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 uthumany/uthy-legacy-os --skill proto-personagit clone --depth 1 https://github.com/uthumany/uthy-legacy-osWrote 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/uthumany/uthy-legacy-os/proto-persona)<a href="https://agentmods.dev/skills/uthumany/uthy-legacy-os/proto-persona"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/proto-persona/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/uthumany/uthy-legacy-os/proto-persona"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/proto-persona.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 | $0.00034 | $0.00961 |
| Opus 5.5 | $0.00014 | $0.00384 |
| Sonnet 5.5 | $0.00007 | $0.00192 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
proto-persona 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 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.
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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proto-Persona
Overview
Proto-personas are lightweight, hypothesis-driven user profiles built from what you already know — before investing in formal research. They align the team around who you're building for, surface assumptions, and create a shared target to validate (or disprove) through real research.
When to Use
- Starting a new product or feature with limited user research
- Aligning a cross-functional team on target users
- Identifying assumptions to validate in upcoming research
- Quick stakeholder alignment without a full research project
- Don't use for: already-well-understood user segments, regulatory/compliance contexts requiring validated research
Instructions
1. Identify Segments
Brainstorm 2-4 distinct user types for your product area. Ask:
- Who uses this product or could use it?
- What different jobs are they trying to accomplish?
- What makes them behave differently?
Each segment should have distinct goals, behaviors, or contexts.
2. Build the Proto-Persona Canvas
For each segment, fill out:
## [Name/Archetype]
**Tagline**: One-liner capturing their relationship to your product
**Demographics** (hypothesized):
- Role, experience level, company size, industry
**Goals & Motivations**:
- What are they trying to achieve?
- What success looks like to them
**Pain Points**:
- What frustrates them about current solutions?
- What workarounds do they use?
**Behaviors**:
- How do they currently solve the problem?
- What tools do they use?
- What triggers their behavior?
**Needs from your product**:
- What would make their life better?
**Assumptions** (critical!):
- List 3-5 things you're assuming about this persona
- Mark which are most uncertain
3. Map Assumptions
For each persona, write down:
- Confident assumptions — things you're fairly sure about (backed by some evidence)
- Uncertain assumptions — things you're guessing (need validation)
- Rate each assumption: 🟢 confident / 🟡 some doubt / 🔴 pure guess
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 · 115 lines · 34 tokens per session scan A 57c3e390bb01
proto-persona is a skill published in the GitHub repository uthumany/uthy-legacy-os (5 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 961 once invoked, about $0.0001 per session on Opus 5.5. 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.
proto-persona compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| proto-personauthumany/uthy-legacy-os | Skill | 5 | 5mo ago | MIT | A | 34 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |