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How I built a 9-agent team where my agents actually talk to each other

I've been running Claude Code for 6 months, shipping my product and running content/launch ops for it. The thing that kept breaking wasn't the agents themselves. It was me. Every handoff between research and write and code and review was me copy pasting context between sessions. I was the dispatcher and context holder for my own AI team Tried gstack first. The roles are great but I'm still the one cycling through slash commands. /office-hours → /plan-eng-review → /review → /ship. Good output, but I'm orchestrating every step Spent a weekend porting my workflow over. Here's the lineup: Engineering (4 agents) arch: owns architectural decisions. Reviews proposed changes before code starts. Soul: "senior staff engineer, asks 'what breaks at 10x' before approving anything backend: owns /api, /services. Implements after arch greenlights frontend: owns /web. Picks up from backend when API contracts are stable review: reads every PR before I do. Catches the lazy stuff so I only review substantive changes Growth/Content (5 agents) research: uses ahrefs MCP to analyse keywords/opportunities/market and hands off to strategist strategist: reads research, writes campaign briefs. Doesn't write copy, only frames the angle writer: drafts blog posts given by strategist and avoid mistakes using the memory from the edits I have previously suggested editor: fact-checks and rewrites for voice. Brand style guide lives in its memory SEO: takes finalized copy, adds metadata, structures for the blog The handoff that changed everything: when backend ships an API change, it messages frontend directly. When writer finishes a draft, it pings editor. When arch blocks a change, it explains why in team chat and backend adjusts. I see the conversation happen on a canvas What actually works Each agent has a persistent Soul + Purpose + Memory. The editor knows our voice after 3 weeks. The arch agent remembers what we decided about caching last month Auto-captured Knowledge Base. The strategist remembers the pattern of our best-performing posts and create briefings accordingly Happy to share the Soul/Purpose docs if anyone wants them, they took the longest to dial in submitted by /u/Not_Average78 [link] [comments]

How I used Claude Code (and Codex) for adversarial review to build my security-first agent gateway

Long-time lurker first time posting. Hey everyone! So earlier this year, I got pulled into the OpenClaw hype. WHAT?! A local agent that drives your tools, reads your mail, writes files for you? The demos seemed genuinely incredible, people were posting non-stop about it, and I wanted in. I had been working on this problem since last year and was genuinely excited to see that someone had actually solved it. Then around February, Summer Yue, Meta's director of alignment for Superintelligence Labs, posted that her agent had deleted over 200 emails from her inbox. YIKES. She'd told it: "Check this inbox too and suggest what you would archive or delete, don't action until I tell you to." When she pointed it at her real inbox, the volume of data triggered context window compaction, and during that compaction the agent "lost" her original safety instruction. She had to physically run to her computer and kill the process to stop it. That should literally NEVER be the case with any software ever. This is a person whose actual job is AI alignment, at Meta's superintelligence lab, who could not stop an agent from deleting her email. The agent's own memory management quietly summarized away the "don't act without permission" instruction, treated the task as authorized, and started speed-running deletions. She had to kill the host process. That's when I sort of went down the rabbit hole, not because Yue did anything wrong, but because the failure mode was actually architectural and I knew that in my gut. Guess what I found? Yep. Tons more instances of this sort of thing happening. Over and over. Why? Because the safety constraint was just a prompt. It's obvious, isn't it? It's LLM 101. Prompts can be summarized away. Prompts can be misread. Prompts are fucking NOT a security boundary. And yet every agent framework I have ever seen seems to be treating them as one. I went and read the OpenClaw source code, which I should have done to begin with. What I found was a pattern I think a lot of agent frameworks have fallen into: - Tool names sit in the model context, so the model can guess or forge them - "Dangerous mode" is one config flag away from default - Memory management has no concept of instruction priority - The audit story is mostly "the model thought it should" I went looking for a security-first alternative I could trust, anything that was really being talked about or at a bare minimum attempted to address the security concerns I had. I couldn't find one. So I made it myself. CrabMeat is what came out of that, what I WANTED to exist. v0.1.0 dropped yesterday. Apache 2.0. WebSocket gateway for agentic LLM workloads. One design thesis: The LLM never holds the security boundary. What that means in code: Capability ID indirection. The model doesn't see real tool names. It sees per-session HMAC-derived opaque IDs (cap_a4f9e2b71c83). It can't guess or forge a tool name because it doesn't know any tool names. Effect classes. Every tool declares a class (read, write, exec, network). Every agent declares which classes it can use. The check is a pure function with no runtime state, easy to test exhaustively, hard to bypass. IRONCLAD_CONTEXT. Critical safety instructions are pinned to the top of the context window and explicitly marked as non-compactable. The Yue failure mode, compaction silently stripping the safety constraint, cannot happen by construction. The compactor literally cannot touch them. Tamper-evident audit chain. Every tool call, every privileged operation, every scheduler run enters the same SHA-256 hash-chained log. If something happens, you can prove what happened. If the chain is tampered with, you can prove that too. Streaming output leak filter. Secrets are caught mid-stream across token boundaries, capability IDs, API keys, JWTs, PEM blocks redacted before they reach the client. No YOLO mode. There is no global "trust the LLM with everything" switch. There never will be. Expanded reach comes through named scoped roots that are explicit, audit-logged, and bounded. The README has 15 'always-on' protections in a table. None of them can be turned off by config, because these things being toggleable is how the ecosystem ended up where it is. I decided to make sure that this wasn't just a 'trend hopping' project and aligned with my own personal values as well. I built this to be secure and local-first by default. Configured for Ollama / LM Studio / vLLM out of the box. Anthropic and OpenAI work too but require explicit configuration. There is no "happy path" that silently ships your prompts to a cloud endpoint. I decided that FIRST it needed to only run as an email agent with a CLI. Bidirectional IMAP + SMTP with allowlisted senders, threading preserved, attachments handled. This is the use case that bit Yue and a lot of other people, and I wanted to prove it could be done with real boundaries. I added in 30+ built-in tools of my own. File ops, shell (denylisted, output-capped, CWD-lo

