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 whawkinsiv/solo-founder-skills --skill prioritizegit clone --depth 1 https://github.com/whawkinsiv/solo-founder-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/whawkinsiv/solo-founder-skills/prioritize)<a href="https://agentmods.dev/skills/whawkinsiv/solo-founder-skills/prioritize"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/prioritize/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/whawkinsiv/solo-founder-skills/prioritize"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/prioritize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00088 | $0.01581 |
| Opus 5 | $0.00044 | $0.00790 |
| Sonnet 5 | $0.00018 | $0.00316 |
| Haiku 4.5 | $0.00009 | $0.00158 |
Grade A, and why
prioritize 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 13d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Strategy & Prioritization
The hardest product decision is what NOT to build. This skill helps you evaluate ideas, score features, and decide what to work on next — so you build the thing that matters most.
Core Principles
- Features don't win markets. Solving a painful problem better than anyone else does.
- Ship the smallest thing that tests the biggest assumption.
- Product work is hypothesis testing, not feature delivery.
- Roadmaps are communication tools, not promises.
- For solo founders: do ONE thing well before adding the next.
Workflow
Prioritization Process:
- [ ] List all feature candidates
- [ ] Score each with RICE
- [ ] Cut anything scoring below threshold
- [ ] Define MVP scope for the winner
- [ ] Write the spec (see plan skill)
- [ ] Build it
Step 1: List Your Candidates
Gather every idea, request, and "we should build..." into one place.
Tell AI:
Help me organize feature candidates for prioritization:
- Product: [what it does]
- Current stage: [pre-launch / 0-$1K MRR / $1K-$10K MRR]
- Current pain points: [what users are asking for or struggling with]
Here are my feature ideas:
1. [Feature idea]
2. [Feature idea]
3. [Feature idea]
4. [Feature idea]
For each, identify: what user problem it solves, who it helps, and whether it drives
acquisition, activation, retention, or revenue.
Step 2: Score with RICE
| Factor | Question | Scale |
|---|---|---|
| R — Reach | How many users will this affect in the next quarter? | Number of users |
| I — Impact | How much will it move the key metric? | 3=massive, 2=high, 1=medium, 0.5=low |
| C — Confidence | How sure are you about reach and impact? | 100%, 80%, 50% |
| E — Effort | Person-weeks of engineering time? | Weeks |
Score = (Reach x Impact x Confidence) / Effort
Rank by score, but use judgment — scores are conversation starters, not final answers.
Tell AI:
Score these features using RICE prioritization:
[Paste your feature list]
For our context:
- Total active users: [number]
- Key metric we're trying to improve: [activation rate / retention / revenue / etc.]
- My capacity: [solo founder / one developer / small team]
Create a table: Feature | Reach | Impact | Confidence | Effort | RICE Score | Rank
Then recommend which to build first and why — don't just go by the numbers.
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.
- 13d ago First seen · 214 lines · 88 tokens per session scan A 43ae5b20d7b1
prioritize is a skill published in the GitHub repository whawkinsiv/solo-founder-skills (243 stars, last pushed 16d ago), licensed MIT. It adds 88 tokens to every session and 1,581 once invoked, about $0.0004 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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