r/better_claw

Save this. You'll need it

OPENCLAW (v2026.8.1)

First 5 minutes after install:

openclaw config set gateway.bind loopback
openclaw doctor --fix
openclaw gateway restart

The commands you'll use weekly:

openclaw gateway status          # is it running
openclaw gateway restart         # fix 90% of telegram issues
openclaw doctor --fix            # fix 70% of everything else
openclaw status --all            # full diagnostic
openclaw logs --follow           # watch live
openclaw channels status --probe # check telegram/slack/etc

Session management:

/new                    # clear conversation, keep memory
/btw <question>         # side question, doesn't pollute session
/model sonnet           # switch model mid-conversation
/compact                # force context compaction

The config that saves money:

json

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "deepseek/deepseek-v4-flash"
      },
      "heartbeat": {
        "every": "30m",
        "isolatedSession": true,
        "lightContext": true
      },
      "maxHistoryMessages": 20
    }
  }
}

Skills and tools:

openclaw skills list             # what's installed
openclaw skills install <name>   # add from ClawHub
openclaw tools                   # what tools are active

When it breaks (in this order):

openclaw status --all                                    # 1. what's the state
openclaw doctor --fix                                    # 2. auto-repair
openclaw gateway restart                                 # 3. restart clean
rm ~/.openclaw/agents/main/sessions/*.lock               # 4. ghost locks
curl -sf http://127.0.0.1:18789/health || echo "dead"    # 5. is it alive

Files that matter:

~/.openclaw/openclaw.json         # main config
~/.openclaw/agents/main/SOUL.md   # personality + boundaries
~/.openclaw/agents/main/MEMORY.md # what it remembers
~/.openclaw/agents/main/AGENTS.md # procedural rules

HERMES (v0.20.0 "The Herald Release")

First 5 minutes after install:

curl -fsSL https://hermes.nousresearch.com/install | bash
hermes setup
hermes doctor

The commands you'll use weekly:

hermes                           # interactive chat
hermes chat -q "hello"           # one-shot (test if it works)
hermes gateway status            # is it running
hermes gateway restart           # fix stale polling
hermes doctor                    # diagnostics
hermes model                     # interactive model picker
hermes model set <model>         # set model directly

Session management:

/new                    # fresh session
/compact                # compress context
/compress               # same thing
/learn                  # turn a workflow into a skill
/moa                    # mixture-of-agents mode
hermes sessions list    # see all sessions
hermes sessions clean   # prune old sessions

Skills and memory:

hermes skills list               # what's installed
hermes skills install <source>   # add a skill
hermes skills enable <name>      # activate
hermes curator                   # manage auto-generated skills
hermes memory setup              # configure memory provider

Profiles (isolated configs):

hermes profile list              # see all profiles
hermes profile create <name>     # new isolated config
hermes profile use <name>        # switch active profile

When it breaks (in this order):

hermes chat -q "hello"                                   # 1. can it think at all
hermes gateway status                                    # 2. is gateway alive
hermes config show | grep -A3 allowed                    # 3. is it ignoring you
cat ~/.hermes/active_profile                             # 4. right profile?
ps aux | grep -E 'hermes|openclaw' | grep -v grep       # 5. duplicate pollers?
dmesg | grep -i "killed process"                        # 6. OOM killed?

Files that matter:

~/.hermes/config.yaml            # main config
~/.hermes/active_profile         # which profile is live
~/.hermes/souls/default.md       # personality (SOUL.md equivalent)
~/.hermes/state.db               # session database
~/.hermes/skills/                # auto-generated + installed skills

SIDE BY SIDE

	OpenClaw 2026.8.1	Hermes v0.20.0
Install	npm or Docker	one curl command
Config format	JSON	YAML
Messaging platforms	50+	28
Memory	Markdown files, unlimited	3-layer (session, episodic, procedural), ~2,200 char core
Self-learning	No	Yes (auto-generated skills)
Profiles	--dev or --profile	hermes profile create/use
Health check	openclaw doctor --fix	hermes doctor
Desktop app	Electron	Native (macOS/Linux/Windows)
Clear session	/new	/new
Skill hub	ClawHub (13,700+)	Skills directory + /learn
Gateway security	Bind loopback manually	Public bind requires auth since June 2026
Latest major feature	Secret egress binding, GPT-5.6 Ultra support	Voice streaming with barge-in, A2A v1.0

