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 coleschaffer/copywritingskills-rmbc --skill competitor-offer-analysisgit clone --depth 1 https://github.com/coleschaffer/copywritingskills-rmbcWrote 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/coleschaffer/copywritingskills-rmbc/competitor-offer-analysis)<a href="https://agentmods.dev/skills/coleschaffer/copywritingskills-rmbc/competitor-offer-analysis"><img src="https://agentmods.dev/badge/skills/coleschaffer/copywritingskills-rmbc/competitor-offer-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/coleschaffer/copywritingskills-rmbc/competitor-offer-analysis"><img src="https://agentmods.dev/badge/skills/coleschaffer/copywritingskills-rmbc/competitor-offer-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 | $0.00035 | $0.01971 |
| Opus 5 | $0.00017 | $0.00986 |
| Sonnet 5 | $0.00007 | $0.00394 |
| Haiku 4.5 | $0.00003 | $0.00197 |
Grade A, and why
competitor-offer-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.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
competitor-offer-analysis
Purpose
Analyze competitor offers to find positioning gaps and differentiation opportunities. This is the competitive intelligence arm of RMBC Research — Step 2 (Competitor Analysis) from the 4-step research framework. Most brands compete on surface features and price. RMBC-driven analysis goes deeper: what mechanism does each competitor claim, how do they prove it, where is their offer architecture weak, and what does no one in the market say that your prospect needs to hear? Output: per-competitor offer breakdown, competitive landscape summary, gap opportunities, and positioning recommendations for differentiation.
Inputs
| Input | Required | Description |
|---|---|---|
product_description |
Yes | Your product — what it does, key features, price point, mechanism (if defined) |
competitors |
Yes | List of competitor names and/or URLs (minimum 2, ideally 3-5) |
target_audience |
Yes | Shared ICP — demographics, pain points, desires, awareness level |
your_key_differentiator |
Yes | What you believe makes your offer unique (will be pressure-tested) |
Execution Protocol
Step 1 — Load Framework Context
Read rmbc-context/SKILL.md to load RMBC framework definitions. Competitor analysis maps directly to RMBC Research Step 2 — studying competing offers, their mechanisms, proof claims, and positioning gaps. The goal is not just to know what competitors do, but to find what they fail to do, fail to say, and fail to prove.
Step 2 — Build Competitor Profiles
For each competitor, analyze across 7 dimensions:
| # | Dimension | Key Questions |
|---|---|---|
| 1 | Offer Architecture | Core product, price point, offer stack (core + bonuses + add-ons), delivery method |
| 2 | Mechanism Claims | Primary mechanism, specificity (vague vs concrete), "only we" test, RMBC novelty score (0-25) |
| 3 | Proof Elements | Proof types (clinical, testimonial, expert, media, demo, data), quality, gaps, RMBC believability score (0-25) |
| 4 | Guarantee | Terms (duration, conditions), strength (unconditional vs conditional), positioning (prominent vs buried) |
| 5 | Bonuses | What's included, relevance to core offer, filler quality assessment |
| 6 | Copy & Positioning | Lead type, tone, awareness level targeting, CTA approach |
| 7 | Weaknesses | Offer gaps, proof gaps, mechanism gaps, experience gaps (onboarding, support, results) |
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 · 196 lines · 35 tokens per session scan A fbde365cac67
competitor-offer-analysis is a skill published in the GitHub repository coleschaffer/copywritingskills-rmbc (30 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 1,971 once invoked, about $0.0002 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 2026-08-30.
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…