Research Pipeline Architecture

Multi-agent justification and memory management

Criteria
Without context
With context

Planning phase described

10%

100%

Execution phase described

87%

100%

Replanning mentioned

0%

100%

Multi-agent decision justified

0%

25%

Memory is selective

83%

100%

Failure modes documented

100%

100%

Graceful degradation behavior

87%

100%

Pipeline skeleton present

100%

100%

Planner component in code

0%

100%

Memory/state structure in code

87%

100%

Architecture-code alignment

100%

100%

Without context: $0.4405 · 2m 31s · 14 turns · 19 in / 9,940 out tokens

With context: $0.7681 · 4m 6s · 27 turns · 32 in / 14,533 out tokens

Evaluated
Agent
Claude Code

Table of Contents

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