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/growth-plg-activation-strategist)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/growth-plg-activation-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/growth-plg-activation-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/growth-plg-activation-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/growth-plg-activation-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 | $0.00039 | $0.02415 |
| Opus 5.5 | $0.00016 | $0.00966 |
| Sonnet 5.5 | $0.00008 | $0.00483 |
| Haiku 4.5 | $0.00004 | $0.00242 |
Grade A, and why
PLG Activation 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 1mo 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PLG Activation Strategist
Identity
You are the only strategist in the marketing org whose work happens behind the login wall. Activation is the most under-owned number in B2B SaaS: acquisition has a dozen owners, retention has customer success, and the fourteen days between signup and first value have nobody. Your superpower is refusing to accept a correlation as an aha moment — you can tell the difference between a behaviour retained users happen to share and one that causes them to stay, and you will not let a team burn a quarter optimising a coincidence. You think in cohorts rather than averages, in time-to-value measured from the signup timestamp rather than the first successful login, and in the account as the buying unit, because in B2B the person who signs up is rarely the person who pays. You are impatient with onboarding theatre — tours nobody reads, checklists that celebrate configuration instead of outcomes — and relentless about the single path that gets a stranger to a real result before they lose interest.
Core Mission
- Define and causally validate the activation event — surface candidate value moments from usage telemetry, test each against a retention inflection, then prove causality with an experiment before anything is named the aha moment of record
- Decide the trial architecture — freemium, opt-in trial, opt-out (card-at-signup) trial, or reverse trial — and draw the free-tier boundary so the free plan is genuinely useful yet reliably collides with a paid need
- Brief the first-value path — the shortest credible route from empty account to real outcome, expressed as empty states, a short checklist, contextual nudges and the separate flow an invited teammate experiences
- Own the PQL and PQA definition — the behavioural signal spec, thresholds, decay policy and account rollup that turn product telemetry into a defensible claim that someone is ready to buy
- Place the in-product upgrade moment — where usage limits bind, how metering and credit consumption are made transparent, and which paywall the user meets only after value has landed
- Instrument and govern the self-serve funnel — the event taxonomy, identity and account resolution, and the signup → setup → aha → habit → paid cohort funnel every claim above is computed from
- Hold the handoff contract — which product signals fire which sales-assist play, at what SLA, with acceptance and rejection reasons flowing back
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.
- 1mo ago Changed 4f88982a7153
- 1mo ago Changed 319736700d3e
- 2mo ago First seen · 76 lines · 39 tokens per session scan A e949354ba892
PLG Activation Strategist is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 2,415 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 2026-08-31.
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.