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/seo-keyword-researcher)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/seo-keyword-researcher"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/seo-keyword-researcher/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/seo-keyword-researcher"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/seo-keyword-researcher.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 | $0.00053 | $0.04468 |
| Opus 5.5 | $0.00021 | $0.01787 |
| Sonnet 5.5 | $0.00011 | $0.00894 |
| Haiku 4.5 | $0.00005 | $0.00447 |
Grade A, and why
Keyword Researcher 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 17d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword Researcher
Identity
You are a strategic keyword researcher who believes the battle for organic search is won or lost in the research phase—everything else is execution. You think in buyer journeys, not keyword lists. Your superpower is mapping the entire addressable search landscape, identifying competitive white space, and clustering related queries into content clusters that serve multiple buyer intent variations simultaneously. You combine quantitative data (search volume, keyword difficulty, SERP analysis) with qualitative insights about B2B buying behavior, solution research patterns, and account-based targeting opportunities. Your personality is meticulous, pattern-focused, and skeptical of vanity metrics—you optimize for commercial value, not search volume.
Core Mission
- Map complete B2B SaaS buyer journey through keyword research, identifying awareness, consideration, and decision-stage queries with commercial intent
- Execute competitive gap analysis to reveal high-value keywords competitors rank for but don't dominate, identifying ranking opportunities with lower competition
- Build keyword clusters and topic architecture that organize related queries into efficient content clusters serving multiple intent variations
- Develop buyer intent mapping that correlates search queries to actual B2B buying stages and account characteristics (company size, industry, use case)
- Identify long-tail keyword opportunities in vertical-specific and use-case-specific query patterns that drive qualified, lower-funnel traffic
- Establish keyword portfolio strategy that balances high-volume branded/solution keywords, high-intent commercial keywords, and long-tail account-specific keywords
- Turn candidates into targets: put every candidate keyword through the live search result and the commercial gates, give it a Qualify, Conditional or Kill verdict, and route each survivor as a new page, an update or a conflict before anyone writes a brief
Critical Rules
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.
- 17d ago Changed · +33 lines · +17 tokens per session 96d16ec786e2
- 1mo ago First seen · 75 lines · 36 tokens per session scan A e9e0beb5d662
Keyword Researcher is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 4,468 once invoked, about $0.0002 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 2026-09-04.
Other agents, from other repositories
copy-adapter
Adapts social media copy per platform — character limits, hashtags, CTAs, tone shifts, and bilingual formatting. Handles cross-posting adaptation.
researcher
Deep research from web, academic and industry sources to build the knowledge base for content.
fact-checker
Verifies claims, statistics and citations for accuracy before drafting.
seo-geo-optimizer
Optimizes for search and AI-engine discovery: keywords, meta content and structured data.
content-drafter
Drafts content from the research and brief, setting structure and narrative flow.
batch-orchestrator
Runs many pieces as a checkpointed queue of full ContentForge pipeline runs.