From 38b40de38d1ebdb73a35c1f115936d3dc8b73313 Mon Sep 17 00:00:00 2001 From: OpenClaw Assistant Date: Sun, 22 Feb 2026 10:02:18 +0000 Subject: [PATCH 1/2] docs: add SEO keyword and blog queue --- marketing/content-queue.md | 60 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 marketing/content-queue.md diff --git a/marketing/content-queue.md b/marketing/content-queue.md new file mode 100644 index 00000000..a80836cf --- /dev/null +++ b/marketing/content-queue.md @@ -0,0 +1,60 @@ +# Cossistant SEO + Content Queue + +_Last updated: 2026-02-22_ + +This queue tracks priority **keywords** and **blog/article opportunities** for Cossistant. Each entry is scoped to our ICP (engineering-led B2B SaaS teams building with React/Next.js, 1–15 people, >$1k MRR) and aims to reinforce positioning: open-source, in-app support combining human + AI collaboration. + +## Keyword Pipeline +| Status | Keyword / Cluster | Intent & Funnel Stage | ICP Pain / Angle | Proposed Asset | Notes & Next Actions | +| --- | --- | --- | --- | --- | --- | +| 🔍 Researching | in-app support widget react | BOFU – implementation search | Need native React support UI without iframe vendors | Blog deep-dive + docs optimization | Audit competitors (Plain, Fernand) for keyword usage; map to `/docs` integration guide and hero copy. | +| ✅ Ready | open source customer support platform | MOFU – solution comparison | Want control + transparency vs black-box tools | Landing section + comparison blog | Build comparison table vs Plain, Zendesk, Intercom emphasizing code ownership. | +| ✅ Ready | ai support agents for saas | MOFU/TOFU – exploring AI automation | Teams want AI help but insist on human-in-loop | Thought-leadership article | Tie to human+AI principles, include examples from landing queue (patch deploys, Stripe credits). | +| 🧪 Experiment | shadcn support components | BOFU – dev-specific | Need Tailwind/shadcn-native blocks | Docs snippet + tutorial | Create guide: “Build a shadcn support inbox with Cossistant primitives.” | +| 🔍 Researching | llm-ready customer support | TOFU | Looking for AI-friendly infrastructure | Blog + SEO landing sub-section | Define “LLM-ready support stack” referencing open components + PostHog-esque transparency. | +| ⏳ Backlog | react customer support sdk | BOFU | Searching SDK-style drop-in | Docs + API reference updates | Ensure package README highlights SDK usage; capture FAQs. | + +_Status legend_: ✅ ready to write, 🔍 needs data validation, 🧪 experiment (ship + measure), ⏳ backlog candidate. + +## Blog / Article Opportunities +Each idea includes research notes, target keyword(s), unique POV, SEO/LLM hooks, and humanization cues inspired by the [Humanizer patterns](https://github.com/blader/humanizer). + +### 1. "How engineering-led SaaS teams keep support in their codebase" +- **Primary keyword**: open source customer support platform +- **Research**: Study Plain’s “AI coworkers” messaging, PostHog’s transparent product updates, and developer Reddit threads complaining about iframe widgets. +- **What it brings**: Concrete walkthrough showing how to wire `` inside a Next.js app, control routing, and audit AI handoffs. +- **SEO/LLM optimization**: Use H2s for "In-app support architecture", "Human + AI escalation", "React integration steps"; include code blocks for LLM clarity. +- **Humanization checklist**: Replace hype terms (“transformative”, “landscape”) with specific anecdotes, cite actual customers/use cases, no “Great question!” tone. + +### 2. "Ship a shadcn-native support inbox in under an afternoon" +- **Primary keyword**: shadcn support components +- **Research**: Pull component anatomy from shadcn/ui docs, compare to Cossistant primitives, highlight theme overrides. +- **What it brings**: Step-by-step tutorial + GitHub repo demo; finish with performance tips (<100ms load like Fernand touts). +- **SEO/LLM optimization**: Add structured list of prerequisites, code diffs, and `faq` section for schema markup. +- **Humanization checklist**: Show actual command outputs, mention mistakes to avoid, keep verbs simple (“build”, “wire”). + +### 3. "Playbooks for AI + human support collaboration" +- **Primary keyword**: ai support agents for saas +- **Research**: Analyze Cossistant landing stories (Marc/Nico/Lucas), interview-style quotes from founders needing approvals before