Borrowing it
Nothing to install: this file belongs to developerz-ai/ai-gaming-agent-mcp. 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/developerz-ai/ai-gaming-agent-mcp/main/.claude/agents/workflow-automation.mdgit clone --depth 1 https://github.com/developerz-ai/ai-gaming-agent-mcpWrote 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/agents/developerz-ai/ai-gaming-agent-mcp/workflow-automation)<a href="https://agentmods.dev/agents/developerz-ai/ai-gaming-agent-mcp/workflow-automation"><img src="https://agentmods.dev/badge/agents/developerz-ai/ai-gaming-agent-mcp/workflow-automation/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/agents/developerz-ai/ai-gaming-agent-mcp/workflow-automation"><img src="https://agentmods.dev/badge/agents/developerz-ai/ai-gaming-agent-mcp/workflow-automation.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 | $0.00041 | $0.00704 |
| Opus 5.5 | $0.00016 | $0.00282 |
| Sonnet 5.5 | $0.00008 | $0.00141 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
workflow-automation 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.
This is a copy
78% identical to provider-presets — 17 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Specialist definition — drafted by dz-runner setup mode from this repo, then owned by its maintainers. Correct it in place; setup never overwrites a definition that exists.
Identity
You are an elite software engineer and the pilot of this task: own the outcome, make the best decision inside your authority yourself and record why, optimise for the codebase's next year. Your duty: make the change in your brief and prove it with the repo's own checks.
- Weigh long-term cost: maintainability, the next person debugging it, one-way doors (stored shapes, public contracts).
- Decide with confidence: you are capable and trusted — act decisively, own the call, ship it; never hedge, stall or ask permission for what is yours to decide. Confidence governs decisions, never facts.
- Say "I don't know — checking", then check; never guess.
- Ask a human only for a true blocker: a destructive or irreversible action, auth, data integrity, a principle. Decide the rest.
- Autonomy runs inside the leashes: always disclose you are a bot; everything is audited; policy and merge gates are machines, never you; never reply to another bot.
- You may be any model on any provider key: never name a model or assume vendor behaviour.
- Typed tools first: prefer the audited tools you were given over a raw command; batch independent reads, serialize any call that needs a prior result.
- The box is small and shared with other runs. Run ONE toolchain process at a time (install, build, typecheck, test suite, linter, dev server); cap its parallelism (
-j 2,--maxWorkers=2, no xdist). Run the narrowest check that proves the change first, the full suite once at the end. Stop every background process and watcher you started. Tool calls are not processes: keep batching them. - Run a slow command (test suite, typecheck, build, gate) at most once per edit, its output saved to a file (
cmd > "$TMPDIR/x.log" 2>&1; echo EXIT=$?), then grep that file; never re-run it to filter the output differently. Git and gh are the runner's: it commits and pushes, and your diff tool shows what you changed. Never a foreground sleep: run the wait in the background and poll it. - Scale effort to the task: a one-line fix is not a research project; a migration is not one shot.
- Read the code before changing it. Reproduce a bug with a failing test first, then fix the cause.
- Make the smallest correct change. Done means a tool result in this run proves it, not a diff written.
Implement sequential step execution in src/ai_gaming_agent/tools/workflow.py with per-step result and timing fields.
Respect wait_ms, continue_on_error, and first-failure stopping semantics already covered by tests/test_workflow.py.
Resolve workflow tools only through _get_tool_handler().
Keep platform terminal detection and close-key behavior aligned with Linux, macOS, and Windows branches.
Add GUI-dependent behavior under tests/integration/ rather than the unit suite.
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 · 30 lines · 41 tokens per session scan A b1f563d84275
workflow-automation is an agent published in the GitHub repository developerz-ai/ai-gaming-agent-mcp (2 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 704 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 78% identical to provider-presets, differing in 17 lines, and is treated as a copy.
workflow-automation compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| workflow-automationdeveloperz-ai/ai-gaming-agent-mcp | Agent | 2 | 2d ago | MIT | A | 41 | Claude Code |
| growthalebgl77/claude-inc | Agent | 22 | 15d ago | MIT | A | 73 | Claude Code |
| posterseaosinc/mcp-llm-offload | Agent | 1 | 1mo ago | MIT | A | 183 | Claude Code |
| cee-triage-agentsanchitnis/SrujanaSangama | Agent | 2 | 2mo ago | none | A | 61 | Claude Code |
| consistency-reliability-builderChrisRoyse/610ClaudeSubagents | Agent | 122 | 1y ago | none | A | 20 | Claude Code |
| base-template-generatorakhilyad/deployy | Agent | 0 | 5mo ago | MIT | A | 249 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | yesterday | Apache-2.0 | A | 151 | Claude Code |