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Omh Lifecycle Growth

CommunityPopular
rlaope
omh-lifecycle-growth

[omh] Turn an observed onboarding, activation, retention, re-engagement, referral, or monetization problem into one consent-safe in-app journey or growth experiment plan with a bounded readout and an explicit decision. Use when the user says: lifecycle-growth, lifecycle growth, lifecycle marketing, lifecycle messaging, in-app journey, in-app message campaign, onboarding journey, onboarding nudge.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-lifecycle-growth
Stars
2.7K
Forks
194
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Lifecycle Growth AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-lifecycle-growth .claude/skills/omh-lifecycle-growth
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Lifecycle Growth in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Lifecycle Growth on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Lifecycle Growth is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Lifecycle Growth

This is an OMH lifecycle-growth workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

lifecycle-growth exists so audience eligibility, consent and frequency safety, sticky exposure, causal measurement, and the stop decision travel together in one plan instead of being assembled ad hoc from analysis, copy, scheduling, and connector work.

Do Not Use When

  • The user only wants a one-off message, email, banner, or push copy rewrite with no audience, experiment, or decision; use content-operator.
  • The user wants generic exploration or calculation over a supplied cohort, retention, conversion, or segment table with no journey or experiment to design; use data-analysis.
  • The user wants a recurring schedule, cron, or digest cadence for an already-decided operation rather than a lifecycle intervention; use automation-blueprint.
  • The user asks to send a message, change a feature flag, create a segment, or start an experiment in a provider now; use connector-operator with explicit authorization and observed results.
  • The user needs a PRD, prioritization frame, or roadmap for a product change rather than a journey or experiment; use product-brief.

Examples

Good example:

  • Prompt: Our day-7 retention dropped for new workspace admins; design an in-app onboarding journey and a holdout experiment so we know whether it works.
  • Expected behavior: Prepare the brief, audience trigger policy, safety policy, experiment plan with sticky assignment and actual exposure, a readout scaffold, and an approval-gated handoff disposition.
  • Why: The request spans lifecycle stage, audience, treatment, and causal measurement, which is the whole lifecycle-growth loop rather than one sibling's slice.

Bad example:

  • Prompt: Send the re-engagement push to every inactive user tonight.
  • Expected behavior: Route to connector-operator with explicit authorization, or return HOLD if consent, suppression, and frequency eligibility are unknown.
  • Why: An immediate external send is a connector action, and lifecycle-growth never sends or claims delivery.

Completion Checklist

  • The target behavior, baseline, eligible audience, safety policy, experiment design, readout, and decision owner are named or marked HOLD.
  • Prepared plan, human approval, observed delivery or display, observed user action, observed outcome, and causal claim are reported as separate states.
  • The readout disposition is exactly ship, rollback, review, or insufficient_data, and every proposed handoff names its owning workflow, approver, and stop conditions.

Recovery Notes

  • If consent, suppression, identity, event semantics, denominator, or the decision owner is unknown, return HOLD with the missing fields and ask for the one input that unblocks the smallest next step.
  • If provider or data evidence for delivery, display, action, or outcome is unavailable, keep every readout stage not_observed and set the disposition to insufficient_data or review rather than ship.
  • If a readout is missing, ask for the analysis run state and its observation time before concluding anything: a queued or long-running analysis holds for reconciliation, while failed, canceled, and never-started runs each need a different next step.

Use When

Use when a product or growth owner wants to improve a lifecycle stage and needs the target behavior, eligible audience, safety policy, experiment design, launch/rollback gates, measurement readout, and ship/rollback/review/insufficient_data decision assembled as one evidence-bounded plan.

Strong routing signals: `lifecycle-growth`, `lifecycle growth`, `lifecycle marketing`, `lifecycle messaging`, `in-app journey`, `in-app message campaign`, `onboarding journey`, `onboarding nudge`, `activation campaign`, `activation experiment`, `retention campaign`, `retention experiment`, `re-engagement campaign`, `win-back campaign`, `referral experiment`, `monetization experiment`, `growth experiment`, `holdout experiment`, `product-led growth loop`, `라이프사이클 마케팅`, `온보딩 여정`, `그로스 실험`

Catalog Metadata

Category: strategy Phase: lifecycle-growth Quality tier: decision-gated Reasoning demand: standard

Quality bar:

  • Define the value-bearing behavior and its baseline before any campaign or treatment is proposed.
  • Separate assignment from actual exposure, and eligible, attempted, delivered, displayed, acted, and outcome stages from one another.
  • Keep copy, supplied-data calculation, recurring scheduling, external sends, and PRD work with their owning workflows.
  • Require a named human approval before any launch handoff and observed evidence before any delivery or outcome claim.

