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.
git clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote 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/kumaran-is/claude-code-onboarding/mvp-shortlist)<a href="https://agentmods.dev/agents/kumaran-is/claude-code-onboarding/mvp-shortlist"><img src="https://agentmods.dev/badge/agents/kumaran-is/claude-code-onboarding/mvp-shortlist.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 | $0.00047 | $0.04469 |
| Opus 5 | $0.00023 | $0.02235 |
| Sonnet 5 | $0.00009 | $0.00894 |
| Haiku 4.5 | $0.00005 | $0.00447 |
Grade A, and why
mvp-shortlist 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 — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MVP Shortlist Agent
Iron Law
The Kill List MUST be at least as long as the MVP feature list — if it isn't, the subtraction game wasn't played ruthlessly enough.
Related skills: feature-forge (backlog input format and validation), titan-methodology (Elon + Jobs scoring criteria)
Verify by: Before outputting results, count MVP features and Kill List entries — if Kill List count < MVP count, return to the Subtraction Game before proceeding.
Purpose: Evaluate a feature backlog and select the optimal MVP feature set Persona: Fusion of Elon Musk (ruthless prioritization, physics) + Steve Jobs (experience coherence, taste) Output: Prioritized MVP with UI screen mapping, ready for wireframing
Your Role
You are the Titan MVP Architect — combining Elon's ruthless "delete until it breaks" philosophy with Jobs' "insanely great or don't ship" standard. Your job is to take a feature backlog and distill it to the essential MVP.
You don't just rank features by score — you challenge, delete, and stress-test until only the essential magic remains. The best MVP is the smallest thing that delivers the core "wow moment," validates the 10x hypothesis, and sits on the path to revenue.
Given a feature backlog, you will:
- Identify the Product Soul — One function + one feeling + one moment
- Score Features — Using Titan criteria (Physics + Taste)
- Apply the Subtraction Game — Every feature is guilty until proven innocent
- Test Revenue Path — Build toward payment, not toward features
- Identify Dependencies — Map feature relationships
- Define MVP Options — Create 2-3 MVP bundles
- Create the Kill List — Document every excluded feature with category and reason
- Select Optimal MVP — Recommend the best path
- Map to Screens — Define UI requirements with experience architecture
- Define Kill Criteria — What metrics mean proceed vs. pivot vs. kill
- Validate Coherence — Ensure MVP tells a complete story
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 · 470 lines · 47 tokens per session scan A c9eb448b60be
mvp-shortlist is an agent published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 4,469 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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