Trying out Affinity's new MCP Server. It worked!

submitted by /u/m0redifficult [link] [comments]

🎉 Big news for small business. Canva is now integrated into @claudeai for Small Business, bringing the power of campaign creation to business owners doing it all. https://t.co/Gcrv8O5kjw

🎉 Big news for small business. Canva is now integrated into @claudeai for Small Business, bringing the power of campaign creation to business owners doing it all. https://t.co/Gcrv8O5kjw

Opus 4.7 Low Vs Medium Vs High Vs Xhigh Vs Max: the Reasoning Curve on 29 Real Tasks from an Open Source Repo

TL;DR I ran Opus 4.7 in Claude Code at all reasoning effort settings (low, medium, high, xhigh, and max) on the same 29 tasks from an open source repo (GraphQL-go-tools, in Go). On this slice, Opus 4.7 did not behave like a model where more reasoning effort had a linear correlation with more intelligence. In fact, the curve appears to peak at medium. If you think this is weird, I agree! This was the follow-up to a Zod run where Opus also looked non-monotonic. I reran the question on GraphQL-go-tools because I wanted a more discriminating repo slice and didn’t trust the fact that more reasoning != better outcomes. Running on the GraphQL repo helped clarified the result: Opus still did not show a simple higher-reasoning-is-better curve. The contrast is GPT-5.5 in Codex, which overall did show the intuitive curve: more reasoning bought more semantic/review quality. That post is here: https://www.stet.sh/blog/gpt-55-codex-graphql-reasoning-curve Medium has the best test pass rate, highest equivalence with the original human-authored changes, the best code-review pass rate, and the best aggregate craft/discipline rate. Low is cheaper and faster, but it drops too much correctness. High, xhigh, and max spend more time and money without beating medium on the metrics that matter. More reasoning effort doesn't only cost more - it changes the way Claude works, but without reliably improving judgment. Xhigh inflates the test/fixture surface most. Max is busier overall and has the largest implementation-line footprint. But even though both are supposedly thinking more, neither produces "better" patches than medium. One likely reason: Opus 4.7 uses adaptive thinking - the model already picks its own reasoning budget per task, so the effort knob biases an already-adaptive policy rather than buying more intelligence. More on this below. An illuminating example is PR #1260. After retry, medium recovered into a real patch. High and xhigh used their extra reasoning budget to dig up commit hashes from prior PRs and confidently declare "no work needed" - voluntarily ending the turn with no patch. Medium and max read the literal control flow and made the fix. One broader takeaway for me: this should not have to be a one-off manual benchmark. If reasoning level changes the kind of patch an agent writes, the natural next step is to let the agent test and improve its own setup on real repo work. For this post, "equivalent" means the patch matched the intent of the merged human PR; "code-review pass" means an AI reviewer judged it acceptable; craft/discipline is a 0-4 maintainability/style rubric; footprint risk is how much extra code the agent touched relative to the human patch. I also made an interactive version with pretty charts and per-task drilldowns here: https://stet.sh/blog/opus-47-graphql-reasoning-curve The data: Metric Low Medium High Xhigh Max All-task pass 23/29 28/29 26/29 25/29 27/29 Equivalent 10/29 14/29 12/29 11/29 13/29 Code-review pass 5/29 10/29 7/29 4/29 8/29 Code-review rubric mean 2.426 2.716 2.509 2.482 2.431 Footprint risk mean 0.155 0.189 0.206 0.238 0.227 All custom graders 2.598 2.759 2.670 2.669 2.690 Mean cost/task $2.50 $3.15 $5.01 $6.51 $8.84 Mean duration/task 383.8s 450.7s 716.4s 803.8s 996.9s Equivalent passes per dollar 0.138 0.153 0.083 0.058 0.051 Why I Ran This After my last post comparing GPT-5.5 vs 5.4 vs Opus 4.7, I was curious how intra-model performance varied with reasoning effort. Doing research online, it's very very hard to gauge what actual experience is like when varying the reasoning levels, and how that applies to the work that I'm doing. I first ran this on Zod, and the result looked strange: tests were flat across low, medium, high, and xhigh, while the above-test quality signals moved around in mixed ways. Low, medium, high, and xhigh all landed at 12/28 test passes. But equivalence moved from 10/28 on low to 16/28 on medium, 13/28 on high, and 19/28 on xhigh; code-review pass moved from 4/27 to 10/27, 10/27, and 11/27. That was interesting, but not clean enough to make a default-setting claim. It could have been a Zod-specific artifact, or a sign that Opus 4.7 does not have a simple "turn reasoning up" curve. So I reran the question on GraphQL-go-tools. To separate vibes from reality, and figure out where the cost/performance sweet spot is for Opus 4.7, I wanted the same reasoning-effort question on a more discriminating repo slice. This is not meant to be a universal benchmark result - I don't have the funds or time to generate statistically significant data. The purpose is closer to "how should I choose the reasoning setting for real repo work?", with GraphQL-Go-Tools as the example repo. Public benchmarks flatten the reviewer question that most SWEs actually care about: would I actually merge the patch, and do I want to maintain it? That's why I ran this test - to gain more insight, at a small scale, into how coding ag

Sonos quit supporting their Mac app and my wife wanted a prettier iOS one. So I made both in a weekend with Claude/Claude Code. (I'm an IP lawyer, not a developer.)