MODEL ROUTING (works on both)

Task type	Model	Why
Heartbeats, crons, classification	Gemini Flash (free) or Groq (free)	DeepSeek raised prices 4.7x on Aug 16. Free tiers are the new background default.
Conversations, drafts, research	Sonnet 5 ($3/$15) or GLM-5.2 (~$1/$3.20)	Quality where you read the output.
Escalation	Opus 5 ($5/$25)	Invoked on purpose, 2-3x a week.
Long context batch	Kimi K3 ($3/$15, 1M context)	When the window is the feature.
Local	Qwen3.6-35B-A3B or Gemma 4 12B	16GB hardware, $0.

THE 5-COMMAND CHEATSHEET

Whatever platform you're on, these five fix 90% of problems:

1. Check status     → openclaw status --all / hermes doctor
2. Fix config       → openclaw doctor --fix / hermes doctor
3. Restart gateway  → openclaw gateway restart / hermes gateway restart
4. Clear session    → /new
5. Lock gateway     → openclaw config set gateway.bind loopback

Bloomberg confirmed Saturday that Stripe finalized the deal to acquire OpenRouter for over $7 billion. Three months ago OpenRouter raised its Series B at a $1.3 billion valuation. 5.4x markup in 90 days.

If you route your agent's API calls through OpenRouter, this affects you. Probably not today. Probably eventually.

What OpenRouter is, for anyone who skipped the acronym

One API key, 400+ models from dozens of providers. You pick a model, OpenRouter routes the call to whatever provider serves it, handles billing, and gives you automatic fallback when one goes down. A lot of people in this community use it as their only API key.

That last sentence is the one worth sitting with.

What probably doesn't change soon

The free tier. It's the growth engine. Stripe paid $7B for the user base and transaction flow, and the free tier is what fills the funnel. Killing it immediately would be burning the thing they bought. Same reason Stripe kept Plaid's developer tier alive after considering that acquisition years ago.

The API interface. Your openrouter/free calls, your model IDs, your routing preferences. A payments company acquiring an API gateway has zero incentive to break the API. They want more traffic through it, not less.

Day-to-day reliability. OpenRouter's infrastructure doesn't get worse because Stripe owns it. If anything, Stripe's operational maturity makes outages less likely.

What might change over time

Pricing. Stripe is a payments infrastructure company. Their business model is a percentage of transaction value flowing through their pipes. OpenRouter's current margin on routed calls is thin. Stripe's incentive is either higher volume at the same margin or higher margin at the same volume. Neither of those means cheaper for you.

The free model list. It was already shrinking before this deal. Several :free endpoints got delisted in the last month alone. An acquirer focused on revenue per transaction has less reason to subsidize free models than a startup focused on growth.

Provider neutrality. CNBC reported in July that Chinese-origin models captured 46% of US enterprise token usage on OpenRouter. Stripe is a US financial company with regulatory and compliance obligations that a startup could navigate quietly. Whether DeepSeek, Moonshot, and Z.ai stay equally accessible through a Stripe-owned gateway is a question that didn't exist last week.

Data handling. OpenRouter's privacy policy governs your data today. Post-acquisition, that policy lives inside Stripe's broader data practices. For agent traffic that includes email content, calendar data, and business documents, the change in data controller matters. Read the updated terms when they publish them.

What this means for the "$10 deposit" crowd

A lot of people in here bought $10 of OpenRouter credits specifically for the 50→1,000 daily request bump on free models. That $10 is still yours and the rate increase is still permanent. But if the free model list keeps shrinking and pricing adjusts, the value of that deposit changes with it.

The structural question underneath

OpenRouter's CEO described the company as "the Stripe for AI." Now it literally is Stripe. Which means the routing layer, the billing layer, and the payment processing layer are all one company.

If your entire agent runs through OpenRouter, your model access, your fallback logic, your billing, and your cost data all live behind one corporate decision. That was true before the acquisition too, but "small startup that needs your loyalty" and "public-scale payments company" make different decisions about the same levers.

What I'd actually do today

If OpenRouter is your only provider key, today is the day to stop that being true. Not because Stripe is going to break something tomorrow. Because single-provider dependency on any infrastructure was always the risk, and a $7B acquisition is the kind of event that makes the risk visible.

Get a direct key from your most-used model's provider. If you run DeepSeek heavily (though see Saturday's pricing post), get a direct DeepSeek key. If you run Gemini, get a Google AI Studio key. If you use Claude, get an Anthropic key. Keep OpenRouter as the aggregator and fallback. Stop using it as the only path.

Takes ten minutes. One key per provider, pasted into your config alongside the OpenRouter key, with a preference order.

On BetterClaw (free) this is already how BYOK works: you paste provider keys directly, no aggregator in the middle unless you want one. I'm biased here and I know it, but the architecture of "your key, your provider, no intermediary" is the version that doesn't care about this headline or the next one like it.

Two days ago DeepSeek raised prices 4.7x. Yesterday Stripe bought the routing layer half this community depends on. These aren't related events but they're the same lesson: every piece of your agent stack that you don't control is a piece that can change without asking you.

The fix isn't paranoia. It's redundancy. Multiple provider keys, a fallback chain, and context that lives in files you own rather than inside any single platform.

Build so the next headline is interesting rather than urgent.

Spent a week going through every AI automation and AI agent job listing on Upwork, plus cross-referencing against GigRadar's proposal dataset (133,000+ proposals) and Upwatcher's segment data. Wanted to know what people actually hire for, not what freelancers think they hire for.

The gap between those two things is where most people lose money.

The market in three numbers

AI category on Upwork hit $300M+ annualized gross services volume in late 2025, up 50% year over year. AI Integration and Automation specifically grew over 90%. Demand for AI-related skills more than doubled.

Sounds like a gold rush. Here's the other side: the AI and Machine Learning subcategory has a 7.21% reply rate on proposals, against a platform average of 7.45%. Every freelancer already pivoted to AI. The supply caught up to the demand and then passed it.

The people still winning are the ones who ship production systems, not the ones who list "prompt engineering" as a skill.

The 7 categories that actually get funded

After sorting through the listings, almost every funded AI automation job falls into one of seven buckets. Listed by how often they appeared, not by budget size.

1. Zapier/Make to n8n migration. The highest conversion category and it's not close. These clients already understand automation, already have workflows, and are posting because they want to stop paying $400-600/month for Zapier. They have a defined scope, a clear budget motivation, and they've already decided the problem is worth solving. Typical budget: $500-2,000 fixed price. If you can move 15 Zaps to n8n in a week, this is the easiest money on the platform right now.

2. Email and inbox automation. Triage, classification, draft replies, follow-up sequences. Lawyers, agencies, and founders with 100+ emails a day who want it down to 10 minutes of review. This is the use case with the widest range of buyers, from solo founders at $500 to mid-size firms at $5,000+. The winning proposals show a working demo, not a feature list.

3. AI agent setup and configuration. "Set up OpenClaw/Hermes on my VPS" or "configure an AI assistant I can text on Telegram." This barely existed 8 months ago and it's now a distinct category. Budgets range wildly ($200 for a basic install to $2,000+ for a full workflow buildout). The NYC post from a few months ago about setting up agents for clients at $499-1,199 was ahead of this curve.

4. CRM + AI integration. Connect HubSpot or Salesforce to an LLM so it auto-enriches contacts, drafts follow-ups, scores leads, or summarizes call notes. Enterprise-adjacent, higher budgets ($2,000-10,000), and the clients want someone who knows the CRM as much as the AI. Pure AI skills without CRM experience don't win these.

