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 commands/jasontang-ai/context-engineering/readmegit clone --depth 1 https://github.com/jasontang-ai/Context-EngineeringWhat 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 | $0.00000 | $0.03152 |
| Opus 5 | $0.00000 | $0.01576 |
| Sonnet 5 | $0.00000 | $0.00630 |
| Haiku 4.5 | $0.00000 | $0.00315 |
Grade A, and why
README 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 yesterday.
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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering AgenticOS
“We shape our tools and thereafter our tools shape us.” — Marshall McLuhan
Anthropic Slash Commands | Subagents
Overview
This directory contains a growing library of modular, customizable, and extendable agents and harnesses embedded as slash commands, forming an Agentic Operating System (AgenticOS) designed for enhancing the capabilities of Claude Code and other frontier systems, such as OpenCode, Amp, Kiro, Codex, Gemini CLI, and more. Each agent implements a standardized workflow with consistent structure, enabling sophisticated context engineering across various domains.
The operating system provides a selection of agents that serve as scaffolds for context-driven AI workflows, leveraging the latest research in cognitive tools, neural field theory, symbolic mechanisms, and quantum semantics to create more capable, interpretable, and predictable AI interactions.
/command Q="query" param="value" [email protected] ...
│
▼
[context]→[specialized_phase_1]→[specialized_phase_2]→...→[synthesis]→[audit/log]
↑___________________feedback/CI___________________|
AgenticOS Library (Under Construction)
| Command | Purpose | Usage Example |
|---|---|---|
alignment.agent.md |
AI safety/alignment evaluation | /alignment Q="prompt injection" model="claude-3" |
cli.agent.md |
Terminal workflow automation | /cli "find all .log files" alias=logscan |
comms.agent.md |
Stakeholder communications | /comms Q="major outage" audience="internal" type="crisis" |
data.agent.md |
Data transformation and validation | /data input="data.csv" op="validate" [email protected] |
deploy.agent.md |
Deployment automation | /deploy target="app" env="staging" version="1.2.0" |
diligence.agent.md |
Due diligence workflows | /diligence target="acquisition" scope="tech" depth="full" |
doc.agent.md |
Documentation generation | /doc target="api" format="markdown" scope="public" |
legal.agent.md |
Legal research and analysis | /legal Q="contract review" jurisdiction="US" type="SaaS" |
lit.agent.md |
Literature review and writing | /literature Q="PEMF effect" type="review" years=3 |
marketing.agent.md |
Marketing strategy and campaigns | /marketing goal="lead gen" channel="email" vertical="SaaS" |
meta.agent.md |
Meta-level agent coordination | /meta agents="research,data" task="market analysis" |
monitor.agent.md |
System/service monitoring | /monitor service="api" period="24h" alert=true |
optimize.agent.md |
Code and process optimization | /optimize target="foo.py" area="speed" mode="aggressive" |
research.agent.md |
Research workflows | /research topic="quantum computing" depth="technical" |
security.agent.md |
Security analysis | /security target="app" scope="full" report="detailed" |
test.agent.md |
Test generation and execution | /test suite="integration" mutate=true report=summary" |
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.
- yesterday First seen · 338 lines · 0 tokens per session scan A e2bf151fff6c
README is a command published in the GitHub repository jasontang-ai/Context-Engineering (9,236 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,152 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.