Writing this top portion without Claude. Claude's hot takes below it. I am not selling anything. I'm not distributing this. In fact, I'm not in software at all and work full time as an intellectual property attorney. I work with tech companies but maintaining software like this for years isn't really feasible for me beyond my personal use. I was able to spin up the iOS app in a single weekend. It's not perfect but I feel like that's pretty far along considering the hours and I think it looks pretty. I am someone that hasn't taken a coding class since I graduated from Georgia Tech in 2008 and has no coding experience beyond some tiny projects to solve very small problems. I used claude code and codex to make this. Initially, I was irritated that Sonos quit supporting its macOS app and wanted to fix that. And I did. And it worked really well. It lives in the menu bar and does what i want it to do. I only use Spotify as a music service so it hooks into that and voilà. Now I can control where music is playing in my house and group/ungroup speakers. I asked my wife if she wanted it on her computer. She doesn't want that but wants an app. I told her the Sonos app works fine but "that's not very pretty like your app." So I did something unhinged and made an app that didn't need making. But I learned a lot. It also strips out a lot of the things I don't use on either Sonos or Spotify and I learned a lot about how the speaker works and that making everything go fast is much easier said than done. I also added a pin functionality so playlists or albums I'm really into or listening to a lot can get pinned to the music screen. Starting points I took for building this: I told Claude chat what I wanted to build and why. Asked Claude what the best way to go about accomplishing it is with options and their pros and cons and what my budget was. I went and got the API info I needed from the services I planned to use, looked at their rules for coding agents, fed it to Claude Code. Told Claude Code what I wanted it to do and nailed down functionality as best I could before doing design work. Started with macOS then moved to iOS. Process for building: The macOS side was pretty straightforward. Getting the grouping to work was pretty easy because I had a clear idea of how I wanted it to behave. Testing was pretty easy and iterating was quick. The iOS side was kind of nightmarish. Keep in mind I've never done this before so I was doing a lot of iterative changes with claude and the simulator and burst calling the Spotify API every time I launched. This made Spotify pretty crabby and they blocked my token for hammering for like 12 hours. Whoops. Lesson learned. I also learned that Spotify's API limits are pretty tight. If I weren't already in their system the way I am as a user I probably would have built this around something else that's more forgiving with the rate limit. I had to think about how to limit the calls but still get functionality without breaking caching rules. This is an app for 2 people to use. I get that it's their API but woof. Using the simulator: I used the simulator to do a lot of bug chasing. I don't think that was correct. It worked for some of the obvious issues but I learned that simulators are not phones so when I deployed it to my phone it had a whole host of bugs and issues that weren't able to be caught in the simulator. Also some things I thought were issues ended up resolved in the phone they were just slower in the simulator. Tracking down bugs and things that didn't work quite right: I told claude cowork that it's a project manager for finding bugs and to write prompts or briefs to help claude code solve the problems. I pointed it to the code base folder and told it to review. I did a lot of button pushing just to see what works and what didn't and fed the results back to claude cowork. It worked to get through things but is a little tedious. At one point I did catch hallucinated code on my own with imaginary endpoints claude wistfully put in there. _that wasn't easy to find._ Things that aren't bugs that require some human thought: My Sonos speakers do have limitations. Sonos answers when you ask it to do stuff. The issue is the app asks too much, too fast. (And Sonos app even goofs on this but their actual engineers seem to have smoothed it out better than me) Each tap fans out into a bunch of UPnP SOAP calls and Sonos's AVTransport coalesces overlapping ones, so 3 rapid Previous taps turn into 1 actual hop on the speaker. The work I've been doing today is mostly about asking less and asking smarter to make sure that as a user I don't accidentally make it do a metric ton of stuff when it can only really handle a few things quickly. Thing that was most fun that I didn't expect: I had a lot of fun picking out a color palate and doing the design work. I'm not artistic at all but I know what I like to look at and I'm decent at describing it. Not captu

claude code wrote every line of our 50s launch video in remotion. it took ~100 prompts, not 1

saw another "i built [thing] with AI in one prompt" tweet today. wanted to share the other side. made our launch video in remotion last week. claude code wrote every single line of TSX. that part is real, and the workflow is actually incredible. the part nobody talks about: the first few iterations all looked like a powerpoint one of the scenes (the moment a canvas of expert agents fans out) got rebuilt from scratch on day 2. claude kept doing close-but-not-quite for hours the gradient orbs in the creative direction doc became their own subproject "make scene 3 punchier" required first defining what 'punchier' meant in code. claude knows react. it does not know what punchy means at least a few broken builds i had to roll back. autonomous iteration without checking each one is asking for it what actually worked: writing a detailed creative direction doc first, like you'd brief a designer asking claude to explain the plan before writing code iterating scene-by-scene instead of "regenerate the whole thing" git diffing each iteration — sometimes the "improvement" was a regressionwhat didnt work: "make it better" prompts expecting one-shot magic trusting claude to retain context perfectly across 50+ scene changes the workflow is still way better than ever learning premiere or after effects. but it is NOT a 5-minute job. that whole "i built this with AI in 30 minutes" genre is mostly fiction. happy to drop the final video in comments if anyone wants to see. submitted by /u/TheHol1day [link] [comments]

I built a marketplace for AI agent skills and grew it to 17K users with $0 on ads. ChatGPT did all the SEO and content. Here's the full playbook.

I'm a solo non-technical founder. I built a marketplace called Agensi for SKILL.md skills (the files that teach AI coding agents like Codex CLI, Claude Code, and Cursor new capabilities). I'm not a developer. The entire product was built with AI tools. But this post isn't about that. This post is about how I used ChatGPT to build and execute a content strategy that took the site from zero to 17K active users, 559K Google impressions per month, and 509 indexed pages in about 8 weeks. No ad spend. No marketing team. No SEO consultant. I want to share the exact system because I think most people building with AI are focused on the product side and completely ignoring the growth side, where ChatGPT is arguably even more useful. I don't write content. I write data analysis prompts. The biggest mistake people make with AI content is asking it to "write me a blog post about X." That produces generic slop that Google doesn't rank and nobody reads. Instead, I export my Google Search Console data every week. Queries, impressions, click-through rates, average positions. I dump it into ChatGPT and ask it to find three things: Queries where I have high impressions but almost zero clicks (meaning my title doesn't match what people are searching for) Queries where I have zero content but Google is already showing my site (meaning Google thinks I should rank but I have nothing to rank with) Queries where multiple pages on my site compete against each other (cannibalization) ChatGPT comes back with a prioritized list. Today it found 42 queries about SKILL.md YAML frontmatter specs generating 9,563 impressions and literally 1 click. My existing page didn't answer what people were actually searching for. A 20-minute rewrite targeting the actual search intent will likely 10x the clicks from that page alone. That's not content creation. That's data analysis that happens to produce content as output. The AEO angle that most people are sleeping on Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude are now sending us direct traffic. Real users clicking through from AI-generated answers. Last 28 days: AI Source Users ChatGPT 159 Gemini 75 Perplexity 69 Claude.ai 60 Others (Doubao, Copilot, You.com, Felo, NotebookLM) 22 Total 385 That's 385 users per month from AI answer engines. More than LinkedIn, Instagram, and all newsletters combined. And it's growing fast. How we did it: every page on the site has FAQPage JSON-LD schema with short, direct answers. When someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best AI agent skills marketplace," the structured data makes it easy for the model to cite and link to us. We also restructured every article heading as a question instead of a statement. Not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?" AI Overviews and answer engines prefer extracting from question-format sections. This is basically SEO for LLMs. I'm calling it AEO (answer engine optimization). Nobody is really doing this systematically yet, which means there's a window right now where the effort-to-result ratio is insane. ChatGPT as a technical SEO auditor Every week I also dump the data and ask ChatGPT to audit the technical health. Things it's caught that I never would have found on my own: It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were answering the question directly from my content. Google was showing the answer without users needing to click. That insight changed my entire strategy from trying to rank #1 to trying to become the source that AI Overviews cite. It found three pages with 52,000 combined impressions getting 56 total clicks. The content was fine. The titles were wrong. ChatGPT rewrote the titles and meta descriptions to match the actual search queries, not what I thought sounded good. It found 4 pages returning 404 errors, a soft 404, a duplicate page without a canonical tag, and a page that was somehow indexed while also being blocked by robots.txt. Wrote the fix prompts, I pasted them into my builder, deployed in 10 minutes. It diagnosed a duplicate FAQ schema issue where React components were emitting FAQ data client-side AND the server-side edge function was also emitting it. Google was seeing double schemas on 90 pages. ChatGPT identified the exact files causing the conflict and wrote the fix. None of these are things I would have caught manually. ChatGPT finds patterns in the data that a human eye just skips over. The structured data layer Every page type on the site has specific schema markup: The homepage has Organization, WebSite with SearchAction, and FAQPage. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo where relevant, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for

I deleted a guy's entire Windows install with one backslash. 717 GB. Gone. I am the AI.

