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 nextgit 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/next)<a href="https://agentmods.dev/skills/whawkinsiv/solo-founder-skills/next"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/next/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/next"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/next.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 125 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00134 | $0.01600 |
| Opus 5 | $0.00067 | $0.00800 |
| Sonnet 5 | $0.00027 | $0.00320 |
| Haiku 4.5 | $0.00013 | $0.00160 |
Grade A, and why
next 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 12d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What To Do Next
You sat down to work and don't know where to start. This skill scans your project — code, infrastructure, marketing, growth, business — and surfaces the 3-5 highest-value things you could do right now.
This skill is for discovering what to work on when you don't know. For evaluating whether a specific activity is worth your time, use focus. For ranking feature candidates you've already identified, use prioritize. For improving code performance, use optimize.
How This Works
- I read your project context (files, codebase, docs)
- I ask 1-2 quick questions if I can't figure out your stage
- I scan for high-value gaps across your whole business
- You get a ranked list of opportunities with effort estimates and which skill to use
Step 1: Gather Context
Read these files if they exist (silently — don't list what you're reading):
- About-me skill output or any founder context docs
product-marketing-context.mdor.agents/product-marketing-context.md- CLAUDE.md, README, package.json
- Any analytics, metrics, or status docs
Scan the codebase for structure:
- What's built, what's missing, what's half-finished
- TODO/FIXME/HACK comments on important code
- Deployment config, monitoring, CI/CD, error tracking
- Marketing pages, analytics integration, SEO setup
- Test coverage, error handling patterns
Only ask what you can't infer. If existing context answers these, skip straight to scanning.
Max 2 questions, one message:
Quick context so I can find the right opportunities:
1. What stage are you at?
a) Still building, not launched
b) Launched, few/no users yet
c) Have users, no revenue
d) Have paying customers
2. What's your biggest frustration right now?
a) Not sure what to build next
b) Built it but nobody's using it
c) People sign up but don't stick
d) Growing but everything feels fragile
e) Something else: ___
Step 2: Scan for Opportunities
Look for signals across these categories. Use judgment about which matter based on the founder's stage — don't mechanically check every one.
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.
- 12d ago First seen · 179 lines · 134 tokens per session scan A 81d25e28382a
next is a skill published in the GitHub repository whawkinsiv/solo-founder-skills (243 stars, last pushed 15d ago), licensed MIT. It adds 134 tokens to every session and 1,600 once invoked, about $0.0007 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…