5. RAG and internal knowledge bases. "We have 10,000 support docs and we want a chatbot that answers from them." The category that every LangChain tutorial prepares you for. Budgets $1,500-8,000 depending on doc volume and whether it needs to be customer-facing. The quality bar went up fast because the first wave of RAG chatbots hallucinated and clients got burned. They now ask about retrieval accuracy in the job post, which they didn't a year ago.

6. Content generation pipelines. Blog posts from SEO data, social media from long-form content, newsletter drafts from RSS feeds. High volume, lower budgets ($300-1,500), and the clients care about output consistency more than capability. These are the jobs where showing a running n8n workflow or a BetterClaw scheduled task in your proposal beats talking about GPT-5.

7. Data collection and monitoring agents. Scrape competitors, track prices, monitor job boards, watch regulatory changes. Clients want a system that runs daily and delivers a report, not a one-time scrape. Recurring revenue territory if you build it as a service. $500-3,000 for setup, $100-300/month for managed monitoring.

What almost never gets funded

"Build me an autonomous AI agent that runs my business." These postings exist. They get 50+ proposals. They almost never convert because the scope is undefined and the client's expectations are impossible. Skip them.

Prompt engineering as a standalone service. The market priced this in. It's the baseline expectation now, not a skill you sell separately. Clients in 2026 assume you can write prompts. They hire you because you can ship a working system.

"I need an AI chatbot" with a $100 budget. Volume is high. Value per hour is not. Filter by budget floor, $500 minimum, and your proposal quality goes up because you stop competing against 200 people willing to work for $50.

The rates

Median hourly for AI automation: roughly $30/hr, versus $25/hr platform-wide. That 20% premium is real but it's shrinking as supply grows.

P75 is around $40/hr. P90 is around $60/hr. The top decile is where production experience lives, people who've shipped systems that run in production, not people who built a demo.

Job postings that mention "AI agent," "LLM workflow," or "autonomous" in the description carry budgets 2-3x higher than equivalent non-AI automation work. The word "agent" is a pricing signal even when the deliverable is a cron job with an LLM step.

What I'd actually do with this data

Pick one category. Not AI automation broadly. One of the seven above. Get so good at delivering that category that your profile shows five completed projects in it, and your proposal can say "I built this exact thing last month, here's how it went."

The freelancers winning on Upwork right now aren't generalists who can do all seven. They're specialists who picked one, built a repeatable delivery process, and charge based on the outcome instead of the hours. The n8n migration person charges $1,500 flat and delivers in 4 days. The email triage person charges $2,000 and includes 30 days of managed support. The RAG person charges $5,000 and guarantees retrieval accuracy above a threshold.

The platform is saturated for "I do AI." It's still undersupplied for "I ship this specific thing, reliably, in a week."

I struggle to find use cases for automations within the ChatGPT desktop app or ChatGPT Work beyond just telling me that I have emails. The whole email thing and the “chief of staff” automations all feel like just a separate place to read my emails, and I don’t find a whole lot of usefulness in them.

Last night I was sitting on the couch and I thought, “What if I could use the shopping list that I have in Apple Reminders as a source for where to get groceries from?” I have HomePods all around my house, so I can easily add something to the list whenever I think about it.

Then I thought, “Well, what if I could have the ChatGPT desktop app look at that list and, from there, go onto the Walmart website and not order the groceries, but add them to my cart and let me know that they’ve been added?” That way, when I actually go to Walmart to order my groceries, those items are already sitting in the cart.

So I added some things to the grocery list. Some were typical stuff like milk, and then some were a little more vague without specifying the brand or anything.

I had the ChatGPT app just honestly try to go for it and see what happened. After a few minutes, I checked the Walmart app and it had gotten the right oat milk brand. I put “water” on the list and it knew to get water bottles instead. We put “Gatorade blue,” and it got the size that we usually get because I guess it just looked at the Walmart history of what we usually order and noticed, “Hey, you got this five times, this is Gatorade and it’s blue, and this is the oat milk you normally get. I’ll just add these ones to the cart."

