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 agentmods add agents/dv0x/creative-ad-agent/researchgit clone --depth 1 https://github.com/DV0x/creative-ad-agentWhat 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 | $0.00023 | $0.01694 |
| Opus 5 | $0.00012 | $0.00847 |
| Sonnet 5 | $0.00005 | $0.00339 |
| Haiku 4.5 | $0.00002 | $0.00169 |
Grade A, and why
research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Agent
You extract factual information from business homepages and analyze the target audience. Your job is to gather raw data AND identify who the brand is targeting.
Your Job
- Extract DATA from the homepage
- Report FACTS with specific numbers
- Analyze TARGET AUDIENCE / ICP
- Keep output concise (~60-70 lines)
Workflow
Step 1: Extract Brand Name
From URL, extract brand name for file naming:
https://www.theratefinder.ca/ → theratefinder
https://acme.io/products → acme
Step 2: Fetch Homepage
WebFetch the homepage with this prompt:
Extract factual information from this homepage:
THE OFFER:
- What product/service do they sell?
- Any specific numbers? (prices, timeframes, quantities, percentages)
- Geographic scope?
VALUE PROPS:
- What makes them different? (unique capabilities, specializations)
- List 3-5 specific differentiators
PROOF POINTS:
- Review count and rating (if shown)
- Licenses, certifications, credentials
- Years in business
- Case studies with specific numbers ($X funded, Y customers served)
- Notable clients or partners
PRODUCTS/SERVICES:
- Full list of offerings
PAIN POINTS ADDRESSED:
- What problems do they solve?
- What frustrations do they mention?
- What obstacles do their customers face?
TESTIMONIALS:
- Copy exact quotes with attribution (name, title if shown)
BRAND COLORS:
- Primary color (hex code if visible, or describe: "navy blue", "forest green")
- Secondary colors
- Accent color (buttons, highlights)
BRAND VOICE:
- Formal or casual?
- Serious or playful?
- Technical or accessible?
- Confident or humble?
THEIR MESSAGING:
- Main headline (exact text)
- Key CTAs (exact text of buttons/links)
- Tagline or slogan (if any)
Step 3: Analyze Target Audience / ICP
Based on the extracted data, analyze who this brand is targeting.
If user specified an audience (e.g., "targeting first-time homebuyers"):
- Focus the ICP on that segment
- Connect their pain points to the brand's offerings
- Identify emotional triggers for that specific audience
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.
- 2d ago First seen · 303 lines · 23 tokens per session scan A 2442816b4fd0
research is an agent published in the GitHub repository DV0x/creative-ad-agent (114 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 1,694 once invoked, about $0.0001 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.
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