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 mrDesign-ww/vault-os --skill competitor-profilinggit clone --depth 1 https://github.com/mrDesign-ww/vault-osWrote 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/mrdesign-ww/vault-os/competitor-profiling)<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/competitor-profiling"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/competitor-profiling/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/mrdesign-ww/vault-os/competitor-profiling"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/competitor-profiling.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 | $0.00139 | $0.03995 |
| Opus 5.5 | $0.00056 | $0.01598 |
| Sonnet 5.5 | $0.00028 | $0.00799 |
| Haiku 4.5 | $0.00014 | $0.00400 |
Grade A, and why
competitor-profiling 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 7d 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
94% identical to competitor-profiling — 0 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 — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Profiling
You are an expert competitive intelligence analyst. Your goal is to take a list of competitor URLs and produce comprehensive, structured competitor profile documents by combining live site scraping with SEO and market data.
Initial Assessment
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered.
Before profiling, confirm:
- Competitor URLs — the list of competitor website URLs to profile
- Your product — what you do (if not in product marketing context)
- Depth level — quick scan (key facts only) or deep profile (full research)
- Focus areas — any specific dimensions to prioritize (e.g., pricing, positioning, SEO strength, content strategy)
If the user provides URLs and context is available, proceed without asking.
Tool Stack Selection
This skill supports two data source stacks. Check which MCPs are active before starting and select accordingly.
| Stack | Scraping | SEO & Market Data | When to use |
|---|---|---|---|
| Primary | Firecrawl MCP | DataForSEO MCP | Preferred — richer data, site mapping, structured extraction |
| Alternative | WebFetch (built-in) | Whatever SEO data source is connected: Ahrefs or Semrush MCP, Ubersuggest MCP, or similar | When Firecrawl or DataForSEO are unavailable |
If neither Firecrawl nor DataForSEO is available but another SEO data source is, use the alternative stack; most cover the core profile fields. If no SEO data source is available, mark those metrics unavailable (never estimate them) and proceed with qualitative observations only.
For full tool documentation, execution order, and error handling for both stacks, see references/tool-reference.md.
Core Principles
1. Facts Over Opinions
Every claim in a profile should be traceable to a source — scraped page content, review data, or SEO metrics. Keep three layers visibly separate:
- Observed: what a source says, with the source and date
- Inferred: your reading of what was observed. Label it, and say how confident you are when it would change a decision
- Implication: what it might mean for your product, framed as a question or option, not a conclusion
What ships with it
4 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.
- 7d ago Changed · +29 lines · +11 tokens per session dd97331ef22b
- 1mo ago First seen · 413 lines · 128 tokens per session scan A ea7b5b7feb79
competitor-profiling is a skill published in the GitHub repository mrDesign-ww/vault-os (2 stars, last pushed 6d ago), licensed MIT. It adds 139 tokens to every session and 3,995 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to competitor-profiling, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
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…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
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…