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 yuusakuri/agent-skills --skill discover-competitive-analysisgit clone --depth 1 https://github.com/yuusakuri/agent-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/yuusakuri/agent-skills/discover-competitive-analysis)<a href="https://agentmods.dev/skills/yuusakuri/agent-skills/discover-competitive-analysis"><img src="https://agentmods.dev/badge/skills/yuusakuri/agent-skills/discover-competitive-analysis/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/yuusakuri/agent-skills/discover-competitive-analysis"><img src="https://agentmods.dev/badge/skills/yuusakuri/agent-skills/discover-competitive-analysis.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 | $0.00067 | $0.00891 |
| Opus 5 | $0.00034 | $0.00445 |
| Sonnet 5 | $0.00013 | $0.00178 |
| Haiku 4.5 | $0.00007 | $0.00089 |
Grade A, and why
discover-competitive-analysis 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 10d 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.
This is a copy
95% identical to discover-competitive-analysis — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Analysis
A competitive analysis provides structured insight into the competitive landscape, helping product teams understand where they stand relative to alternatives and identify opportunities for differentiation. Rather than exhaustively cataloging every competitor, an effective analysis focuses on actionable insights that inform product strategy.
When to Use
- Before entering a new market or launching a new product
- When planning differentiation strategy for an existing product
- During quarterly or annual strategic planning reviews
- When evaluating build vs. buy decisions
- After losing deals to understand competitive positioning
- When onboarding new product team members to the market context
When NOT to Use
- You need market size rather than competitor positioning -> use
discover-market-sizing - You are stress-testing strategic differentiation across the whole business model -> use
lean-canvas, orpositioning-ideasinside a Foundation Sprint - You want to understand why customers switch products -> use
define-jtbd-canvas; it examines competing solutions through the job lens - The landscape is already mapped and you need the problem framed -> use
define-problem-statement
Instructions
When asked to create a competitive analysis, follow these steps:
-
Define the Scope Clarify what you're analyzing: a specific feature area, overall product positioning, or pricing strategy. Identify 3-5 key competitors.direct competitors (same solution), indirect competitors (different solution to same problem), and potential disruptors.
-
Gather Intelligence Research each competitor through public sources: websites, pricing pages, G2/Capterra reviews, press releases, job postings, and customer testimonials. Note what you can verify vs. what you're inferring.
-
Build the Feature Matrix Create a comparison grid of key capabilities. Focus on features that matter to your target customers, not exhaustive checklists. Use consistent ratings (e.g., Full, Partial, None, Unknown).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 78 lines · 67 tokens per session scan A 0f4df6da0fff
discover-competitive-analysis is a skill published in the GitHub repository yuusakuri/agent-skills (2 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 891 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to discover-competitive-analysis, differing in 20 lines, and is treated as a copy.
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…