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 aiagentwithdhruv/skills --skill instantly-campaignsgit clone --depth 1 https://github.com/aiagentwithdhruv/skillsWrote 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/aiagentwithdhruv/skills/instantly-campaigns)<a href="https://agentmods.dev/skills/aiagentwithdhruv/skills/instantly-campaigns"><img src="https://agentmods.dev/badge/skills/aiagentwithdhruv/skills/instantly-campaigns/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/aiagentwithdhruv/skills/instantly-campaigns"><img src="https://agentmods.dev/badge/skills/aiagentwithdhruv/skills/instantly-campaigns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 117 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 118 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00042 | $0.00970 |
| Opus 5 | $0.00021 | $0.00485 |
| Sonnet 5 | $0.00008 | $0.00194 |
| Haiku 4.5 | $0.00004 | $0.00097 |
Grade A, and why
instantly-campaigns 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 11d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instantly Campaign Creation
Goal
Create three email campaigns in Instantly based on a client description and offers. Each campaign has A/B tested first emails and follow-up sequences.
Inputs
- Client Description: Company name, industry, target audience, value proposition
- Offers (optional): List of 3 offers. If not provided, will be generated.
Scripts
./scripts/instantly_create_campaigns.py- Creates campaigns via Instantly API./scripts/read_sheet.py- Read lead data if needed
Process
1. Load Examples
Read .tmp/instantly_campaign_examples/campaigns.md for inspiration on personalization + social proof + offer structure.
2. Generate Campaigns
python3 ./scripts/instantly_create_campaigns.py \
--client_name "ClientName" \
--client_description "Description of the client..." \
--offers "Offer 1|Offer 2|Offer 3" \
--target_audience "Who we're emailing" \
--social_proof "Credentials/results to mention"
3. Review Output
The script creates 3 campaigns (one per offer), each with:
- Email 1: Two A/B variants
- Email 2: Follow-up bump
- Email 3: Breakup email
Campaign Structure
Email 1 (A/B Split Test)
- Personalization hook (
{{icebreaker}}or custom opener) - Social proof (credentials, results)
- Offer (clear value proposition)
- Soft CTA
Email 2 (Follow-up)
- Brief, friendly bump
- Reference original email
- Restate value
- Clear CTA
Email 3 (Breakup)
- Short, direct
- Last chance framing
- Simple yes/no ask
Available Variables
{{firstName}}- Lead's first name{{lastName}}- Lead's last name{{companyName}}- Lead's company{{casualCompanyName}}- Informal company name{{icebreaker}}- AI-generated icebreaker{{sendingAccountFirstName}}- Sender's first name
Edge Cases
- No offers provided: Generate 3 distinct offers from client description
- API errors: Script retries once, then fails with detailed error
- Rate limits: Handled with exponential backoff
What ships with it
2 files 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.
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.
- 11d ago First seen · 125 lines · 42 tokens per session scan A 80503d0f9a17
instantly-campaigns is a skill published in the GitHub repository aiagentwithdhruv/skills (17 stars, last pushed 14d ago), licensed MIT. It adds 42 tokens to every session and 970 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.
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