Required inputs:

  • lifecycle objective and stage
  • target segment
  • event schema and baseline
  • channels or product surfaces
  • consent and policy constraints
  • experiment budget
  • decision owner

Expert clarification questions:

  • lifecycle objective and stage
    • English: Which lifecycle stage (onboarding, activation, retention, re-engagement, referral, monetization) and which value-bearing user behavior should improve, from what baseline?
    • Korean: 어떤 라이프사이클 단계(온보딩, 활성화, 리텐션, 재참여, 추천, 수익화)에서 어떤 가치 있는 사용자 행동을 어느 기준선에서 개선해야 하나요?

Expected outputs:

  • lifecycle_growth_brief/v1
  • audience_trigger_policy/v1
  • lifecycle_safety_policy/v1
  • growth_experiment_plan/v1
  • growth_measurement_readout/v1
  • growth_handoff_disposition/v1

Artifact expectations:

  • prepared lifecycle-growth plan and readout, as metadata-only records with safe references, when a wrapper captures them

Safety rules:

  • Fail closed: unknown consent, suppression, frequency eligibility, event semantics, identity, denominator, or decision owner returns HOLD and blocks a launch-ready handoff.
  • Consent and suppression come only from supplied records; product usage or the absence of an opt-out never implies either.
  • Do not claim a message was sent, a flag was changed, a treatment was displayed, a user acted, an outcome moved, or an experiment succeeded without observed provider, runtime, or data evidence.
  • Delivery and click counts are not product or revenue impact; a causal claim needs a valid observed experiment or another named identification method.
  • Retain bounded metadata and safe references only; never store user identity, event payloads, message bodies, consent records, or transcripts in durable artifacts.
  • Treat small samples, novelty effects, seasonality, concurrent interventions, and inconsistent event semantics as blockers or stated uncertainty, not as results.
  • A throttle window is identified by its configured key or expression plus the resolved value, scoped to a recipient or tenant; a resolved value is never re-read as a second key, a missing static value stays ungrouped, and an empty dynamic value falls back to the default window.
  • Per-step matched and skipped outcomes carry a reason and status but never evaluated values or secrets; a step trace is best-effort diagnostics, not delivery evidence, and its absence must not block or fail a send.
  • Production or published workflow content is view-only in prepared guidance; mutations go to a development or draft copy, then an explicit promotion decision, and only an observed provider result proves the promotion happened.
  • A missing analysis result is not proof that no analysis is running; name the run state and the time it was observed, and never report a queued or running analysis as failed, canceled, absent, or complete.
  • Elapsed time is a delay warning measured against a supplied service expectation, never evidence about a run; no fixed staleness cutoff may overwrite an observed in-flight state, and analysis-job runtime, experiment minimum runtime, and source-data freshness stay three separate questions.
  • While the latest observed run is queued or running, do not start or recommend another analysis; reconcile the existing work first, and select the newest in-flight run rather than the newest run of any kind.
  • A cancellation or status-reconciliation handoff is prepared, never performed: it names the exact run and scope, stays prepared_not_observed, and only an observed provider result may record acceptance or a terminal cancellation.

Procedure: load references/procedure.md.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Omh Lifecycle Growth AI skill do?

[omh] Turn an observed onboarding, activation, retention, re-engagement, referral, or monetization problem into one consent-safe in-app journey or growth experiment plan with a bounded readout and an explicit decision. Use when the user says: lifecycle-growth, lifecycle growth, lifecycle marketing, lifecycle messaging, in-app journey, in-app message campaign, onboarding journey, onboarding nudge.

Why use Omh Lifecycle Growth on TypingMind?

Because you install it once and use it with any model. Omh Lifecycle Growth is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Lifecycle Growth in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-lifecycle-growth. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Omh Lifecycle Growth?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Lifecycle Growth?

As many as you like. As long as a model supports skills, you can use Omh Lifecycle Growth with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Lifecycle Growth AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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