agent-builder: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Build agent from spec: code, skill, config, launchd

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Agent Builder

Takes a spec from Process Analyst and implements the agent: code, skill, config, launchd.

When to use

After Process Analyst has created a spec
"build an agent for process X"
"implement spec Y"

Input

Spec file from $AGENTS_PATH/specs/[name].spec.md

How to execute

Step 1: Read the spec

Read the spec file completely
Read the reference implementation: Email Pipeline ($GOOGLE_TOOLS_PATH/email_agent.py)
Understand the pipeline: trigger → steps → output

Step 2: Define architecture

Based on the spec, define:

agents/[name]/
├── [name]_agent.py        ← Main agent script
├── config.json            ← Configuration (paths, params)
├── README.md              ← Documentation
└── test_[name].py         ← Tests

Build rules:

One file = one step (if step is complex) or one file = entire pipeline (if simple)
Claude CLI for AI — use claude -p --model [model] instead of API key
CSV for data — read/write via pandas or csv module
Git auto-commit — if agent modifies CRM/PM data
Telegram notification — if human approval is needed
Dry-run mode — mandatory --dry-run flag
Logging — stdout for launchd, file for debug
Idempotency — re-run must not duplicate data

Step 3: Build

For each step from the spec:

Write the function/script
Handle errors according to the spec
Add logging
Add dry-run branch

Step 4: Create skill

Create skill file skills/agents/[name]-run.md with instructions on how to run the agent manually.

Step 5: Create launchd plist (if scheduled)

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "...">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.yourcompany.[name]-agent</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/bin/python3</string>
        <string>$AGENTS_PATH/[name]/[name]_agent.py</string>
    </array>
    <key>StartInterval</key>
    <integer>[seconds]</integer>
    <key>StandardOutPath</key>
    <string>/tmp/[name]-agent.log</string>
    <key>StandardErrorPath</key>
    <string>/tmp/[name]-agent-error.log</string>
</dict>
</plist>

Step 6: Hand off to Agent Tester

Notify that the agent is ready for testing.

Output

Agent code in $AGENTS_PATH/[name]/
Skill file in $SKILLS_PATH/skills/agents/
Launchd plist (if scheduled)

Examples

Reference: Email Pipeline

google-tools/
├── email_monitor.py        ← Step 1: Gmail API check
├── email_agent.py          ← Step 2: AI classify (haiku)
├── email_action_agent.py   ← Step 3: CRM match + log
└── data/
    ├── email_summaries/    ← Output: summaries
    └── email_drafts/       ← Output: draft replies

Trigger: launchd every 3600s Model: Claude haiku (classification) Output: CRM activities + PM tasks + drafts + Telegram notify

process-analyst — creates the spec
agent-tester — tests the agent
git-workflow — commit and PR

View source ↗

File metadata

name: agent-builder
description: Build agent from spec: code, skill, config, launchd

View original text

---
name: agent-builder
description: Build agent from spec: code, skill, config, launchd
---
# Agent Builder

> Takes a spec from Process Analyst and implements the agent: code, skill, config, launchd.

## When to use

- After Process Analyst has created a spec
- "build an agent for process X"
- "implement spec Y"

## Input

Spec file from `$AGENTS_PATH/specs/[name].spec.md`

## How to execute

### Step 1: Read the spec

- Read the spec file completely
- Read the reference implementation: Email Pipeline (`$GOOGLE_TOOLS_PATH/email_agent.py`)
- Understand the pipeline: trigger → steps → output

### Step 2: Define architecture

Based on the spec, define:

```
agents/[name]/
├── [name]_agent.py        ← Main agent script
├── config.json            ← Configuration (paths, params)
├── README.md              ← Documentation
└── test_[name].py         ← Tests
```

**Build rules:**

1. **One file = one step** (if step is complex) or **one file = entire pipeline** (if simple)
2. **Claude CLI for AI** — use `claude -p --model [model]` instead of API key
3. **CSV for data** — read/write via pandas or csv module
4. **Git auto-commit** — if agent modifies CRM/PM data
5. **Telegram notification** — if human approval is needed
6. **Dry-run mode** — mandatory `--dry-run` flag
7. **Logging** — stdout for launchd, file for debug
8. **Idempotency** — re-run must not duplicate data

### Step 3: Build

For each step from the spec:

1. Write the function/script
2. Handle errors according to the spec
3. Add logging
4. Add dry-run branch

### Step 4: Create skill

Create skill file `skills/agents/[name]-run.md` with instructions on how to run the agent manually.

### Step 5: Create launchd plist (if scheduled)

```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "...">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.yourcompany.[name]-agent</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/bin/python3</string>
        <string>$AGENTS_PATH/[name]/[name]_agent.py</string>
    </array>
    <key>StartInterval</key>
    <integer>[seconds]</integer>
    <key>StandardOutPath</key>
    <string>/tmp/[name]-agent.log</string>
    <key>StandardErrorPath</key>
    <string>/tmp/[name]-agent-error.log</string>
</dict>
</plist>
```

### Step 6: Hand off to Agent Tester

Notify that the agent is ready for testing.

## Output

- Agent code in `$AGENTS_PATH/[name]/`
- Skill file in `$SKILLS_PATH/skills/agents/`
- Launchd plist (if scheduled)

## Examples

### Reference: Email Pipeline

```
google-tools/
├── email_monitor.py        ← Step 1: Gmail API check
├── email_agent.py          ← Step 2: AI classify (haiku)
├── email_action_agent.py   ← Step 3: CRM match + log
└── data/
    ├── email_summaries/    ← Output: summaries
    └── email_drafts/       ← Output: draft replies
```

Trigger: launchd every 3600s
Model: Claude haiku (classification)
Output: CRM activities + PM tasks + drafts + Telegram notify

## Related skills

- `process-analyst` — creates the spec
- `agent-tester` — tests the agent
- `git-workflow` — commit and PR

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Environment variables like $AGENTS_PATH, $GOOGLE_TOOLS_PATH, and $SKILLS_PATH are referenced but not defined or documented in SKILL.md.
The skill assumes a specific macOS/launchd environment and Claude CLI, which may limit portability.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 91 GitHub stars
Stars/forks activity: 91 stars, 23 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

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Start with one small task

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: aAAaqwq/AGI-Super-Team
License: MIT
Version: 1.0.0
Last GitHub push: Sep 7, 2026
Registry updated: Sep 7, 2026
Instruction path: skills/agent-builder/SKILL.md @ 286d7deeb083

Version reported in registry metadata; check source releases before relying on it.

Quality

66/100

Promising

Trust

54/100

Agent access

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Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Source: aAAaqwq/AGI-Super-Team
Indexed by: OpenAgentSkill community index

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