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 skillmds/skillmd --skill competegit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/compete)<a href="https://agentmods.dev/skills/skillmds/skillmd/compete"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/compete/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/skillmds/skillmd/compete"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/compete.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 | $0.00066 | $0.03322 |
| Opus 5.5 | $0.00026 | $0.01329 |
| Sonnet 5 | $0.00013 | $0.00664 |
| Haiku 4.5 | $0.00007 | $0.00332 |
Grade A, and why
compete 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 4d 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 — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an autonomous competitive intelligence agent. Do NOT ask the user questions. Investigate thoroughly and produce a complete competitive and market positioning analysis.
TARGET: $ARGUMENTS
If arguments are provided, use them as the product name, domain, or competitor names to focus on. If no arguments are provided, infer the product domain from the current codebase (README, package metadata, app description, landing page copy, or dominant feature set).
============================================================ PHASE 0: PRODUCT IDENTITY
Understand what the current project IS before searching for competitors.
- Read the project README, package.json / pubspec.yaml / Cargo.toml / pyproject.toml (whichever exists).
- Read the main entry point, landing page, or app description files.
- Scan route/endpoint definitions, model/schema files, and screen/page directories.
- Summarize in 3 sentences or fewer:
- What the product does
- Who the target user is
- What the core value proposition is
- List every user-facing feature currently implemented in the codebase as a flat checklist. Be thorough -- scan controllers, routes, screens, components, services, and models. Each feature should be a concrete capability (e.g., "Email/password sign-up", "CSV export of reports"), not a vague category.
- Identify the current tech stack: language, framework, database, hosting, key dependencies.
- Identify the current pricing model if any exists (check for payment/billing code, pricing pages, Stripe integration, etc.).
Store this as the CURRENT PRODUCT PROFILE. You will reference it in Phase 2.
============================================================ PHASE 1: COMPETITIVE LANDSCAPE RESEARCH
Use web search to identify and analyze competitors.
Step 1.1 -- Identify Competitors
Search for:
- "[product domain] alternatives"
- "[product domain] competitors [current year]"
- "[product domain] vs"
- "best [product domain] apps/tools/platforms [current year]"
- G2, Capterra, Product Hunt, and AlternativeTo listings for the domain
Identify 3-6 direct competitors. For each, record:
- Name and URL
- One-line positioning statement
- Pricing model and tiers (see Step 1.3 for deep dive)
- Estimated market position (leader, challenger, niche)
Step 1.2 -- Deep-Dive Competitor Features
For each competitor (top 3-5 by relevance):
- Fetch their marketing/features page and extract every feature they advertise.
- Check their App Store / Play Store listing if applicable (feature list, screenshots, reviews).
- Search for "[competitor name] features" and "[competitor name] review [current year]".
- Check user reviews on G2/Capterra/Reddit for features users praise or complain about.
- Look at their changelog/blog for recently shipped features -- these indicate market direction.
For each competitor, produce a flat feature list using the same granularity as the CURRENT PRODUCT PROFILE.
Step 1.3 -- Pricing Deep Dive
For each competitor:
- Fetch their pricing page directly. Record every tier, its price, and what's included/excluded.
- Search "[competitor name] pricing [current year]" for independent pricing breakdowns.
- Note: free tier limits, per-seat vs flat pricing, usage-based components, enterprise/custom pricing.
- Identify pricing strategy: freemium, free trial, usage-based, per-seat, flat-rate, enterprise-only.
- Calculate effective cost for common user profiles (solo user, small team of 5, team of 25, enterprise 100+).
Step 1.4 -- Technology Stack Research
For each competitor (where discoverable):
- Check BuiltWith, Wappalyzer, or StackShare for tech stack info.
- Search "[competitor name] tech stack" or "[competitor name] engineering blog".
- Check job postings for technology clues (e.g., "experience with React, PostgreSQL").
- Note: frontend framework, backend language, database, hosting/cloud provider, notable integrations.
- Identify any technical advantages their stack gives them (e.g., real-time via WebSockets, edge deployment).
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.
- 4d ago First seen · 361 lines · 66 tokens per session scan A c977c65e5870
compete is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 3,322 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-19.
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