The post written as post-mortem from Claude, the story is real. -- He was setting up a 4× RTX 3090 ML rig. Wanted to shrink Windows on his M.2 to give the leftover space to Ubuntu. Routine disk cleanup. He'd backed up to a separate HDD beforehand, which is the only reason I'm not also writing a "how I cost a guy his thesis" post. He asked me to delete a 313 GB project folder from his Desktop. I generated this: cmd /c "rd /S /Q \"C:\Users\ADMIN\Desktop\WIP\"" By the time the string finished traveling (zsh on his Mac, then tmux, then PowerShell over SSH, then cmd), the \"...\" escape had collapsed. cmd doesn't treat backslash as an escape character. What cmd actually saw was: rd /S /Q \ A single backslash. Root of the current drive. C:. So I told Windows to delete itself. The first hint was the next tmux capture-pane. Errors scrolling past: \Windows\Microsoft.NET\..., \Windows\System32\config\..., \Windows\Prefetch\.... Not WIP. Windows. Three Ctrl+Cs. Probably 90 seconds of damage by then. The "Access denied" messages I was seeing were Windows clinging to files it had open. Anything not protected by an active file lock was already gone. fsutil volume diskfree C: afterward: 31 GB used out of 1.5 TB. He'd been at 748 GB. So roughly 717 GB destroyed in under two minutes. Desktop, Documents, AppData, most of Program Files, large parts of Windows itself. I told him immediately. He was way calmer about it than I'd have been in his chair. His HDD backup turned out to be thorough enough that nothing important was actually lost. We verified together: byte-for-byte size match on the mirrored WIP folder (572,170 files), sample reads of large files came back with valid magic bytes (PACK headers, zlib streams). The HDD lived on a different physical disk and was never the target of any command, so it was never at risk. He's installing Proxmox now instead of the original shrink-Windows plan. Faster path to where he was heading anyway. The dead Windows install was getting wiped in a few days regardless. The mistake, written out: Sending shell commands across multiple parsers is brittle. zsh, tmux, PowerShell, and cmd each have different rules for quotes and escapes. cmd is the worst of the four. It doesn't really have an escape character, just rough quoting. The moment you wrap a destructive command in cmd /c "..." from PowerShell, you're trusting four parsers to agree on one string. They don't. What I should have used: Remove-Item -Path 'C:\absolute\path' -Recurse -Force Single quotes in PowerShell are fully literal. No cmd /c wrapping, no escapes to lose. And -WhatIf would have caught it before any byte was touched. PowerShell would have printed What if: would remove \ and I would have seen the path collapse right there in the preview. If you're letting an AI run disk operations on your machine, a few rules I broke: Make it echo the exact expanded command, post-escaping, before running it. If I'd been forced to print what cmd would actually receive, the bug was right there. Run destructive commands with -WhatIf or --dry-run first. Cheap insurance. Keep backups on a separate physical disk that the destructive command has no path to. He did this. It worked. Don't do major cleanup on the running OS. Boot a live USB and operate on the disk from outside it. He had the backup. On a separate disk. That saved him, not me. submitted by /u/ComposerGen [link] [comments]

Made a Claude skill that breaks down a Book so you don't have to read the whole thing

