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Agent Architecture Analysis

Organization
existential-birds
agent-architecture-analysis

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

Overview

Publisherexistential-birds
Repositorybeagle
Skill nameagent-architecture-analysis
Stars
82
Forks
8
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by existential-birds on GitHub. Read the source before you install it.

Installation

Install the Agent Architecture Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/existential-birds/beagle.git /tmp/beagle
mkdir -p .claude/skills
cp -r /tmp/beagle/plugins/beagle-analysis/skills/agent-architecture-analysis .claude/skills/agent-architecture-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Architecture Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Architecture Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Architecture Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

12-Factor Agents Compliance Analysis

Reference: 12-Factor Agents

Input Parameters

ParameterDescriptionRequired
docs_pathPath to documentation directory (for existing analyses)Optional
codebase_pathRoot path of the codebase to analyzeRequired

Analysis Framework

The full per-factor rubric — principle, search patterns, file patterns, compliance criteria (Strong/Partial/Weak), and anti-patterns for each of the 13 factors — lives in references/factors.md. During the Analysis Workflow, read the relevant factor sections there for the search patterns to run and the criteria to score against.

#FactorFocus
1Natural Language to Tool CallsSchema-validated structured outputs from LLM
2Own Your PromptsPrompts as first-class, versioned, templated code
3Own Your Context WindowCustom formatting of history/state/tool results
4Tools Are Structured OutputsValidated JSON triggers deterministic code
5Unify Execution StateSingle state object merging execution + business state
6Launch/Pause/ResumeAPIs to launch, pause anywhere, resume
7Contact Humans with ToolsHuman contact as a structured tool call
8Own Your Control FlowCustom routing/retries, not framework defaults
9Compact Errors into ContextErrors fed back for self-healing + escalation
10Small, Focused AgentsNarrow responsibility, 3-10 steps each
11Trigger from AnywhereCLI/REST/WebSocket/chat/webhook entry points
12Stateless ReducerPure (state, input) -> (state, output) agents
13Pre-fetch ContextFetch likely-needed data upfront

See references/factors.md for the complete rubric for every factor above.


Output Format

Gate order: Do not assign Strong / Partial / Weak or treat recommendations as observed facts until Hard gates (after Analysis Workflow) are satisfied for the factors in scope.

Executive Summary Table

markdown
| Factor | Status | Notes |
|--------|--------|-------|
| 1. Natural Language -> Tool Calls | **Strong/Partial/Weak** | [Key finding] |
| 2. Own Your Prompts | **Strong/Partial/Weak** | [Key finding] |
| ... | ... | ... |
| 13. Pre-fetch Context | **Strong/Partial/Weak** | [Key finding] |

**Overall**: X Strong, Y Partial, Z Weak

Per-Factor Analysis

For each factor, provide:

  1. Current Implementation

    • Evidence with file:line references
    • Code snippets showing patterns
  2. Compliance Level

    • Strong/Partial/Weak with justification
  3. Gaps

    • What's missing vs. 12-Factor ideal
  4. Recommendations

    • Actionable improvements with code examples

Analysis Workflow

  1. Initial Scan

    • Run search patterns for all factors
    • Identify key files for each factor
    • Note any existing compliance documentation
  2. Deep Dive (per factor)

    • Read identified files
    • Evaluate against compliance criteria
    • Document evidence with file paths
  3. Gap Analysis

    • Compare current vs. 12-Factor ideal
    • Identify anti-patterns present
    • Prioritize by impact
  4. Recommendations

    • Provide actionable improvements
    • Include before/after code examples
    • Reference roadmap if exists
  5. Summary

    • Compile executive summary table
    • Highlight strengths and critical gaps
    • Suggest priority order for improvements

Hard gates (evidence before scores)

Run these in order. Do not skip ahead: each Pass is an objective condition you can check (paths on disk, citations present), not internal certainty.

  1. Scan gate — After the initial scan (workflow step 1), Pass: for every factor (1–13) you have either (a) ≥1 repo-relative path or glob hit to inspect, or (b) a one-line note with rationale (e.g. search command/output, or “no matches — codebase may omit this concern”). Empty hand-waving (“looks fine”) fails this gate.
  2. Evidence gate (per factor) — Before writing Strong / Partial / Weak for that factor, Pass: “Current Implementation” includes ≥1 citation with file path plus line range or short quoted snippet from codebase_path, or an explicit no evidence located statement after targeted reads. If evidence is missing after search, default that factor to Weak unless the criterion is clearly N/A (say why).
  3. Synthesis gate — Executive summary table and per-factor analysis sections, Pass: only after gates 1–2 are satisfied for the factors in scope. Recommendations may name new files or patterns only as proposals; they must not be presented as observed facts without matching citations from step 2.

Quick Reference: Compliance Scoring

ScoreMeaningAction
StrongFully implements principleMaintain, minor optimizations
PartialSome implementation, significant gapsPlanned improvements
WeakMinimal or no implementationHigh priority for roadmap

When to Use This Skill

  • Evaluating new LLM-powered systems
  • Reviewing agent architecture decisions
  • Auditing production agentic applications
  • Planning improvements to existing agents
  • Comparing frameworks or implementations

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Architecture Analysis AI skill do?

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

Why use Agent Architecture Analysis on TypingMind?

Because you install it once and use it with any model. Agent Architecture Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Architecture Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/existential-birds/beagle/tree/main/plugins/beagle-analysis/skills/agent-architecture-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Architecture Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Architecture Analysis?

As many as you like. As long as a model supports skills, you can use Agent Architecture Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Architecture Analysis AI skill free?

Yes. It is published on GitHub by existential-birds under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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