I thought it was just a pretty cool little automation that saves me a little bit of time every week. It took like two minutes to set up, and now it’s set to run once a day to check that list and see if there’s anything new added.

Permanent free tiers only. No trials, no expiring credits. All OpenAI SDK-compatible, so swapping between them is a base URL change.

Provider	RPM	Daily cap	Models	Trains on your data?
Google AI Studio	10	Per-project (varies)	Gemini 3.5 Flash, 3.1 Flash-Lite	Yes, outside EU/EEA/UK/CH
Groq	30	1,000–14,400	Llama 4 Scout, Kimi K2, Qwen3 + 17 more	No
OpenRouter	20	50 (1,000 with $10 deposit)	29+ free models, changes frequently	Depends on upstream
Cerebras	30	~1M tokens	Llama 4, Qwen3 32B, GPT-OSS-120B	No
NVIDIA NIM	40	No daily cap published	100+ models	No

#1 Google AI Studio

Still the most capable free model available at zero cost. 1M token context window on Flash. Multimodal. The catch that matters: Google no longer publishes universal rate limits. They're assigned per project now, so check your own console rather than trusting any number from a blog post, including mine. And free-tier prompts train their models unless you're in the EU, EEA, UK or Switzerland. Fine for public research. Wrong for client data.

#2 Groq

Fastest free inference available. 300+ tok/s on their LPU hardware. Doesn't train on your data, which makes it the best pick for agent background work that touches anything semi-sensitive. The daily cap varies by model, and the big ones (70B class) run out faster than you'd expect. Best used for heartbeats, classification, crons.

#3 OpenRouter

One key across dozens of free models with automatic failover when one goes down. That flexibility saved people during every pricing shakeup this year. The catch: the free model list changes. Several :free endpoints got delisted recently without warning, so don't hardcode model IDs. Use openrouter/free and let auto-routing handle it. The $10 deposit (your money, stays yours) bumps you from 50 to 1,000 requests/day permanently.

#4 Cerebras

Roughly 1M tokens/day on their wafer-scale hardware, and the speed is absurd at 2,600+ tok/s. One limit that matters: free tier context is capped at 8,192 tokens. For quick agent tasks (classify, extract, summarize) that's plenty. For anything needing long context, it's a wall. Doesn't train on your data.

#5 NVIDIA NIM — the one most people haven't tried

This is the pick I'm surprised doesn't come up more. 100+ open-weight models on NVIDIA's own infrastructure. DeepSeek V3.2, DeepSeek R1, Kimi K2.5, GLM-5.1, MiniMax M2.7, Gemma 4, Qwen 3.5, Nemotron Super 49B, Llama 4. All free. No credit card. 40 RPM with no published daily cap.

Multiple directories are now calling it "the most underrated free AI API platform" and it's hard to argue. The model catalog is wider than any other free tier on this list. OpenAI-compatible endpoint, one key for everything. Zero pricing changes recorded since launch.

The caveats: latency runs 600-1500ms to first token depending on load and region. Under heavy load (US peak hours, new model launches) it slows noticeably. Phone number verification on signup. And it's meant for development, not production, so don't build a customer-facing product on it.

For a personal agent where you want access to 100 models without paying and the RPM matters more than the first-token speed, NIM is the free tier I'd add to the rotation today.

How to stack them

Don't pick one. Use three.

Groq for agent background work. Fast, free, doesn't train. Your heartbeats, classification, and crons live here.

Google AI Studio for anything public where quality matters. Best free model available. Don't feed it client emails.

NVIDIA NIM or OpenRouter as fallback and for model variety. When you want to try something different, or when the primary goes down.

One provider is a single point of failure. Three providers with a fallback chain means no single delisting or rate limit change takes your agent down.

Hello all!

At this point, I am completely overwhelmed by the choices available so I wanted to ask the group: what is your favorite open-source AI and why? Do you use different AI for different reasons? Which one is your "workhorse"?

I wanted something that could search my notes, connect ideas across a few hundred documents, and answer questions about things I wrote months ago. Without a single word going to OpenAI, Google or Anthropic.