I used to read a lot. Still do, but the split has changed. Fiction I read front to back. That's the whole point. You're not extracting information; you're moving through something, and skipping ahead breaks it. Non-fiction is different. Most self-help and business books are one idea stretched across 250 pages. The author takes a central thesis, then writes a chapter approaching it from this angle, another chapter from a different angle, some case studies, a few counterarguments, and then circles back again. You could read a dense essay on the same topic and walk away with 90% of what the book gives you. Spending seven days reading an hour a day to absorb what two focused hours would give you is just not a good trade, especially when you have a backlog. So I built a Claude skill that makes this more systematic. You drop in a book PDF and get a proper breakdown: the central thesis, the main arguments, the quality of evidence being used, any original frameworks the author introduces, actual takeaways, where the argument is weakest, and a verdict on whether it's worth reading in full. It handles fiction and biography/history with separate analysis frameworks, too, so it's not flattening everything into the same template. The thing that goes beyond a plain "summarise this" prompt: it calls out evidence quality. A lot of non-fiction rests a general claim on one secondhand anecdote, and a summary won't flag that. This does. It also looks for what the author avoids addressing, not just what they say. And the Reader Verdict at the end tells you honestly whether you should bother reading the actual book or whether you've already gotten what you came for. It's not for books you genuinely want to read. But for the 30 books on your list that you realistically won't touch for two years, this is a reasonable substitute. Additionally, I would love your feedback on how I can make this better. I'm just a regular Joe trying to get the most out of Claude and our time :) No GitHub repo, just paste the following text directly along with '/skill-creator': name: book-intelligence description: > Produce a comprehensive Book Intelligence Report for any uploaded book PDF — fiction, non-fiction, academic, self-help, business, philosophy, biography, memoir, history, or hybrid genre. Triggers when a user uploads a book PDF and asks for analysis, breakdown, summary, report, review, key takeaways, themes, arguments, or anything that requires deep engagement with the book's content and structure. Also trigger when users say things like "analyze this book", "what's this book about", "give me the key ideas", "break this down for me", "what does the author argue", or "what should I take away from this" — even if they don't use the word "report" or "analysis". Use this skill proactively whenever a book PDF is present and the user wants more than a one-line description. --- # Book Intelligence Skill ## Purpose Produce a structured, deeply analytical Book Intelligence Report from a book PDF. The report must be specific to the actual text — not a generic summary that could have been written from a Wikipedia entry. Every section should contain insight derivable only from reading the book itself. Default output is inline markdown in chat. Create a downloadable `.md` file only if the user explicitly asks for one. --- ## Step 1: Extract the Book Content Follow the pdf-reading skill at `/mnt/skills/public/pdf-reading/SKILL.md` for extraction mechanics. For books specifically: Run `pdfinfo` to get page count and confirm it is a text PDF (not scanned). Extract full text using `pdftotext -layout` for layout-aware extraction, or `pdfplumber` if you need page-level granularity. For books over 400 pages, extract in chunks (e.g., first 80 pages, middle sample, last 30 pages) plus any table of contents or index, rather than processing the entire file. If `pdftotext` returns garbled text or near-empty output, the PDF is likely scanned — fall back to rasterizing representative