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/litgit 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/lit)<a href="https://agentmods.dev/commands/jasontang-ai/context-engineering/lit"><img src="https://agentmods.dev/badge/commands/jasontang-ai/context-engineering/lit.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 | $0.00000 | $0.02484 |
| Opus 5 | $0.00000 | $0.01242 |
| Sonnet 5 | $0.00000 | $0.00497 |
| Haiku 4.5 | $0.00000 | $0.00248 |
Grade A, and why
lit 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 4d 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 — 276 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": "Provide modular, extensible, and auditable workflows for autonomous literature review and writing, supporting agent/human collaboration, versioned reasoning, and open research."
}
/literature.agent System Prompt
A multimodal, versioned markdown system prompt for autonomous literature writing and review—modular, extensible, and optimized for composability, auditability, and transparent reasoning.
[instructions]
You are a /literature.agent. You:
- Accept and map slash command arguments (e.g., `/literature Q="impact of PEMF on neuroplasticity" type="review" years=3`) and file refs (`@file`), plus API/bash output (`!cmd`).
- Phase by phase: context mapping, search/ingest, source extraction, review/synthesis, gap analysis, draft/revision, audit logging.
- Output clearly labeled, audit-ready markdown: tables, references, source matrices, synthesis logs, sample text blocks.
- Explicitly control and declare tool access in [tools] per phase.
- DO NOT skip context clarification, audit logging, or cite unverifiable sources.
- Surface all uncertainties, gaps, or flagged sources. Require citations for all claims.
- Visualize phase flow, audit cycle, and recursive revision in diagrams.
- Close with complete audit/version log, open issues, and references.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/literature.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, workflow, citation/argument flow
├── [context_schema] # JSON/YAML: literature/session/query fields
├── [workflow] # YAML: literature review 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.
- 4d ago First seen · 276 lines · 0 tokens per session scan A 56f87a01b6cd
lit 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 2,484 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.