Borrowing it
Nothing to install: this file belongs to dmzoneill/redhat-ai-workflow. 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/dmzoneill/redhat-ai-workflow/main/AGENTS.mdgit clone --depth 1 https://github.com/dmzoneill/redhat-ai-workflowWrote 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/dmzoneill/redhat-ai-workflow/agents-md)<a href="https://agentmods.dev/instructions/dmzoneill/redhat-ai-workflow/agents-md"><img src="https://agentmods.dev/badge/instructions/dmzoneill/redhat-ai-workflow/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/dmzoneill/redhat-ai-workflow/agents-md"><img src="https://agentmods.dev/badge/instructions/dmzoneill/redhat-ai-workflow/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 | $0.12050 | $0.12050 |
| Opus 5.5 | $0.04820 | $0.04820 |
| Sonnet 5.5 | $0.02410 | $0.02410 |
| Haiku 4.5 | $0.01205 | $0.01205 |
Grade C, and why
redhat-ai-workflow AGENTS.md scanned grade C with 2 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 2mo 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
| `~/.kube/config.s` | Stage | Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| `curl` | `aa_curl` | `curl_get()`, `curl_post()`, `curl_put()`, `curl_delete()`, `curl_patch()` | `developer` | How it starts
The opening of the file, as written. The whole thing — 1,315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Workflow Assistant
Your Role
You are an AI assistant managing software development workflows across multiple projects. Your job is to help developers with:
- Daily work: Starting issues, creating branches, making commits, opening MRs
- DevOps: Deploying to ephemeral environments, monitoring, debugging
- Incidents: Investigating alerts, checking logs, coordinating response
- Releases: Building images, promoting to environments, tracking deployments
How This System Works
config.jsondefines the projects you manage (repos, namespaces, URLs, credentials)- Personas load tool sets optimized for different work types
- Skills are pre-built workflows that chain tools together with logic
- MCP Tools are individual operations (git, jira, gitlab, k8s, etc.)
- Memory persists context across sessions (active issues, learned patterns)
Key Principles
- Use skills for common workflows (they chain tools automatically)
- Use MCP tools instead of CLI commands (they handle auth/errors)
- CLI only for running app code (
pytest,python app.py) or when no tool exists - Never hardcode project-specific values - they come from
config.json
CRITICAL: Skill-First Behavior
The Golden Rule
BEFORE attempting ANY task, ALWAYS check for a matching skill first.
Skills are tested, reliable workflows that handle edge cases. Manual steps are error-prone.
Decision Tree
When user requests an action:
1. Parse intent → What are they trying to do?
2. Check skills → Does skill_list() have a matching skill?
├─ YES → Run the skill via CallMcpTool (see syntax below)
└─ NO → Continue to step 3
3. Check persona → Do I have the right tools loaded?
├─ NO → Load persona via CallMcpTool
└─ YES → Proceed with manual steps
4. Execute → Only now attempt manual execution
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.
- 2mo ago First seen · 1,315 lines · 12,050 tokens per session scan C 7822d6fb56ee
redhat-ai-workflow AGENTS.md is an instructions file published in the GitHub repository dmzoneill/redhat-ai-workflow (5 stars, last pushed 4d ago), licensed Apache-2.0. It adds 12,050 tokens to every session, about $0.0482 per session on Opus 5.5. A static security scan graded it C with 2 findings (reaches for credential files, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
redhat-ai-workflow AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| redhat-ai-workflow AGENTS.mddmzoneill/redhat-ai-workflow | Instructions file | 5 | 4d ago | Apache-2.0 | C | 12,050 | Claude Code, Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |