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 samkawsarani/sams-product-plugins --skill analyze-competitorgit clone --depth 1 https://github.com/samkawsarani/sams-product-pluginsWrote 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/samkawsarani/sams-product-plugins/analyze-competitor)<a href="https://agentmods.dev/skills/samkawsarani/sams-product-plugins/analyze-competitor"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-competitor/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/samkawsarani/sams-product-plugins/analyze-competitor"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-competitor.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 | $0.00079 | $0.01894 |
| Opus 5 | $0.00039 | $0.00947 |
| Sonnet 5 | $0.00016 | $0.00379 |
| Haiku 4.5 | $0.00008 | $0.00189 |
Grade A, and why
analyze-competitor 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dependency Check
- Notion MCP (optional): Check if
Notion:notion-searchis available in your tools list.- If available: Use it to search for internal competitor research.
- If missing: Skip Notion search and proceed with other sources.
Competitor Analysis Skill
Perform comprehensive competitive analysis of a single competitor, gathering intelligence from multiple sources to create a structured research report.
This skill analyzes ONE competitor at a time. For analyzing multiple competitors in parallel, pass multiple competitor names.
Output: Structured markdown report saved to competitor-[NAME]-comparison.md (or user-specified location)
Data sources (in priority order):
- Project files (existing research, transcripts, context files)
- Notion workspace (if MCP available)
- Web search (pricing pages, reviews, testimonials)
- User-provided sources
Workflow
Step 1: Understand the Request
Gather essential information:
Required:
- Competitor name
- Website URL (or prompt user if not provided)
Optional:
- Our product context (look for a product context file)
- Specific focus areas (e.g., "focus on pricing" or "analyze their enterprise features")
- Output location (default:
competitor-[name]-comparison.md)
If a product context file exists: Read it to understand our product for comparison purposes.
Step 2: Gather Information (Multi-Source Strategy)
Follow this prioritized approach to gather comprehensive competitive intelligence:
Source 1: Project Files (Check First)
- Search for existing research mentioning the competitor
- Search for transcripts or notes mentioning the competitor
- Use
Readto load any existing research notes found - Why first: Existing internal research is most reliable and contextual
Source 2: Notion Workspace (If Available)
- Check if Notion MCP tools are available
- Use
Notion:notion-searchwith queries like:[Competitor Name][Competitor Name] pricing[Competitor Name] features[Competitor Name] customers
- Use
Notion:notion-fetchto retrieve relevant pages - Why second: Internal research may contain analysis and context
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.
- 12d ago First seen · 230 lines · 79 tokens per session scan A 8642fca59741
analyze-competitor is a skill published in the GitHub repository samkawsarani/sams-product-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,894 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 2026-08-31.
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…
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…