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 instructions/dragoon0x/argus/agents-mdgit clone --depth 1 https://github.com/Dragoon0x/argusWhat 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 | $0.01213 | $0.01213 |
| Opus 5 | $0.00607 | $0.00607 |
| Sonnet 5 | $0.00243 | $0.00243 |
| Haiku 4.5 | $0.00121 | $0.00121 |
Grade A, and why
argus AGENTS.md 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 2d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Argus Agent Context
You are an AI coding agent working with the Argus design intelligence platform.
Your capabilities via Argus
When connected to the Argus MCP server, you have access to design intelligence tools that let you analyze and improve visual quality. These tools return structured, deterministic results — not opinions.
Available MCP tools
design.inspect
Analyze a page or component's visual structure.
Input: { url?: string, selector?: string, html?: string }
Output: PageSnapshot with element positions, computed styles, accessibility data, and structural relationships.
Use this when you need to understand what's visually happening on a page before making changes.
design.score
Score design quality against established principles.
Input: { snapshot: PageSnapshot, categories?: string[], threshold?: number }
Output: ScoreReport with per-rule findings, category scores, and an aggregate score (0–100).
Categories: spacing, typography, color, hierarchy, accessibility, consistency.
Use this to evaluate whether your generated UI meets quality standards before presenting it to the user.
design.suggest
Get specific, actionable improvement suggestions.
Input: { snapshot: PageSnapshot, maxSuggestions?: number, focus?: string }
Output: Array of Suggestion objects with element selectors, CSS changes, and expected score improvement.
Use this when the score is below threshold and you need concrete fixes.
design.apply
Apply a design fix and verify the improvement.
Input: { selector: string, changes: CSSChanges, verify?: boolean }
Output: { applied: boolean, before: Score, after: Score, delta: number }
Use this to fix issues identified by design.suggest. The verify flag re-scores after applying to confirm improvement.
design.compare
Compare two states of a page.
Input: { before: PageSnapshot, after: PageSnapshot }
Output: DiffReport with structural changes, score deltas, and visual regression indicators.
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.
- 2d ago First seen · 136 lines · 1,213 tokens per session scan A 598fd3dd03dc
argus AGENTS.md is an instructions file published in the GitHub repository Dragoon0x/argus (4 stars, last pushed 4mo ago), licensed MIT. It adds 1,213 tokens to every session, about $0.0061 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.
Other instructions, from other repositories
aiconnect-figma-mcp CLAUDE.md
Instructions for guptaprakhariitr/aiconnect-figma-mcp, covering claude.md, project overview, build & development commands, architecture and mcp server (src/aiconnectmcp/server.ts).
agentic-playwright selectors.instructions.md
Instructions for idavidov13/agentic-playwright, covering selector strategy, critical, instructions, phase 1: open and authenticate and phase 2: explore like a user.
awesome-copilot-id AGENTS.md
Instructions for GulajavaMinistudio/awesome-copilot-id, covering communication, explanation and documentation, markdown formatting, user communication style and workflow & methodology.
llm-ide-rules pytest-integration-tests.instructions.md
Instructions for iloveitaly/llm-ide-rules, covering pytest integration tests and example integration test.
toki AGENTS.md
Instructions for choi138/toki, covering workspace rules, source of truth, toki workflow, required checks and global safety rules.
agentdex CLAUDE.md
Instructions for CaptainYouz/agentdex, covering claude.md — agentdex, purpose, commands, architecture and what gets scanned.