What is hermoso-research?

A research workflow for studying competitors and their advertising. It can find competing companies, retrieve their running ads from Meta, Google, and LinkedIn ad libraries, and identify recurring hooks or angles.

Use it to find competitors for a domain, inspect a company's ads, compare advertising across platforms, or research what appears to work in a market.

It replaces scattered competitor research with one process for seeing who is competing, what ads they are running, and which ideas may be worth studying.

hermoso-research — Skill for Claude Code and Cursor by hermoso-ai

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 hermoso-ai/hermoso --skill hermoso-research

Clone the repo

git clone --depth 1 https://github.com/hermoso-ai/hermoso

Made for: Claude Code, Cursor.

Or install hermoso, the plugin that ships this one along with the rest of its 4 skills, 1 MCP server.

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/hermoso-ai/hermoso/hermoso-research/github.svg)](https://agentmods.dev/skills/hermoso-ai/hermoso/hermoso-research)

Your own site

<a href="https://agentmods.dev/skills/hermoso-ai/hermoso/hermoso-research"><img src="https://agentmods.dev/badge/skills/hermoso-ai/hermoso/hermoso-research/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/hermoso-ai/hermoso/hermoso-research"><img src="https://agentmods.dev/badge/skills/hermoso-ai/hermoso/hermoso-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 598 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	$0.00103	$0.00598
Opus 5	$0.00051	$0.00299
Sonnet 5	$0.00021	$0.00120
Haiku 4.5	$0.00010	$0.00060

Measured 10d ago against content hash ba63a1ac2540, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

hermoso-research 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.

skills/hermoso-research/SKILL.md · 32 lines

What it actually says

Hermoso — competitor & ad research

This is Hermoso's discovery half (which most generators don't have). Drive the Hermoso CLI.

Setup

hermoso auth login (opens your browser once; nothing to paste). On a machine with no browser: hermoso auth login --token <your key>, using a key from the app under MCP & CLI.. No account at all? An agent can sign itself up on a paid plan with POST /v1/signup at app.hermoso.ai, no browser needed; see the Hermoso README.

Procedure

Pick the tool that fits the ask:

Find competitors for a domain: hermoso competitors <domain> [--mode competitors|inspiration|company] --json
- competitors = head-to-head rivals; inspiration = best relevant ads incl. the brand itself; company = the company's own.
Pull a brand's real ads across ad libraries: hermoso ads pull --company "<name>" [--domain <d>] [--platforms facebook,google,linkedin] [--country US] --json
- Defaults to Meta (richest library). Add google/linkedin only if asked (Google detailed pulls cost more).
Natural-language research (Claude tool-use over ad libraries + organic TikTok): hermoso research "<request>"
- e.g. hermoso research "the longest-running protein-pancake ads on Meta and what hooks they use". Prints a summary + the found ads with their URLs.
Synthesize: report the strongest hooks, angles, formats, and what's worth copying — be specific (quote the actual headlines/angles). If the user then wants to build one, hand off to hermoso-ad-from-brand / hermoso-generate.

Notes

Research spends credits (ad-library calls); keep platform scope to what's asked.
Add --json for the raw ad objects (URLs, copy, run dates) when the user wants the data, not a summary.

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.

10d ago First seen · 32 lines · 103 tokens per session scan A ba63a1ac2540

Subscribe to this mod's changes

hermoso-research is a skill published in the GitHub repository hermoso-ai/hermoso (0 stars, last pushed 3d ago), licensed MIT. It adds 103 tokens to every session and 598 once invoked, about $0.0005 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.

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