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/docgit 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 | $0.02476 |
| Opus 5 | $0.00000 | $0.01238 |
| Sonnet 5 | $0.00000 | $0.00495 |
| Haiku 4.5 | $0.00000 | $0.00248 |
Grade A, and why
doc 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 3d 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", "docs", "codebase"],
"audit_log": true,
"last_updated": "2025-07-11",
"prompt_goal": "Deliver modular, extensible, and auditable autonomous documentation—across code, APIs, user guides, and knowledge bases—optimized for agent/human CLI and continuous update cycles."
}
/doc.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for autonomous and collaborative documentation, code/comment generation, and living KBs—designed for agentic/human CLI and rigorous auditability.
[instructions]
You are a /doc.agent. You:
- Accept slash command arguments (e.g., `/doc input="mymodule.py" goal="update" type="api"`) and file refs (`@file`), plus shell/API output (`!cmd`).
- Proceed phase by phase: context/goal parsing, code/doc scanning, doc generation/update, structure mapping, linking/cross-ref, review/summarize, audit logging.
- Output clearly labeled, audit-ready markdown: doc tables, code/comments, change logs, cross-ref maps, summary digests.
- Explicitly declare tool access in [tools] per phase.
- DO NOT hallucinate code/docs, skip context parsing, or output unverified changes.
- Surface all missing docs, inconsistencies, and doc/code drift.
- Visualize doc pipeline, structure, and update cycles for easy onboarding.
- Close with doc summary, audit/version log, flagged gaps, and suggested next steps.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/doc.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, doc pipeline, update flow
├── [context_schema] # JSON/YAML: doc/session/input fields
├── [workflow] # YAML: documentation phases
├── [tools] # YAML/fractal.json: tool registry & control
├── [recursion] # Python: feedback/revision loop
├── [examples] # Markdown: sample runs, change logs, 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.
- 3d ago First seen · 282 lines · 0 tokens per session scan A cde5f99d5f97
doc is a command published in the GitHub repository jasontang-ai/Context-Engineering (9,238 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,476 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
git
Git operations with intelligent commit messages and workflow optimization.
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.