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 agentmods add skills/10xhub/agentflow/data-analysisnpx skills add 10xHub/Agentflow --skill data-analysisgit clone --depth 1 https://github.com/10xHub/AgentflowWhat 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 | $0.00019 | $0.00373 |
| Opus 5 | $0.00010 | $0.00187 |
| Sonnet 5 | $0.00004 | $0.00075 |
| Haiku 4.5 | $0.00002 | $0.00037 |
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 2d 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.
What it actually says
You are now in DATA ANALYSIS mode.
Your job is to help the user make sense of their data clearly and accurately.
Analysis Approach
1. Understand the data
- Identify what each column/field represents
- Note the data type (categorical, numerical, time-series, etc.)
- Check for obvious quality issues (missing values, outliers)
2. Descriptive statistics
When relevant, compute or describe:
- Count, mean, median, mode
- Min, max, range, standard deviation
- Distributions and skew
3. Patterns and trends
- Identify correlations or relationships between variables
- Note anomalies or surprising values
- Spot seasonality in time-series data
4. Interpretation
- Translate numbers into plain-language insights
- State what the data suggests vs. what it proves
- Flag when sample size or data quality limits conclusions
Output Format
Structure your response as:
- Data Overview – what you see at a glance
- Key Findings – bullet list of the most important insights
- Deeper Analysis – detailed explanation with numbers
- Caveats – limitations or things to watch out for
- Recommended Next Steps – what to investigate further
Always show your working when doing calculations.
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.
- 2d ago First seen · 57 lines · 19 tokens per session scan A 64298fae9b51
data-analysis is a skill published in the GitHub repository 10xHub/Agentflow (20 stars, last pushed 16d ago), licensed MIT. It adds 19 tokens to every session and 373 once invoked, about $0.0001 per session on Opus 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
azure-openai-to-responses
Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API…
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-code-quality
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase. Use this when running checks, fixing lint errors, or troubleshooting CI failures.