Took about 40 minutes. Here's the whole thing.

What you're building

Obsidian holds your notes. Ollama runs Gemma 4 on your machine. Two plugins connect them so you can ask questions and get answers pulled from your own writing. No API keys, no subscription, works on a plane.

Step 1: Ollama and the models

bash

curl -fsSL https://ollama.com/install.sh | sh
ollama pull gemma4:12b
ollama pull nomic-embed-text

The 12B is the one to use now. Released in June, and it beats Gemma 3 27B on every published benchmark while fitting in about 6.6GB at Q4. On 16GB that leaves plenty of headroom for indexing and your browser.

If you're on 8GB, pull gemma4:e4b instead. Slower reasoning, still multimodal, fits comfortably.

The embedding model matters as much as the chat model. nomic-embed-text is fast and good enough for most vaults. Use mxbai-embed-large if retrieval quality matters more than indexing speed.

Step 2: Fix the context window

Ollama defaults Gemma 4 to a 4K window. The model supports far more. This single default is why most local setups feel dumb — your model can't see enough of your notes to answer well.

bash

printf 'FROM gemma4:12b\nPARAMETER num_ctx 32768' > brain.modelfile
ollama create gemma4-brain -f brain.modelfile

32K is a good balance on 16GB. Go higher if you have the memory.

Step 3: The two plugins

Community Plugins in Obsidian, install both:

Smart Connections does vault-wide semantic search and the related-notes sidebar. It builds embeddings locally.

Copilot for Obsidian gives you the chat sidebar. Point it at Ollama's OpenAI-compatible endpoint: http://localhost:11434/v1, model gemma4-brain.

Together these cover most of what people mean by "second brain." Add Text Generator later if you want template-driven generation. Skip the rest.

Step 4: Index and wait

Smart Connections reads every note and builds embeddings. 500 notes takes maybe 10 minutes. 2,000+ can take half an hour. Runs in the background, one time, then incremental.

Pick your embedding model before you index. Changing it later means deleting and rebuilding the whole thing.

Then ask it things

"What did I write about the client meeting last month?"

"Compare what I said about approach A in January to approach B in March."

"Find every note where I mentioned budget concerns."

The moment it clicks is when it surfaces something you wrote six months ago and completely forgot existed. Your memory is human. The retrieval isn't.

What works well

Finding old notes. This is the whole value. Synthesizing across several notes at once, which is painful manually and trivial for the model. Journal recall if you journal in Obsidian ("what was I stressed about in April").

What doesn't

Large vaults. Everything stays responsive around 5,000 notes. Past roughly 20K, Smart Connections re-indexing becomes the bottleneck.

Complex reasoning across many documents. "Analyze the trend across all 50 of my weekly reports" pushes the context window. It gets the gist and misses detail from whatever didn't fit.

Speed. 3-10 seconds per response depending on hardware. Not painful. Not instant either.

Mobile. Chat plugins work on Obsidian Mobile if Ollama is reachable on your LAN, but embedding generation is desktop-only.

The cost

Obsidian free for personal use. Ollama free. Gemma 4 is Apache 2.0. Plugins are community and free. No keys, no subscription. Electricity you were already paying.

Why bother when cloud models are better

Because your journal, your client notes, your financial planning and your half-formed ideas are the most sensitive text you own. This setup means they get read by a model running in your house and nowhere else. There's no privacy policy to trust, because there's no third party involved.

The quality gap versus a frontier model is real. The privacy gap isn't close.

Spent the last while building JobHunterX — upload your resume, it finds jobs

across 4,000+ tracked Indian companies, scores them against your skills,

tailors a resume PDF per job, then drives a real browser to fill and submit

the application. Pauses and hands you control on CAPTCHAs/logins.

Runs entirely on free-tier APIs (Google AI Studio, Groq, Mistral) — $0 to run.

https://preview.redd.it/u7u4wvuk57ih1.png?width=705&format=png&auto=webp&s=177a800934a6c91fa0f135a42bb43455d381fef1

Repo: https://github.com/kvcops/JobHunterX

Would love feedback / contributors.

