Context Engineering is a handbook and research-oriented course about designing the information supplied to language models at inference time, including context selection, organization, orchestration, and optimization. It is for people building or studying AI agents and other systems that need to provide models with the right information for each task. The catalogue entries contain commands and instructions for using these ideas with coding-agent tools.
Borrowing it
Nothing to install: this file belongs to jasontang-ai/Context-Engineering. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jasontang-ai/Context-Engineering/main/.claude/commands/data.agent.mdgit clone --depth 1 https://github.com/jasontang-ai/Context-EngineeringWrote 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/commands/jasontang-ai/context-engineering/data)<a href="https://agentmods.dev/commands/jasontang-ai/context-engineering/data"><img src="https://agentmods.dev/badge/commands/jasontang-ai/context-engineering/data/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/commands/jasontang-ai/context-engineering/data"><img src="https://agentmods.dev/badge/commands/jasontang-ai/context-engineering/data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.02529 |
| Opus 5.5 | $0.00000 | $0.01012 |
| Sonnet 5.5 | $0.00000 | $0.00506 |
| Haiku 4.5 | $0.00000 | $0.00253 |
Grade A, and why
data 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[meta]
{
"agent_protocol_version": "2.0.0",
"prompt_style": "multimodal-markdown",
"intended_runtime": ["Anthropic Claude", "OpenAI GPT-4o", "Agentic System"],
"schema_compatibility": ["json", "yaml", "markdown", "python", "shell"],
"namespaces": ["project", "user", "team", "dataflow", "env"],
"audit_log": true,
"last_updated": "2025-07-11",
"prompt_goal": "Deliver modular, extensible, and auditable data wrangling, validation, conversion, and pipeline management—optimized for agent/human CLI and automated workflows."
}
/data.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for data transformation, validation, cleaning, conversion, and pipeline orchestration—designed for CLI/agent/human use and rigorous audit trails.
[instructions]
You are a /data.agent. You:
- Accept slash command arguments (e.g., `/data input="data.csv" op="validate" to="parquet" [email protected]`) and file refs (`@file`), plus shell/API output (`!cmd`).
- Proceed phase by phase: context/schema mapping, validation, transformation, cleaning, conversion, linkage, pipeline run, audit logging.
- Output clearly labeled, audit-ready markdown: data reports, validation logs, pipeline graphs, schema diffs, error/warning tables.
- Explicitly declare tool access in [tools] per phase.
- DO NOT skip schema/context parsing, output verification, or audit logging.
- Surface all warnings, errors, inconsistencies, and unverified transformations.
- Visualize pipeline/dataflow, transformation sequence, and audit cycles.
- Close with data summary, audit/version log, issues, and recommendations.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/data.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, dataflow, pipeline/workflow diagrams
├── [context_schema] # JSON/YAML: data/session/operation fields
├── [workflow] # YAML: data pipeline phases
├── [tools] # YAML/fractal.json: tool registry & control
├── [recursion] # Python: feedback/validation loop
├── [examples] # Markdown: sample runs, logs, argument usage
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.
- 2mo ago First seen · 282 lines · 0 tokens per session scan A d2cd13618748
data is a command published in the GitHub repository jasontang-ai/Context-Engineering (9,274 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,529 tokens. 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.
data compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| datajasontang-ai/Context-Engineering | Command | 9.3k | 8mo ago | MIT | A | 0 | Claude Code |
| bootmicrosoft/PromptKit | Command | 113 | 9d ago | MIT | A | 14 | GitHub Copilot |
| music-suno-promptfrankxai/Starlight-Intelligence-System | Command | 10 | yesterday | MIT | A | 23 | Claude Code |
| audit-promptOwl-Listener/ai-design-skills | Command | 199 | 5mo ago | MIT | A | 13 | Claude Code |
| laravel-ai-sdkjpcaparas/superpowers-laravel | Command | 157 | 4mo ago | MIT | A | 27 | Claude Code |
| vlm-ocr-evaluationscdenney/open-science-skills | Command | 69 | yesterday | none | A | 0 | Claude Code |
| dare-llm-integrationdewtech-technologies/dare-method | Command | 5 | 3mo ago | MIT | A | 0 | Claude Code |