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/ntaffzii/skill-agents/agents-mdgit clone --depth 1 https://github.com/ntaffzii/Skill-AgentsWrote 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/instructions/ntaffzii/skill-agents/agents-md)<a href="https://agentmods.dev/instructions/ntaffzii/skill-agents/agents-md"><img src="https://agentmods.dev/badge/instructions/ntaffzii/skill-agents/agents-md.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 | $0.00536 | $0.00536 |
| Opus 5 | $0.00268 | $0.00268 |
| Sonnet 5 | $0.00107 | $0.00107 |
| Haiku 4.5 | $0.00054 | $0.00054 |
Grade A, and why
Skill-Agents 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Project-wide rules for the Antigravity agent.
Global Scope Note: These default mode settings apply strictly to complex projects. If the current workspace is a simple script or scratchpad, disregard the deep-planning requirement and default to fast execution.
Default Mode Settings
- New feature / multi-file change / architecture decision: Always use Planning Mode.
Apply the
deep-planningskill before writing any code. - Bug fix with unknown root cause: Planning Mode. Apply
deep-planning. - Bug fix that is an obvious one-line typo or off-by-one: Fast Mode is fine.
- Refactor touching more than one file: Planning Mode.
- Refactor within a single file, no behavior change: Fast Mode is fine.
- Test generation / documentation / formatting: Fast Mode.
When in doubt, default to Planning Mode. A wasted plan review costs seconds; an unreviewed multi-file change that goes wrong costs much more.
Plan continuity (important)
This project has had recurring issues with the agent losing earlier plan context when the conversation gets long or the user changes direction mid-thread.
Rules:
- If an Implementation Plan artifact already exists for the current thread/feature, treat new chat instructions as edits to that plan, not as a new task. Update the existing artifact in place. Never silently discard prior plan content.
- When a plan changes materially, add a one-line "Revision note" at the top of the artifact explaining what changed and why, so the human can track drift over time.
- For any task expected to span multiple sessions, write the approved plan to
PLAN.mdat the project root as the source of truth, not just the in-conversation artifact. ReadPLAN.mdat the start of every session before proposing new work.
Model routing
This project uses two custom agents (see .agents/agents/):
architect— planning, spec-writing, and plan revisions. Runs on a stronger reasoning model. Never edits files directly.implementer— executes approved plans. Runs on a faster/cheaper model. Never invents scope beyond whatarchitectapproved.
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 · 50 lines · 536 tokens per session scan A b9e6d8baf9cc
Skill-Agents AGENTS.md is an instructions file published in the GitHub repository ntaffzii/Skill-Agents (4 stars, last pushed 4d ago), licensed MIT. It adds 536 tokens to every session, about $0.0027 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 2026-09-04.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.