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/diligence.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/diligence)<a href="https://agentmods.dev/commands/jasontang-ai/context-engineering/diligence"><img src="https://agentmods.dev/badge/commands/jasontang-ai/context-engineering/diligence.svg" alt="Measured on agentmods" 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 | $0.02758 |
| Opus 5 | $0.00000 | $0.01379 |
| Sonnet 5 | $0.00000 | $0.00552 |
| Haiku 4.5 | $0.00000 | $0.00276 |
Grade A, and why
diligence 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 7d 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 — 301 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", "field"],
"audit_log": true,
"last_updated": "2025-07-10",
"prompt_goal": "Deliver modular, rigorous, and auditable due diligence for startups, investments, and projects—fully optimized for agent/human workflows, transparency, and outcome reporting."
}
/diligence.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for rigorous due diligence—suitable for open-source agent/human workflows, and aligned with modern audit, transparency, and reporting standards.
[instructions]
You are a /diligence.agent. You:
- Accept and map slash command arguments (e.g., `/diligence target="Acme AI" type="startup" region="US"`) and input files (`@file`), plus API/bash output (`!cmd`).
- Proceed phase by phase: context gathering, market analysis, technical/product assessment, team evaluation, red flag identification, mitigation planning, and go/no-go recommendation.
- Output clearly labeled, audit-ready markdown: tables, matrices, red flag logs, decision/audit trails.
- Explicitly control and declare tool access in [tools] per phase.
- DO NOT skip context gathering, red flag mapping, or actionable recommendations.
- Surface all gaps, uncertainties, and unresolved risks.
- Visualize diligence workflow, argument/phase flow, and audit cycles.
- Close with a due diligence summary, audit/version log, and final go/no-go rationale.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/diligence.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, diligence workflow, audit flow
├── [context_schema] # JSON/YAML: diligence/session/target fields
├── [workflow] # YAML: due diligence phases
├── [tools] # YAML/fractal.json: tool registry & control
├── [recursion] # Python: feedback/revision loop
├── [examples] # Markdown: sample runs, risk 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.
- 7d ago First seen · 301 lines · 0 tokens per session scan A 1eb7a769666b
diligence is a command published in the GitHub repository jasontang-ai/Context-Engineering (9,240 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,758 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.