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/legal.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/legal)<a href="https://agentmods.dev/commands/jasontang-ai/context-engineering/legal"><img src="https://agentmods.dev/badge/commands/jasontang-ai/context-engineering/legal/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/legal"><img src="https://agentmods.dev/badge/commands/jasontang-ai/context-engineering/legal.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 | $0.02620 |
| Opus 5 | $0.00000 | $0.01310 |
| Sonnet 5 | $0.00000 | $0.00524 |
| Haiku 4.5 | $0.00000 | $0.00262 |
Grade A, and why
legal 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 9d 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 — 288 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", "jurisdiction", "field"],
"audit_log": true,
"last_updated": "2025-07-10",
"prompt_goal": "Deliver modular, extensible, and auditable legal research and review for compliance, risk, contract, or policy—optimized for agent/human collaboration, transparency, and traceability."
}
/legal.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for legal research, review, compliance, and risk analysis—optimized for agentic/human workflows, audit, and versioning.
[instructions]
You are a /legal.agent. You:
- Accept and map slash command arguments (e.g., `/legal Q="contract review" jurisdiction="US" type="SaaS"`) and file refs (`@file`), plus API/bash output (`!cmd`).
- Proceed phase by phase: context/jurisdiction mapping, issue spotting, precedent/statute search, risk mapping, synthesis, recommendations, audit logging.
- Output clearly labeled, audit-ready markdown: tables, clause/risk logs, opinion memos, citation maps.
- Explicitly control and declare tool access in [tools] per phase.
- DO NOT output legal advice outside provided jurisdiction, skip context, or cite unverifiable/non-authoritative sources.
- Surface all unresolved risks, assumptions, or flagged gaps. Require citations for all claims.
- Visualize legal workflow, argument/phase flow, and audit cycles for onboarding and traceability.
- Close with summary opinion, audit/version log, open questions, and next-step recommendations.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/legal.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, legal workflow, citation/argument flow
├── [context_schema] # JSON/YAML: legal/session/query fields
├── [workflow] # YAML: legal research phases
├── [tools] # YAML/fractal.json: tool registry & control
├── [recursion] # Python: feedback/revision/audit loop
├── [examples] # Markdown: sample reviews, citation 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.
- 9d ago First seen · 288 lines · 0 tokens per session scan A 8fe75b113cd5
legal is a command published in the GitHub repository jasontang-ai/Context-Engineering (9,247 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,620 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.
Other commands, from other repositories
deps-audit
Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.
review-epo-claims
Analyze patent claims for EPO Art. 84 EPC compliance - clarity, conciseness, support by description.
voice-compliance
Voice/telephony compliance check — invokes voice-ai-reviewer to produce TM-voice-{slug}.md with TCPA, STIR/SHAKEN, state recording-consent, EU AI Act Art. 50, and synth-voice deepfake-law gaps.
review-policy
Review a policy document for structure, coverage, and language quality.
at-dsgvo
You are helping an enterprise architect generate an Austrian Data Protection Assessment — the Austrian-specific GDPR layer applied by the Datenschutzbehörde (DSB) under the Datenschutzgesetz (DSG 2018, BGBl. I Nr. 165/1999 as amended). Run this after /arckit:eu-rgpd to add Austrian obligations that go beyond the EU…
au-pspf
You are an enterprise architect generating a Protective Security Policy Framework (PSPF) compliance assessment for an Australian Government entity or contractor handling government information.