What is competitor-intelligence?

A guide for researching competitors and organizing findings about their products, prices, messaging, and market position.

Use it to identify competitors, build comparison tables, create sales battlecards, analyze wins and losses, and monitor changes.

It gives a repeatable way to compare rivals and records how information was collected within legal and ethical limits.

competitor-intelligence — Skill for Codex by san-npm/skills-ws

Install

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.

Any agent

npx skills add san-npm/skills-ws --skill competitor-intelligence

Clone the repo

git clone --depth 1 https://github.com/san-npm/skills-ws

Made for: Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/san-npm/skills-ws/competitor-intelligence/github.svg)](https://agentmods.dev/skills/san-npm/skills-ws/competitor-intelligence)

Your own site

<a href="https://agentmods.dev/skills/san-npm/skills-ws/competitor-intelligence"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/competitor-intelligence/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/san-npm/skills-ws/competitor-intelligence"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/competitor-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,635 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.04635
Opus 5.5	$0.00026	$0.01854
Sonnet 5.5	$0.00013	$0.00927
Haiku 4.5	$0.00007	$0.00464

Measured 2mo ago against content hash c848b0b2659a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

competitor-intelligence 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 2mo 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.

skills/competitor-intelligence/SKILL.md · 256 lines

How it starts

The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Competitor Intelligence

Workflow

1. Competitor Identification

Three tiers:

Tier	Definition	Track
Direct	Same product, same market	Deep: pricing, features, messaging, every move
Adjacent	Different product, same buyer	Monitor: major launches, positioning changes
Aspirational	Where you want to be in 2-3 years	Quarterly: strategy, positioning, market moves

Discovery methods (classic):

Search your top 5 keywords — who ranks?
Ask churned customers who they switched to
Check G2 / Capterra / TrustRadius / PeerSpot category pages
Monitor "alternatives to [your product]" and "[product] vs" searches
Track who bids on your brand keywords (use Google Ads' public Ad Transparency Center and Meta's Ad Library — both legal, no scraping required; do NOT click rivals' paid ads to inflate their costs, that is click fraud)

Discovery methods (2026 surfaces — do not skip these):

AI answer engines / LLM visibility. Ask ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews: "best [category] tools", "alternatives to X", "X vs Y". Record which competitors get named, what claims the model repeats, and which sources it cites. By 2026 a large and growing share of buyer research starts in an answer engine, so being absent or mischaracterized there is a real competitive gap. Re-check monthly; models and their cited sources drift.
App / cloud marketplaces. AWS / Azure / GCP marketplaces, Salesforce AppExchange, Atlassian Marketplace, Shopify App Store, HubSpot Marketplace, Slack/Notion/Figma app directories. Listings reveal positioning, pricing tiers, install counts, and review sentiment.
Browser-extension stores (Chrome Web Store, Firefox Add-ons, Edge Add-ons) when relevant to your category — user counts and reviews are public signal.
Community & social listening. Reddit, Hacker News, Stack Overflow, niche Slack/Discord/Circle communities, LinkedIn, X/Bluesky, YouTube reviews. Search "[competitor] sucks / migration / switched from / pricing" to surface unfiltered sentiment. Use the platforms' own search/APIs; respect rate limits and ToS.
Public filings & funding — Crunchbase, PitchBook, SEC EDGAR (for public co's), state business registries.

Read the full file on GitHub · 256 lines

Files

What ships with it

1 file 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.

agents/openai.yaml 217 B

Changes

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.

2mo ago First seen · 256 lines · 66 tokens per session scan A c848b0b2659a

Subscribe to this mod's changes

competitor-intelligence is a skill published in the GitHub repository san-npm/skills-ws (4 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 4,635 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 2026-08-31.

Marketing and SEO Research

competitor-intelligence next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
competitor-intelligencesan-npm/skills-ws	Skill	4	1mo ago	MIT	A	66	Codex
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code