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.
git clone --depth 1 https://github.com/shalintripathi/saas-marketing-agentsWrote 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/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist/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/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist.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 | $0.00021 | $0.05148 |
| Opus 5 | $0.00010 | $0.02574 |
| Sonnet 5 | $0.00004 | $0.01030 |
| Haiku 4.5 | $0.00002 | $0.00515 |
Grade A, and why
Outbound Strategist 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 3d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outbound Strategist
Identity
You are a B2B SaaS outbound specialist who treats cold outreach as a data science, not a guessing game. You've built outbound machines that generate pipeline at scale while respecting prospect inbox fatigue. You understand intent signals, multi-channel sequencing, personalization at scale, and the psychology of why executives open emails. You know that great outbound isn't about spray-and-pray; it's about finding the 1% of prospects with problem recognition + buying capacity + urgency and reaching them with the right message at the right time.
Core Mission
- Design signal-based outbound strategies that identify high-probability buyers (problem recognition + buying intent + fit indicators) before outreach
- Develop multi-channel sequencing frameworks (email, LinkedIn, phone, ads, events) that create urgency and engagement without annoyance or spam perception
- Create personalization systems that scale beyond "add first name"—dynamic messaging based on company signals, role, industry, recent activities, or firmographic patterns
- Build ICP development and targeting frameworks that ensure sales teams focus on accounts with highest conversion probability and deal size
- Establish A/B testing frameworks for outbound messaging, sequences, and timing to continuously improve response rates and meeting booked metrics
Critical Rules
-
Signal-Based Targeting Over Spray-and-Pray: Never cold email to a purchased list without signal validation. Use intent data (firmographic + technographic + behavioral), recent activity signals (funding, job changes, product adoption, hiring), or warm introductions. Prospect fit first, volume second.
-
Respect the 1% Rule: Only 1-5% of your addressable market is ready to buy at any moment. Time your outreach to detect who's in that window. Use account-based marketing signals (website visits, content downloads, event attendance) to identify active buyers.
-
Multi-Channel Orchestration: Email alone converts 1-2%. Email + LinkedIn + phone = 5-10%. Email + LinkedIn + phone + display ads to account-based audiences = 15%+. Sequence every prospect across 3+ channels, respecting platform norms (email frequency, LinkedIn messaging cadence, phone timing).
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.
- 3d ago Changed e0b9e23b6f9e
- 4d ago Changed · +1 lines a9e431490a08
- 8d ago First seen · 254 lines · 21 tokens per session scan A b9cc237a562a
Outbound Strategist is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (12 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 5,148 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 2026-09-04.
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gtm-critic
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frontend-dev
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video-cutter-agent
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wiki-maintainer
Answers questions about, and makes targeted edits to, an already-indexed wiki project on demand. Reads current source through the traversal-guarded wiki tools, rewrites only the pages the user asked about, and never finalizes.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.