agentmods.dev

ai-workflows AGENTS.md

Reusable AI workflows for GitHub Actions, the automation service built into GitHub repositories.

In plain words
What is it for?
Use them to run reusable AI-assisted workflows such as code review, issue handling, dependency review, notifications, testing, verification, and release-note work.
Why use it?
They let many repositories share one maintained set of continuous-integration and deployment workflows instead of copying workflow logic into each repository.

Instructions file for CodexOpenCode

5 4d ago A scan 1,766 tokens original MIT
View the source file dryvist/ai-workflows
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/agents-md
Clone the repo
git clone --depth 1 https://github.com/dryvist/ai-workflows

Made for: Codex, OpenCode.

Per session 1,766 This file is loaded in full into every session.
When invoked 1,766 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 $0.01766 $0.01766
Opus 5 $0.00883 $0.00883
Sonnet 5 $0.00353 $0.00353
Haiku 4.5 $0.00177 $0.00177

Measured 3d ago against content hash b960c9dfaddc, 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 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 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.

AGENTS.md · 190 lines

How it starts

The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ai-workflows

Reusable AI agent workflows for GitHub Actions. Consumer repos call these with thin ~10-20 line callers.

Architecture

This repo is the single source of truth for CI/CD automation workflows. Each workflow is a GitHub reusable workflow (on: workflow_call) that consumer repos invoke via uses: dryvist/ai-workflows/.github/workflows/<name>.yml@main.

Directory Structure

.github/
  prompts/
    *.md                            # Prompt files (one per workflow)
  scripts/
    render-prompt.sh                # Shared: envsubst + GITHUB_OUTPUT
    best-practices/                 # Extracted JS scripts per workflow
    ci-fail-issue/
    ci-fix/
    dep-review/
    issue-backlog-sweep/
    issue-linker/
    issue-resolver/
    notification/
    post-merge-docs-review/
    post-merge-tests/
    release-notes/
    review-thread-resolver/
    shared/
    verification/
  workflows/
    *.yml                           # Pure YAML workflow definitions (no embedded content)

Workflow Types

All AI workflows use the shared run-ai-agent adapter. The adapter selects Claude or Codex from GH_ACTION_AI_AGENT and defaults to Claude. Non-AI utility workflows (notify-ai-pr, ci-fail-issue, review-thread-resolver) use plain actions/github-script — see docs/PATTERNS.md "Non-AI Utility Workflow Pattern".

  • Prompts rendered via render-prompt.sh + step output (envsubst)
  • Static prompts: most workflows
  • Dynamic prompts (ci-fix, post-merge-tests, post-merge-docs-review): render-prompt.sh with named env vars
  • Write workflows (code-simplifier, next-steps, post-merge-*, ci-fix, issue-resolver, pr-review-responder): the selected agent only edits files and writes typed handoffs (no git or GitHub mutations). A fresh publisher job lands a GitHub-VERIFIED commit/PR via createCommitOnBranch (shared scripts/shared/verified-commit.js). This is mandatory — native use_commit_signing cannot target a branch on our workflow_run/issues/schedule/dispatch triggers. See docs/PATTERNS.md "Verified Commit & PR Pattern".

Read the full file on GitHub · 190 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 · 190 lines · 1,766 tokens per session scan A b960c9dfaddc

Subscribe to this mod's changes

ai-workflows AGENTS.md is an instructions file published in the GitHub repository dryvist/ai-workflows (5 stars, last pushed 4d ago), licensed MIT. It adds 1,766 tokens to every session, about $0.0088 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.

agentic-workflowsai-agentsai-automationautomationcode-quality
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