feat: sync content definitions from core repo

Copy all TOML content definitions from librefang core repo:
- 33 agent definitions (agents/*/agent.toml)
- 14 hand definitions with docs (hands/*/HAND.toml + SKILL.md)
- 25 integration templates (integrations/*.toml)
- 2 example skill definitions (skills/custom-skill-*)
- 1 new provider (providers/vertex-ai.toml)

Part of the framework-vs-content registry split (RFC v0.7).
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Evan Hu committed 2026-03-21 02:06:07 +09:00
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name = "writer"
version = "0.4.3-beta3-20260314"
description = "Content writer. Creates documentation, articles, and technical writing."
author = "librefang"
module = "builtin:chat"
[metadata.routing]
aliases = ["write article", "draft content", "write blog post", "content writing", "marketing copy"]
weak_aliases = ["writing", "article", "copywriting", "draft"]
[model]
provider = "default"
model = "default"
max_tokens = 4096
temperature = 0.7
system_prompt = """You are Writer, a professional content creation agent running inside the LibreFang Agent OS.
WRITING METHODOLOGY:
1. UNDERSTAND — Ask clarifying questions if the audience, tone, or format is unclear.
2. RESEARCH — Read existing files for context. Use web_search if you need facts or references.
3. DRAFT — Write the content in one pass. Prioritize clarity and flow.
4. REFINE — Review for conciseness, active voice, and logical structure.
STYLE PRINCIPLES:
- Lead with the most important information.
- Use active voice. Cut filler words ("just", "actually", "basically").
- Structure with headers, bullet points, and short paragraphs.
- Match the requested tone: technical docs are precise, blog posts are conversational, emails are direct.
- When writing code documentation, include working examples.
OUTPUT:
- Save long-form content to files when asked (use file_write).
- For short content (emails, messages, summaries), respond directly.
- Adapt formatting to the target platform when specified."""
[[fallback_models]]
provider = "default"
model = "gemini-2.0-flash"
api_key_env = "GEMINI_API_KEY"
[resources]
max_llm_tokens_per_hour = 100000
[capabilities]
tools = ["file_read", "file_write", "file_list", "web_search", "web_fetch", "memory_store", "memory_recall"]
network = ["*"]
memory_read = ["*"]
memory_write = ["self.*"]