ai-workflows copilot-instructions.md

A set of coding instructions for the ai-workflows repository, which contains reusable GitHub Actions workflows for AI-assisted automation. It documents the project layout, Bun commands, file rules, and JavaScript format.

In plain words
What is it for?
Use it when editing workflows, JavaScript helper scripts, prompts, configuration, or tests in the ai-workflows repository.
Why use it?
Repository-specific rules prevent agents from putting logic in the wrong file, using the wrong package manager, or changing the intended workflow structure.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/dryvist/ai-workflows/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/dryvist/ai-workflows

Made for: GitHub Copilot.

Per session 767 This file is loaded in full into every session.
When invoked 767 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00767 $0.00767
Opus 5 $0.00383 $0.00383
Sonnet 5 $0.00153 $0.00153
Haiku 4.5 $0.00077 $0.00077

Measured 3d ago against content hash 69f862cc3f32, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ai-workflows copilot-instructions.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 3d 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.

.github/copilot-instructions.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GitHub Copilot Instructions — ai-workflows

Repository Purpose

Reusable GitHub Actions workflows for AI-assisted automation. Consumer repos call these with thin callers (~10-20 lines). This is the single source of truth for CI/CD automation workflows.

Build & Test

  • Package manager: bun (not npm or node)
  • Run tests: bun test
  • Watch mode: bun test --watch
  • bun is globally available; no install step needed

File Structure Rules

Each file must contain a single language — never mix:

  • .yml files: YAML only (workflow configuration)
  • .js files: JavaScript only (logic — one script per purpose)
  • .md files: prompts with ${VAR} placeholders for dynamic values
  • .json files: configuration only

Inline threshold: Scripts of 5 lines or fewer may be embedded in YAML run: steps. Scripts exceeding 5 lines must be extracted to .github/scripts/<workflow-name>/<script>.js.

JavaScript Script Pattern

All scripts use this module pattern:

module.exports = async ({ github, context, core }) => {
  // logic here
};

Pass GitHub Actions expression values (${{ }}) via env: on the step, then read with process.env in the script. Never interpolate expressions inside .js files.

Invoke extracted scripts in YAML:

- uses: actions/github-script@v8
  env:
    MY_VAR: ${{ steps.prev.outputs.value }}
  with:
    script: |
      const run = require('./.ai-workflows/.github/scripts/<dir>/<name>.js');
      await run({ github, context, core });

Testing Requirements

  • Framework: bun:test (built-in globals: describe, it, expect, beforeEach)
  • One test file per script: tests/<script-name>.test.js
  • Mock factories in tests/helpers.js — use createMockCore, createMockContext, createMockGithub
  • Use mock() from bun:test for mock functions
  • Every new script must include a test: happy path + key failure modes

Prompt Files

Prompts live in dryvist/ai-llm-prompts under automation/ with ${VAR} placeholders. Workflows pin an immutable catalog commit and render with .github/scripts/render-prompt.sh, which strips OKF frontmatter before envsubst.

Read the full file on GitHub · 89 lines

Changes

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.

  1. 3d ago First seen · 89 lines · 767 tokens per session scan A 69f862cc3f32

Subscribe to this mod's changes

ai-workflows copilot-instructions.md is an instructions file published in the GitHub repository dryvist/ai-workflows (5 stars, last pushed 4d ago), licensed MIT. It adds 767 tokens to every session, about $0.0038 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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