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.
npx skills add aroyburman-codes/pm-skills --skill technical-pmgit clone --depth 1 https://github.com/aroyburman-codes/pm-skillsWrote 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/aroyburman-codes/pm-skills/technical-pm)<a href="https://agentmods.dev/skills/aroyburman-codes/pm-skills/technical-pm"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/technical-pm.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.01820 |
| Opus 5 | $0.00017 | $0.00910 |
| Sonnet 5 | $0.00007 | $0.00364 |
| Haiku 4.5 | $0.00003 | $0.00182 |
Grade A, and why
technical-pm 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical PM Skill
Apply a structured framework to technical PM questions targeting AI product roles.
When to Use
- User asks about RLHF, fine-tuning, evals, inference, model architecture
- User asks "Design a system that uses LLMs to X"
- User asks "How would you build a RAG system for X"
- User asks about technical trade-offs in AI/ML systems
- User asks about API design for AI products
- User says
/technical-pmfollowed by a question - Any question requiring ML/AI technical depth from a PM perspective
Context
- Tuned for: AI product roles at frontier AI companies
- What matters: Going deep with researchers and engineers. You don't need to implement, but you need to understand the technical landscape well enough to make informed product decisions.
- Common pitfall: Hand-waving on technical details. Be specific about architectures, trade-offs, and constraints.
Framework: AI PM Technical Method (6 Sections)
Section 1: Technical Clarifications & Constraints
Before designing anything, scope the technical problem:
- Capability Assumptions: What model capabilities are available? (reasoning, multimodal, tool use, code gen)
- Scale: How many users/queries? What latency requirements?
- Infrastructure: Cloud vs. on-prem? What compute budget?
- Data: What training/eval data exists? Privacy constraints?
- Integration: What systems does this need to plug into?
- Timeline: MVP vs. production-grade?
Section 2: Users (Developer & End-User Personas)
For technical products, think about two user layers:
- Developers/Engineers: Who builds on this? What's their skill level? What do they expect?
- End Users: Who consumes the output? What quality bar do they need?
For each persona: current workflow, technical sophistication, key frustrations.
Section 3: High-Level System Design
Draw the system architecture (describe it clearly):
- Data Pipeline: How does data flow in? (user input → preprocessing → model → postprocessing → output)
- Model Layer: Which model(s)? Foundation model + fine-tuned? Routing? Ensemble?
- Orchestration: How are multi-step workflows managed? (agents, chains, state machines)
- Storage: What needs to be persisted? (conversation history, embeddings, user preferences, model artifacts)
- Serving: How is inference served? (batch vs. real-time, edge vs. cloud)
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.
- 8d ago First seen · 173 lines · 34 tokens per session scan A 124350fe07f6
technical-pm is a skill published in the GitHub repository aroyburman-codes/pm-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 1,820 once invoked, about $0.0002 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 2026-08-30.
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