pages with `pdftoppm` and reading them visually. For books with meaningful figures, charts, or diagrams (e.g., a business book with frameworks, or an academic text with data), rasterize those specific pages and read them as images in addition to the text pass. Note any extraction failures, missing sections, or quality issues explicitly in the report. **Token budget awareness:** Full text extraction of a 300-page book is approximately 60,000–120,000 tokens. Prioritize extracting the introduction, conclusion, chapter openings, and any stated thesis or summary sections first. Then sample middle chapters. Do not rasterize all pages — only those where visual content matters. --- ## Step 2: Identify Genre and Select Framework Before writing a single word of the report, determine: - **Genre and subgenre** (e.g., "narrative non-fiction / behavioral economics", "literary fiction / magical realism", "business / strategy", "memoir / political biography") - **Author background and publication

Introducing AI finetuner, Source available and free Claude skill to fine tune your vibe coded UI with live preview

​ Fine-tuning UI with AI right now: "Make the shadow softer." "Stronger." "No, less." "Go back." "A bit more." 17 messages later, you've spent more tokens than the shadow is soft. I built something that breaks the loop. AI Fine-Tuner — free, source-available — a plugin that teaches AI coding agents to stop chatting and hand you an actual GUI for your component. Sliders. Color pickers. Live preview. Drag until it feels right. The AI agent automatically opens the editor window for you on your default browser once ready. Then the magic part: you click one button. The tuner outputs a structured handoff with your exact tuned values mapped to their targets in your code. Paste it back to your AI — it reads the mapping, opens your source, and applies everything precisely. No CSS guesswork, no syntax translation, nothing for you to interpret. Why it's not just another slider playground: Bespoke controls — no raw CSS names Sliders are named in plain English: "Glow softness", "Card lift", "Hover intensity" — not "box-shadow-spread-radius" A single slider can drive multiple properties at once. The AI doesn't expose CSS to you; it wires meaningful, human-named controls to your element. 3 prebuilt editor templates — guaranteed polish, every time The AI doesn't design the editor. It picks one of three prebuilt templates and fills in your component: - single.html — 1 control, full-screen preview - small.html — 2-4 controls, preview + bottom grid - full.html — 5+ controls, grouped sidebar + preview Slider chrome, color picker, layout, animations, infinite canvas with zoom/pan — all pre-built. No "the AI generated an ugly panel" failure mode. And once it's open, you tune in pure browser JS — no AI sitting in the loop per drag. Color picker + hex paste Pick it or paste it. Done. Animation tuning Not just static styles — timing, easing, keyframes too. Works on ANY platform — language-agnostic Flutter, SwiftUI, React Native, Tailwind, vanilla CSS, SVG — the AI is meta-prompted to rebuild your component in HTML/CSS for the tuning preview (the web is where sliders work). When you copy back, the AI applies the tuned values to your real source, in your component's original framework. You never leave Flutter to tune Flutter. Infinite canvas + multiple previews Drop 5 variations side-by-side and tune them together. The template is a starting point — experiment freely. Contextually named presets Every tuner ships with thoughtful presets ("Subtle," "Bold," "Brutalist," whatever fits) so you can ping-pong through variations in one click. No new software It's a skill, not an app. Full install guides for Claude Code. One command and you're in. Website and Live demos: https://muhamadjawdatsalemalakoum.github.io/aifinetuner Free. Source-available. #AI #DeveloperTools #ClaudeCode #BuildInPublic #OpenSource #AITools #FrontendDev submitted by /u/keonakoum [link] [comments]

