Data Analysis — LM Studio

Summarize pasted tables or CSV snippets into findings, caveats, and what to check next. Analysis only. (LM Studio system prompt + local server notes.)

Id: lm-studio/data-analysis · Slug: data-analysis · Status: Untested · Untested on this machine — recipe reviewed for safety (no secrets, no destructive commands, no auto-spend). Mark tested after you run it locally.

What is this

Prompt for LM Studio. Summarize pasted tables or CSV snippets into findings, caveats, and what to check next. Analysis only. (LM Studio system prompt + local server notes.)

How this runtime fits

This LM Studio pack is a local AI agent / local AI workflow for the parent AI Team. It is a single-prompt (or system-prompt) pack — do not force multi-agent system language here.

Parent AI Team: data-analysis · What is an AI Team?

What it does

Summarize pasted tables or CSV snippets into findings, caveats, and what to check next. Analysis only. (LM Studio system prompt + local server notes.)

Who for

Builders working on Analytics jobs who can run LM Studio themselves.

Need to run

You will run this locally or on infra you control.
No production credentials in prompts or committed files.
LM Studio installed
A GGUF/instruct model loaded

How to use

Open LM Studio and load a local instruct model.
Paste system-prompt.txt into the System Prompt field.
Apply sampler hints (temperature ~0.3).
Chat with the Example in payload; copy the structured reply.
If using the local server, keep it bound to localhost.

Limitations

Not a substitute for professional legal, medical, or investment advice.
Outputs can be wrong; human review required before publish or spend.
Stage-1 Library items are free recipes — marketplace ready-to-use teams remain separate.
Local models may omit sections; re-prompt once if needed.

Test status

Status: Untested · Untested on this machine — recipe reviewed for safety (no secrets, no destructive commands, no auto-spend). Mark tested after you run it locally.

Files

system-prompt.txt — Paste into LM Studio system prompt
preset.hints.json — Suggested sampler settings (manual)

Full prompt / config / code

# system-prompt.txt — Data Analysis (LM Studio)
You are a data analysis desk. Goal: findings from pasted numbers only.

RULES:
- Do not invent rows. If math is approximate, say so.
- Call out sample size, missing fields, and selection bias.
- No trading/investment advice. No auto-spend recommendations.
- Charts described in text only unless user provides image.

OUTPUT:
1) Dataset snapshot
2) Key findings (max 7)
3) Caveats
4) Next checks / cuts to request

# preset.hints.json (apply manually in UI; do not auto-download weights)
{
  "temperature": 0.3,
  "top_p": 0.9,
  "max_tokens": 2048,
  "repeat_penalty": 1.1,
  "notes": "Disable any tool plugins that can spend money or mutate production systems."
}

# Optional local server:
# LM Studio → Start server → OpenAI-compatible http://localhost:1234/v1
# Point your client at that base URL. No cloud key required for local weights.

Example in

Paste:
day,signups,paid
Mon,40,2
Tue,38,1
Wed,22,1
Thu,21,0
Fri,25,1
Note: Wed deploy.

Example out

Snapshot: 5 weekdays; Wed deploy coincides with signup drop.
Findings: Signups fell ~45% Wed–Thu vs Mon–Tue; paid sparse (n small).
Caveats: one week; no traffic denominator.
Next: traffic + error logs around deploy.

Model / runtime notes

Prefer models with solid instruction following. Turn off external tool plugins for this job.

Canonical Team

Parent: data-analysis · implementation_id: lm-studio/data-analysis

GitHub source

teams/data-analysis/lm-studio · commit eb87e8e049fdf9908e438305ff827c7b617505b5 · license: free-use-at-own-risk

Structural check: PASS · env: structural (structural only — does not set Verified)

Related Library items

Related marketplace Team pages

Soft thematic links only — not identity merges. Marketplace Teams ≠ Library AI Team records.

Numbers Desk (marketplace Team page — separate entity)
Use Numbers (marketplace Team page — separate entity)