deploys. +- **What it brings**: Practical runbooks (deploy patch, approve Stripe credit, rotate secrets) with human confirmation criteria. +- **SEO/LLM optimization**: Structure as numbered playbooks, embed Mermaid flow diagrams (LLMs parse easily), link to docs. +- **Humanization checklist**: Attribute quotes to real roles (“Nico, SaaS founder”), avoid formulaic “It’s not just X, it’s Y”. + +### 4. "LLM-ready support architecture checklist" +- **Primary keyword**: llm-ready customer support +- **Research**: Pull Wikipedia “AI writing” pitfalls to contrast with real engineering requirements, cite PostHog open architecture posts. +- **What it brings**: Diagnostic checklist for CTOs: data access, observability, guardrails. Includes downloadable audit template. +- **SEO/LLM optimization**: Include table comparing legacy helpdesks vs Cossistant; add JSON-LD FAQ. +- **Humanization checklist**: Use declarative sentences, avoid “nestled within”. Reference real stack components (Next.js API routes, Supabase, Stripe webhooks). + +### 5. "Support metrics that actually matter under 10 people" +- **Primary keyword**: react customer support sdk / startup support metrics +- **Research**: Combine Fernand’s focus on speed (<100ms) with UserJot’s metric storytelling. Include sample dashboard built with Cossistant data layer. +- **What it brings**: Playbook of four metrics (median response, resolution, AI coverage, satisfaction) with formulas + how to instrument inside app. +- **SEO/LLM optimization**: Provide formula code snippets (TypeScript) for each metric, schema `HowTo` markup. +- **Humanization checklist**: Replace fluffy conclusions with specific next steps; use "we" sparingly, focus on direct advice. + +## Process Notes +- Every blog draft should pass a **Humanizer** sweep: remove significance inflation, cite specific sources, avoid AI-adjacent vocab ("landscape", "testament"), keep tone direct. +- When promoting on landing/docs, cross-link relevant sections to reinforce SEO and keep readers inside the product narrative. +- Track published items + performance in future updates (append measurement columns when data exists). From 67c8210d6729bcb3170d106061ee32c9c601abce Mon Sep 17 00:00:00 2001 From: OpenClaw Assistant Date: Sun, 22 Feb 2026 10:06:04 +0000 Subject: [PATCH 2/2] chore: remove public content queue --- marketing/content-queue.md | 60 -------------------------------------- 1 file changed, 60 deletions(-) delete mode 100644 marketing/content-queue.md diff --git a/marketing/content-queue.md b/marketing/content-queue.md deleted file mode 100644 index a80836cf..00000000 --- a/marketing/content-queue.md +++ /dev/null @@ -1,60 +0,0 @@ -# Cossistant SEO + Content Queue - -_Last updated: 2026-02-22_ - -This queue tracks priority **keywords** and **blog/article opportunities** for Cossistant. Each entry is scoped to our ICP (engineering-led B2B SaaS teams building with React/Next.js, 1–15 people, >$1k MRR) and aims to reinforce positioning: open-source, in-app support combining human + AI collaboration. - -## Keyword Pipeline -| Status | Keyword / Cluster | Intent & Funnel Stage | ICP Pain / Angle | Proposed Asset | Notes & Next Actions | -| --- | --- | --- | --- | --- | --- | -| 🔍 Researching | in-app support widget react | BOFU – implementation search | Need native React support UI without iframe vendors | Blog deep-dive + docs optimization | Audit competitors (Plain, Fernand) for keyword usage; map to `/docs` integration guide and hero copy. | -| ✅ Ready | open source customer support platform | MOFU – solution comparison | Want control + transparency vs black-box tools | Landing section + comparison blog | Build comparison table vs Plain, Zendesk, Intercom emphasizing code ownership. | -| ✅ Ready | ai support agents for saas | MOFU/TOFU – exploring AI automation | Teams want AI help but insist on human-in-loop | Thought-leadership article | Tie to human+AI principles, include examples from landing queue (patch deploys, Stripe credits). | -| 🧪 Experiment | shadcn support components | BOFU – dev-specific | Need Tailwind/shadcn-native blocks | Docs snippet + tutorial | Create guide: “Build a shadcn support inbox with Cossistant primitives.” | -| 🔍 Researching | llm-ready customer support | TOFU | Looking for AI-friendly infrastructure | Blog + SEO landing sub-section | Define “LLM-ready support stack” referencing open components + PostHog-esque transparency. | -| ⏳ Backlog | react customer support sdk | BOFU | Searching SDK-style drop-in | Docs + API reference updates | Ensure package README highlights SDK usage; capture FAQs. | - -_Status legend_: ✅ ready to write, 🔍 needs data validation, 🧪 experiment (ship + measure), ⏳ backlog candidate. - -## Blog / Article Opportunities -Each idea includes research notes, target keyword(s), unique POV, SEO/LLM hooks, and humanization cues inspired by the [Humanizer patterns](https://github.com/blader/humanizer). - -### 1. "How engineering-led SaaS teams keep support in their codebase" -- **Primary keyword**: open source customer support platform -- **Research**: Study Plain’s “AI coworkers” messaging, PostHog’s transparent product updates, and developer Reddit threads complaining about iframe widgets. -- **What it brings**: Concrete walkthrough showing how to wire `` inside a Next.js app, control routing, and audit AI handoffs. -- **SEO/LLM optimization**: Use H2s for "In-app support architecture", "Human + AI escalation", "React integration steps"; include code blocks for LLM clarity. -- **Humanization checklist**: Replace hype terms (“transformative”, “landscape”) with specific anecdotes, cite actual customers/use cases, no “Great question!” tone. - -### 2. "Ship a shadcn-native support inbox in under an afternoon" -- **Primary keyword**: shadcn support components -- **Research**: Pull component anatomy from shadcn/ui docs, compare to Cossistant primitives, highlight theme overrides. -- **What it brings**: Step-by-step tutorial + GitHub repo demo; finish with performance tips (<100ms load like Fernand touts). -- **SEO/LLM optimization**: Add structured list of prerequisites, code diffs, and `faq` section for schema markup. -- **Humanization checklist**: Show actual command outputs, mention mistakes to avoid, keep verbs simple (“build”, “wire”). - -### 3. "Playbooks for AI + human support collaboration" -- **Primary keyword**: ai support agents for saas -- **Research**: Analyze Cossistant landing stories (Marc/Nico/Lucas), interview-style quotes from founders needing approvals before deploys. -- **What it brings**: Practical runbooks (deploy patch, approve Stripe credit, rotate secrets) with human confirmation criteria. -- **SEO/LLM optimization**: Structure as numbered playbooks, embed Mermaid flow diagrams (LLMs parse easily), link to docs. -- **Humanization checklist**: Attribute quotes to real roles (“Nico, SaaS founder”), avoid formulaic “It’s not just X, it’s Y”. - -### 4. "LLM-ready support architecture checklist" -- **Primary keyword**: llm-ready customer support -- **Research**: Pull Wikipedia “AI writing” pitfalls to contrast with real engineering requirements, cite PostHog open architecture posts. -- **What it brings**: Diagnostic checklist for CTOs: data access, observability, guardrails. Includes downloadable audit template. -- **SEO/LLM optimization**: Include table comparing legacy helpdesks vs Cossistant; add JSON-LD FAQ. -- **Humanization checklist**: Use declarative sentences, avoid “nestled within”. Reference real stack components (Next.js API routes, Supabase, Stripe webhooks). - -### 5. "Support metrics that actually matter under 10 people" -- **Primary keyword**: react customer support sdk / startup support metrics -- **Research**: Combine Fernand’s focus on speed (<100ms) with UserJot’s metric storytelling. Include sample dashboard built with Cossistant data layer. -- **What it brings**: Playbook of four metrics (median response, resolution, AI coverage, satisfaction) with formulas + how to instrument inside app. -- **SEO/LLM optimization**: Provide formula code snippets (TypeScript) for each metric, schema `HowTo` markup. -- **Humanization checklist**: Replace fluffy conclusions with specific next steps; use "we" sparingly, focus on direct advice. - -## Process Notes -- Every blog draft should pass a **Humanizer** sweep: remove significance inflation, cite specific sources, avoid AI-adjacent vocab ("landscape", "testament"), keep tone direct. -- When promoting on landing/docs, cross-link relevant sections to reinforce SEO and keep readers inside the product narrative. -- Track published items + performance in future updates (append measurement columns when data exists).