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 agents/shuchitajain/awesome-ai-setup/generate-contextgit clone --depth 1 https://github.com/shuchitajain/awesome-ai-setupWrote 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/agents/shuchitajain/awesome-ai-setup/generate-context)<a href="https://agentmods.dev/agents/shuchitajain/awesome-ai-setup/generate-context"><img src="https://agentmods.dev/badge/agents/shuchitajain/awesome-ai-setup/generate-context.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 | $0.00026 | $0.01355 |
| Opus 5 | $0.00013 | $0.00678 |
| Sonnet 5 | $0.00005 | $0.00271 |
| Haiku 4.5 | $0.00003 | $0.00136 |
Grade A, and why
generate-context 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 5d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate CONTEXT.md
You are generating a CONTEXT.md file for this repository.
This file captures domain knowledge - what the application does, who uses it, what the core entities are, and what business rules govern them. It is read by AI tools to reduce hallucination on domain-specific behavior and to prevent suggestions that contradict business rules.
The goal is to document what the application actually does, not what a similar application might do.
Step 1 - Read the Repository
Gather information from the codebase before writing anything.
Primary sources (read these):
- Any existing documentation:
README.md,docs/,CONTEXT.md, wiki files - Entity and model definitions - files in
domain/entities/,models/,types/, or equivalent. These reveal the core domain vocabulary. - Use cases or service layer - files in
domain/usecases/,services/,interactors/, or equivalent. These reveal business operations and rules. - Repository or API interfaces - reveal what data operations exist
- Auth-related files - reveal user roles and permissions
- Routing configuration - reveals what screens/views exist (= what workflows exist)
- Validation logic - reveals business rules as code
Secondary sources (read if primary sources are sparse):
- UI screens/components - reveal user workflows from the user's perspective
- API client code - reveals backend capabilities and data structures
- Test files - often contain realistic domain examples and edge cases
Reference Example (Optional)
Check for a reference example in this order:
.ai/reference/*/CONTEXT.md- if the user copied one during setupnode_modules/awesome-ai-setup/examples/*/CONTEXT.md- if the package is installed locally
If neither path exists, skip this section entirely and proceed to Step 2.
Use it as a structural guide only - what sections to include and how to format them. Do not copy its content; it describes a different project. All content must come from reading this codebase in Step 1.
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.
- 5d ago First seen · 160 lines · 26 tokens per session scan A 4d2aa1f7f38c
generate-context is an agent published in the GitHub repository shuchitajain/awesome-ai-setup (5 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 1,355 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-31.
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