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 hamzabellouch/agent-skills --skill marketing-competitor-profilinggit clone --depth 1 https://github.com/hamzabellouch/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/hamzabellouch/agent-skills/marketing-competitor-profiling)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/marketing-competitor-profiling"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/marketing-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/hamzabellouch/agent-skills/marketing-competitor-profiling"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/marketing-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 | $0.00128 | $0.03289 |
| Opus 5.5 | $0.00051 | $0.01316 |
| Sonnet 5.5 | $0.00026 | $0.00658 |
| Haiku 4.5 | $0.00013 | $0.00329 |
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 — 44 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 — 413 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.
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. Label inferences clearly.
2. Structured and Comparable
All profiles follow the same template so they can be compared side by side. Consistency matters more than completeness on any single profile.
3. Current Data
Profiles are snapshots. Always include the date generated. Flag anything that looks stale (e.g., "pricing page last updated 2023").
4. Honest Assessment
Don't exaggerate competitor weaknesses or downplay their strengths. Accurate profiles are useful profiles.
Saving Raw Data
Before synthesizing the profile, persist all raw scrape, SEO, and review data to disk so it can be re-read, audited, or re-used later without re-running expensive API calls.
Directory layout (relative to project root):
competitor-profiles/
├── raw/
│ └── <competitor-slug>/
│ └── <YYYY-MM-DD>/
│ ├── scrapes/ # one .md file per scraped page (homepage.md, pricing.md, ...)
│ ├── seo/ # one .json file per DataForSEO call (backlinks-summary.json, ranked-keywords.json, ...)
│ └── reviews/ # one .md or .json file per review source (g2.md, capterra.md, ...)
├── <competitor-slug>.md # final synthesized profile
└── _summary.md # cross-competitor summary
What ships with it
3 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 First seen · 413 lines · 128 tokens per session scan A ea7b5b7feb79
competitor-profiling is a skill published in the GitHub repository hamzabellouch/agent-skills (8 stars, last pushed today), licensed MIT. It adds 128 tokens to every session and 3,289 once invoked, about $0.0005 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 44 lines, and is treated as a copy.
Other skills, from other repositories
spec-implement
Orchestrate parallel implementation of a feature specification by dispatching coder agents batch-by-batch with code review gates between batches. Use this skill when the user says "implement this feature", "start implementing", "run the spec", "execute the plan", "continue implementing", or wants to begin coding a…
spec-ship
Push the current branch to GitHub and create a pull request. Use this skill when the user says "ship it", "ship this", "push to github", "create a pr", "open a pull request", "send for review", "get this reviewed", or wants to push their work and open a PR. Also use when the user says "/spec-ship" (or their host's…
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.