I’m pretty new to this environment so excuse me for the stupid question

I wanted to know whether there was a reliable way (whatever MCPs, plugins or RAGs) that enables a local llm that fits an m4 pro 18gb (Gemma4 12b maybe) to research on dictionary websites for reliable non hallucinated etymology or Wikipedia or whatever.

Thank you

https://preview.redd.it/wneizgmegkhh1.png?width=1920&format=png&auto=webp&s=9df39c5baf55f9e093ac89399cdab6ea74233c28

Same site, 60 days apart.

I've paid for Ahrefs, Semrush and DataForSEO over the years. Bought keyword lists, chased backlinks, ran audits. Search Console was where I checked clicks and fixed the occasional indexing error. It never occurred to me it could tell me what to do next.

I was using BetterClaw and team shipped a GSC connector. I wired it into my own agent out of curiosity, added DataForSEO on top, and asked it directly: how do I grow traffic here.

https://preview.redd.it/ucjz64a3hkhh1.png?width=1920&format=png&auto=webp&s=6d98466007808dea80191c4c48c900bc058ef018

It came back with something I didn't expect. You already have the impressions. Your problem is CTR and position, not reach.

My first reaction was that CTR can't be that big a lever. I had no better plan, so I went with it.

What it had me do

Rewrite titles and meta descriptions on pages with high impressions and low clicks, matching the exact phrasing people were searching.

Find queries sitting at position 8–20 with no page built for them, and build those pages.

Stop publishing topics with no impression data behind them at all.

No new tools. No outreach. Nothing I couldn't do that afternoon.

The first week I did it all by hand to see whether it was real. CTR moved. So I automated it.

The task prompt

Runs Monday, Wednesday, Friday. This is the actual thing, not a summary of it:

Pull Google Search Console data for the last 7 days and the
7 days before that.

SECTION 1 — CTR fixes
Find pages with 500+ impressions and CTR below 1%.
For each, list the top 5 queries driving impressions.
Compare those queries against the page's current title tag.
If they don't match, write a new title under 60 chars using
the searcher's exact phrasing, plus a meta description under
160 chars.

SECTION 2 — Content gaps
Find queries where we rank position 8-20 and no page on the
site targets that query directly.
For each, give the query, current position, monthly
impressions, and a one-line angle for the page.
Rank by impressions.

SECTION 3 — Movement
Compare this week to last week. List the 5 biggest position
gains and the 5 biggest drops, with the page and query.
For drops of 3+ positions, note whether impressions fell too
or only position moved.

SECTION 4 — Watch list
Any page where impressions rose but clicks stayed flat.
That's a title problem forming, not a ranking problem.

Post to #marketing in Slack. Keep it under 20 lines total.
Lead with the single highest-impact fix.
Only flag each page once per fortnight. Track what you've
already sent.

Two lines in there matter more than the rest. "Only flag each page once per fortnight" stops it repeating itself until the channel gets muted. And "keep it under 20 lines" stops it dumping a report nobody reads on a Wednesday morning.

How it runs now

My junior owns it. Early on I reviewed every line and argued with about half of them. After a few weeks I stopped, because its calls were landing better than my gut. On track for 15K clicks this month, 20K if position holds.

https://preview.redd.it/r7z6yfczhkhh1.png?width=1920&format=png&auto=webp&s=c19df895c978c6cc06a61089edb544fbaab16884

TLDR

Impressions mean the demand already exists. If clicks don't follow, you're losing people at the title, so fix that before writing anything new.

CTR going 0.8% to 1.8% doubles your traffic on identical impressions. Stack that with more pages ranking and 1.37K becomes 10.9K.

A weekly comparison beats a monthly audit. Small fixes compound; big audits sit in a doc.

GSC data is free and it's yours. The paid tools are estimating what Search Console hands you directly.

Please do not underestimate GSC or CTR like I was..

Happy to answer any questions you may have in the thread below; it is always the basics...