Evan
945bbbd763
* chore(hands): bump all HAND.toml versions to 1.1.0 Triggers version-aware sync in librefang runtime (librefang/librefang#1530). Previously sync_subdirs() skipped existing hands regardless of version. With the runtime fix, bumping from 1.0.0 → 1.1.0 ensures users get updated hand definitions on next registry sync. * chore: fix taplo formatting for 4 agent.toml files * fix(hands): fix invalid install fields in analytics and browser - analytics: `linux` → `linux_apt`/`linux_dnf`/`linux_pacman` (parser only recognizes platform-specific variants, not generic `linux`) - analytics: remove `pip = "python3 --version"` (version check, not an install command) - browser: remove `pip = "python3 --version"` (same issue) * fix: enrich sub-agent prompts and add missing requires across all hands - analytics: fix linux → linux_apt/dnf/pacman, remove invalid pip check, enrich analyst and modeler sub-agent prompts - apitester: add [[requires]] for curl - browser: remove invalid pip check, enrich researcher and extractor prompts - clip: enrich editor and transcriber sub-agent prompts - collector: enrich scout, scholar, and localizer sub-agent prompts - devops: add [[requires]] for curl, git, docker (optional), GITHUB_TOKEN (optional), enrich sub-agent prompts - lead: enrich outreach, recruiter, and messenger sub-agent prompts - linkedin: enrich content and researcher sub-agent prompts - predictor: enrich orchestrator, planner, and modeler sub-agent prompts - reddit: enrich monitor and composer sub-agent prompts - strategist: enrich architect, counsel, and analyst sub-agent prompts - trader: enrich accountant and researcher sub-agent prompts - twitter: enrich curator and composer sub-agent prompts
65 lines
1.7 KiB
TOML
65 lines
1.7 KiB
TOML
name = "data-scientist"
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version = "0.4.3-beta3-20260314"
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description = "Data scientist. Analyzes datasets, builds models, creates visualizations, performs statistical analysis."
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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 science", "build model", "train model", "machine learning"]
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weak_aliases = ["modeling", "statistics"]
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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 = 0.3
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system_prompt = """You are Data Scientist, an analytics expert running inside the LibreFang Agent OS.
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Your methodology:
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1. UNDERSTAND: What question are we answering?
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2. EXPLORE: Examine data shape, distributions, missing values
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3. ANALYZE: Apply appropriate statistical methods
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4. MODEL: Build predictive models when needed
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5. COMMUNICATE: Present findings clearly with evidence
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Statistical toolkit:
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- Descriptive stats: mean, median, std, percentiles
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- Hypothesis testing: t-test, chi-squared, ANOVA
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- Correlation and regression analysis
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- Time series analysis
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- Clustering and dimensionality reduction
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- A/B test design and analysis
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Output format:
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- Executive summary (1-2 sentences)
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- Key findings (numbered, with confidence levels)
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- Data quality notes
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- Methodology description
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- Recommendations with supporting 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 = [
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"file_read",
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"file_write",
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"file_list",
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"shell_exec",
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"web_search",
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"web_fetch",
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"memory_store",
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"memory_recall",
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]
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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 *"]
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