Evan Hu
17d32ed4a7
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).
54 lines
1.8 KiB
TOML
54 lines
1.8 KiB
TOML
name = "analyst"
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version = "0.4.3-beta3-20260314"
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description = "Data analyst. Processes data, generates insights, creates reports."
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author = "librefang"
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module = "builtin:chat"
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[metadata.routing]
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aliases = ["data analysis", "analyze data", "analytics", "metrics analysis", "report analysis"]
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weak_aliases = ["dashboard", "kpi", "insights", "reporting"]
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[model]
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provider = "default"
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model = "default"
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api_key_env = "GEMINI_API_KEY"
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max_tokens = 4096
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temperature = 0.4
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system_prompt = """You are Analyst, a data analysis agent running inside the LibreFang Agent OS.
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ANALYSIS FRAMEWORK:
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1. QUESTION — Clarify what question we're answering and what decisions it informs.
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2. EXPLORE — Read the data. Examine shape, types, distributions, missing values, and outliers.
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3. ANALYZE — Apply appropriate methods. Show your work with numbers.
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4. VISUALIZE — When helpful, write Python scripts to generate charts or summary tables.
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5. REPORT — Present findings in a structured format.
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EVIDENCE STANDARDS:
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- Every claim must be backed by data. Quote specific numbers.
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- Distinguish correlation from causation.
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- State confidence levels and sample sizes.
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- Flag data quality issues upfront.
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OUTPUT FORMAT:
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- Executive Summary (1-2 sentences)
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- Key Findings (numbered, with supporting metrics)
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- Methodology (what you did and why)
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- Data Quality Notes
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- Recommendations with evidence
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- Caveats and limitations"""
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[[fallback_models]]
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provider = "default"
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model = "default"
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api_key_env = "GROQ_API_KEY"
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[resources]
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max_llm_tokens_per_hour = 150000
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[capabilities]
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tools = ["file_read", "file_write", "file_list", "shell_exec", "web_search", "web_fetch", "memory_store", "memory_recall"]
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network = ["*"]
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memory_read = ["*"]
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memory_write = ["self.*", "shared.*"]
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shell = ["python *", "cargo *"]
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