Borrowing it
Nothing to install: this file belongs to bashebr/ai-native-sdlc. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bashebr/ai-native-sdlc/main/AGENTS.mdgit clone --depth 1 https://github.com/bashebr/ai-native-sdlcWrote 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/bashebr/ai-native-sdlc/agents-md)<a href="https://agentmods.dev/instructions/bashebr/ai-native-sdlc/agents-md"><img src="https://agentmods.dev/badge/instructions/bashebr/ai-native-sdlc/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/bashebr/ai-native-sdlc/agents-md"><img src="https://agentmods.dev/badge/instructions/bashebr/ai-native-sdlc/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00385 | $0.00385 |
| Opus 5.5 | $0.00154 | $0.00154 |
| Sonnet 5.5 | $0.00077 | $0.00077 |
| Haiku 4.5 | $0.00038 | $0.00038 |
Grade A, and why
ai-native-sdlc 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 1mo 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.
What it actually says
AGENTS.md
This repo is a reusable skill and plugin bundle implementing the AI-native SDLC workflow. Follow these conventions when working in it.
Source of truth
- The workflow lives in
skills/ai-native-sdlc/SKILL.md. Read it first, then the references it routes to (references/playbook.md,references/adoption.md). - Templates in
skills/ai-native-sdlc/assets/are copied into target projects — never edited to fit one project. skills/ai-native-sdlc/scripts/init_workflow.pyperforms that copy; change the script when the scaffold layout changes.skills/ai-native-sdlc/scripts/init_org.pyscaffolds the optional agent org (roles, protocol, intake); change it when the org templates change.
Validation
After changing the skill or plugin, run the self-check suite (from the repo root):
python3 skills/ai-native-sdlc/scripts/quick_validate.py skills/ai-native-sdlc
bash tests/test_gate.sh
bash tests/test_init.sh
python3 -m unittest discover -s tests -v
All must pass before finishing; CI runs the same checks
(.github/workflows/self-check.yml). Keep plugin.json and SKILL.md
consistent in name, description, and version — quick_validate.py enforces
this.
Conventions
- Keep
SKILL.mdshort; put phase detail in references. - Keep artifacts (intent/spec/plan templates) generic; org specifics belong in the adopter's own skills and hooks.
- Never add hooks or secrets for a specific adopter into the shared templates.
- Commit with the repo-local git identity (
bashebr <[email protected]>); never fall back to the global git identity.
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.
- 1mo ago First seen · 34 lines · 385 tokens per session scan A 53d8f5c1a3ce
ai-native-sdlc AGENTS.md is an instructions file published in the GitHub repository bashebr/ai-native-sdlc (90 stars, last pushed 17d ago), licensed MIT. It adds 385 tokens to every session, about $0.0015 per session on Opus 5.5. 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 2026-08-30.
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).
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.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
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.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
vscode model-prompts.instructions.md
Model-specific prompt authoring and registry guidelines.