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 nickstellarstreamai/ai-opportunity-finder --skill priority-rankergit clone --depth 1 https://github.com/nickstellarstreamai/ai-opportunity-finderWrote 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/nickstellarstreamai/ai-opportunity-finder/priority-ranker)<a href="https://agentmods.dev/skills/nickstellarstreamai/ai-opportunity-finder/priority-ranker"><img src="https://agentmods.dev/badge/skills/nickstellarstreamai/ai-opportunity-finder/priority-ranker/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/nickstellarstreamai/ai-opportunity-finder/priority-ranker"><img src="https://agentmods.dev/badge/skills/nickstellarstreamai/ai-opportunity-finder/priority-ranker.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 | $0.00059 | $0.02267 |
| Opus 5 | $0.00030 | $0.01133 |
| Sonnet 5 | $0.00012 | $0.00453 |
| Haiku 4.5 | $0.00006 | $0.00227 |
Grade A, and why
priority-ranker 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 — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Priority Ranker
Takes all your department syntheses and ruthlessly prioritizes opportunities. This is where most DIY discovery efforts fall apart — finding problems is easy, but knowing which ones to solve first is hard. This skill applies the same prioritization framework used by professional AI consultants.
Why Ruthless Prioritization Matters
You will find more opportunities than you can possibly pursue. The temptation is to try to fix everything — don't. Organizations that chase 20 initiatives simultaneously accomplish nothing. Organizations that nail 3 priorities create momentum.
The goal: from many opportunities to 5-7 that actually matter.
What I Need From You
- All Department Synthesis documents (from
/department-synthesizer) - Organization context:
- Company name and size
- Strategic priorities from leadership (what does the CEO/owner care about most?)
- Known constraints (budget range, timeline expectations, technical limitations)
- Internal capacity (do you have technical people? AI experience?)
- Any additional findings not captured in department syntheses
What You'll Get
An Organization-Wide Priority Matrix with:
- Ranked opportunities (with clear rationale)
- MECE workstreams (no overlaps, no gaps)
- Quick Wins vs Strategic Initiatives
- Implementation roadmap (what to do first, second, third)
- Executive-ready summary
Prioritization Framework
Step 1: Eliminate Low-Value Items
Any opportunity with:
- < $50K annual value OR < 500 hours/year saved → Cut it
- Vague impact that can't be quantified → Cut it
- No internal champion to push it forward → Deprioritize
This typically eliminates 30-50% of identified opportunities.
Step 2: Combine Related Items
Multiple scheduling problems = 1 Scheduling workstream Multiple reporting problems = 1 Reporting Automation workstream Multiple data access problems = 1 Data & Insights workstream
MECE principle: Mutually Exclusive, Collectively Exhaustive. No overlaps, no gaps.
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 · 262 lines · 59 tokens per session scan A 0e866182ea4e
priority-ranker is a skill published in the GitHub repository nickstellarstreamai/ai-opportunity-finder (11 stars, last pushed 5mo ago), licensed MIT. It adds 59 tokens to every session and 2,267 once invoked, about $0.0003 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…