Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/gtm-engineer-playbook. 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/Othmane-Khadri/gtm-engineer-playbook/main/.claude/skills/gtm-playbook/agent-architecture-planner/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/gtm-engineer-playbookWrote 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/skills/othmane-khadri/gtm-engineer-playbook/agent-architecture-planner)<a href="https://agentmods.dev/skills/othmane-khadri/gtm-engineer-playbook/agent-architecture-planner"><img src="https://agentmods.dev/badge/skills/othmane-khadri/gtm-engineer-playbook/agent-architecture-planner/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/skills/othmane-khadri/gtm-engineer-playbook/agent-architecture-planner"><img src="https://agentmods.dev/badge/skills/othmane-khadri/gtm-engineer-playbook/agent-architecture-planner.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 | $0.00064 | $0.07416 |
| Opus 5.5 | $0.00026 | $0.02966 |
| Sonnet 5.5 | $0.00013 | $0.01483 |
| Haiku 4.5 | $0.00006 | $0.00742 |
Grade A, and why
agent-architecture-planner scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- API calls using `curl` with error handling This is a copy
100% identical to agent-architecture-planner — 768 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.
How it starts
The opening of the file, as written. The whole thing — 766 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Architecture Planner
Design autonomous agents using the "Shell Orchestrates, Claude Thinks" pattern. This skill takes a workflow description, selects the right architecture pattern, and produces a complete agent package: shell wrapper, system prompt, config, scheduling setup, and cost estimate.
The core principle: the shell handles everything deterministic (API calls, file I/O, scheduling, budget tracking), and Claude handles everything that requires judgment (scoring, filtering, writing, categorizing). This separation makes agents cheaper, more reliable, and easier to debug.
Tools Used
| Tool | Purpose |
|---|---|
Read |
Check for existing agent docs, load reference files |
Write |
Output all agent architecture files |
Bash |
Make shell scripts executable, verify directory structure |
Glob |
Check for existing agents to avoid naming conflicts |
Methodology
Follow these steps in order. Do not skip steps. Do not produce output files before completing the discovery interview and confirming the architecture with the user.
Step 1 — Workflow Discovery (Interactive)
Present these questions to the user all at once in a numbered list. Tell the user they can answer inline or paste a block of text.
Ask exactly these questions:
I need to understand the workflow before designing the agent. Answer these 6 questions — be as specific as possible.
1. What task do you want to automate? (Describe the manual workflow as you do it today, step by step.)
2. How often should it run? (Every hour, daily, every 2 days, weekly, on-demand only?)
3. What data sources does it need? (APIs, websites to scrape, databases, local files, RSS feeds?)
4. What should it produce? (Report files, database entries, API calls, Slack messages, emails, CSV exports?)
5. What decisions does it need to make? (Scoring relevance, filtering noise, writing content, categorizing items, choosing actions?)
6. What's your budget constraint? (Max cost per run in dollars, or monthly ceiling. Include both API/data costs and LLM costs if you know them.)
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 · 766 lines · 64 tokens per session scan A b0e34be464c6
agent-architecture-planner is a skill published in the GitHub repository Othmane-Khadri/gtm-engineer-playbook (59 stars, last pushed 6mo ago), licensed MIT. It adds 64 tokens to every session and 7,416 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to agent-architecture-planner, differing in 768 lines, and is treated as a copy.
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