DeerFlow is an open-source super-agent harness that coordinates sub-agents, memory, tools, sandboxes, and extensible skills to handle research, coding, and content-creation tasks that may run for minutes or hours. It is intended for long-running, multi-step work performed by AI agents. The catalogue entries are skills, agents, and instructions that support its workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bytedance/deer-flow --skill data-analysisgit clone --depth 1 https://github.com/bytedance/deer-flowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bytedance/deer-flow/data-analysis)<a href="https://agentmods.dev/skills/bytedance/deer-flow/data-analysis"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/data-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bytedance/deer-flow/data-analysis"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/data-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
- Socket warn
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.02094 |
| Opus 5.5 | $0.00028 | $0.00838 |
| Sonnet 5.5 | $0.00014 | $0.00419 |
| Haiku 4.5 | $0.00007 | $0.00209 |
Grade A, and why
data-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- data-analysis — 100% identical, 0 lines differ
- data-analysis — 100% identical, 0 lines differ
- data-analysis — 100% identical, 0 lines differ
- data-analysis — 100% identical, 0 lines differ
- data-analysis — 100% identical, 0 lines differ
- data-analysis — 100% identical, 0 lines differ
- data-analysis — 100% identical, 0 lines differ
- data-analysis — 92% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis Skill
Overview
This skill analyzes user-uploaded Excel/CSV files using DuckDB — an in-process analytical SQL engine. It supports schema inspection, SQL-based querying, statistical summaries, and result export, all through a single Python script.
Core Capabilities
- Inspect Excel/CSV file structure (sheets, columns, types, row counts)
- Execute arbitrary SQL queries against uploaded data
- Generate statistical summaries (mean, median, stddev, percentiles, nulls)
- Support multi-sheet Excel workbooks (each sheet becomes a table)
- Export query results to CSV, JSON, or Markdown
- Handle large files efficiently with DuckDB's columnar engine
Workflow
Step 1: Understand Requirements
When a user uploads data files and requests analysis, identify:
- File location: Path(s) to uploaded Excel/CSV files under
/mnt/user-data/uploads/ - Analysis goal: What insights the user wants (summary, filtering, aggregation, comparison, etc.)
- Output format: How results should be presented (table, CSV export, JSON, etc.)
- You don't need to check the folder under
/mnt/user-data
Step 2: Inspect File Structure
First, inspect the uploaded file to understand its schema:
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action inspect
This returns:
- Sheet names (for Excel) or filename (for CSV)
- Column names, data types, and non-null counts
- Row count per sheet/file
- Sample data (first 5 rows)
Step 3: Perform Analysis
Based on the schema, construct SQL queries to answer the user's questions.
Run SQL Query
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action query \
--sql "SELECT category, COUNT(*) as count, AVG(amount) as avg_amount FROM Sheet1 GROUP BY category ORDER BY count DESC"
Generate Statistical Summary
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action summary \
--table Sheet1
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 249 lines · 69 tokens per session scan A d9383f31df40
data-analysis is a skill published in the GitHub repository bytedance/deer-flow (83,916 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 2,094 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
data-analysis
Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to…
composio-delivery
Delivers the finalized analyst brief to Slack, Notion, Google Sheets, and Gmail via authenticated Composio tools. Use whenever a completed, grounded brief must be distributed and logged.
ppt-generation
Use this skill when the user requests to generate, create, or make presentations (PPT/PPTX). Creates visually rich slides by generating images for each slide and composing them into a PowerPoint file.
gridcraft
Headless spreadsheets with GridCraft (XLSX/CSV/TSV workbooks, formula evaluation, recalculation, PDF export), provisioned on demand by pi-craft. Use for "build a budget spreadsheet with formulas", "convert this xlsx to csv", "what does this formula evaluate to", "change a cell and recalculate the totals", "export the…
xlsx
Instructions for reading, creating, editing, cleaning, and converting spreadsheet files such as XLSX, XLSM, XLS, CSV, and TSV. It covers formulas, formatting, charts, macros, and data types.
document-processor
Guidance for processing documents, extracting content, and transforming structured information. Use when the user asks to process, parse, extract, or transform document content such as PDFs, Word files, or spreadsheets.