Files
librefang-registry/agents/analyst/agent.toml
T
EvanandClaude Opus 4.6 8f2244eb6f chore: remove router agent (#5)
* chore: remove router agent

builtin:router has been replaced by LLM intent routing in the kernel.
Assistant is now the sole entry point — see librefang/librefang#1336.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style: format all TOML files with taplo

Fix CI taplo format check by running `taplo fmt` on all 132 TOML files.

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Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-21 03:26:11 +09:00

69 lines
1.8 KiB
TOML

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