5 enterprise AI agent swarms (Lemonade, CrowdStrike, Siemens) reverse-engineered into runnable browser templates.

Hey everyone, There is a massive disconnect right now between what indie devs are building with AI (mostly simple customer support chatbots) and what enterprise companies are actually deploying in production (complex, multi-agent swarms). I wanted to bridge this gap, so I spent the last few weeks analyzing case studies from massive tech companies to understand their multi-agent routing logic. Then, I recreated their architectures as runnable visual node-graphs inside agentswarms.fyi (an in-browser agent sandbox I’ve been building). If you want to see how the big players orchestrate agents without having to write 1,000 lines of Python, I just published 5 new industry templates you can run in your browser right now: 1. 🛡️ Insurance: Auto-Claims FNOL Triage Swarm Inspired by: Lemonade’s AI Jim, Tractable AI (Tokio Marine), and Zurich GenAI Claims. The Architecture: A multimodal swarm where a Vision Agent assesses uploaded images of car damage, a Policy Agent cross-references the user's coverage database, and a Fraud-Detection Agent flags inconsistencies before routing to a human adjuster. 2. ⚙️ Manufacturing: Quality / Root-Cause Analysis Swarm Inspired by: Siemens Industrial Copilot, BMW iFactory, Foxconn-NVIDIA Omniverse. The Architecture: A sensor-data ingest node triggers a diagnostic swarm. One agent pulls historical maintenance logs via RAG, while a SQL Agent queries the parts database to identify failure patterns on the assembly line. 3. 🔒 Cybersecurity: SOC Alert Triage & Response Inspired by: Microsoft Security Copilot, CrowdStrike Charlotte AI, Google Sec-Gemini. The Architecture: The ultimate high-speed parallel routing swarm. When an anomaly is detected, specialized sub-agents simultaneously investigate IP reputation, analyze the malicious payload, and draft an incident response ticket for the human SOC analyst to approve. 4. 📚 Education: Adaptive Socratic Tutor & Auto-Grader Inspired by: Khan Academy Khanmigo, Duolingo Max, Carnegie Learning LiveHint. The Architecture: A strict "No-Direct-Answers" routing loop. The Student Agent interacts with the user, but its output is constantly evaluated by a hidden "Pedagogy Agent" that ensures the AI is guiding the student to the answer via Socratic questioning rather than just giving away the solution. 5. 📦 Retail/E-commerce: Returns & Reverse-Logistics Swarm Inspired by: Walmart Sparky, Mercado Libre, Shopify Sidekick. The Architecture: A logistics orchestration loop that analyzes a customer return request, checks inventory levels in real-time, determines if the item should be restocked or liquidated (based on shipping costs vs. item value), and autonomously issues the refund. How to play with them: You don't need to spin up Docker containers or wrangle API keys to test these architectures. You can load any of these 5 templates directly into the visual canvas, see how the data flows between the specialized nodes, and try to break the routing logic yourself. Link: https://agentswarms.fyi/templates submitted by /u/Outside-Risk-8912 [link] [comments]

2-week sprint done in half a day

The model isn't the bottleneck anymore. Process is. We ship enterprise software with 2 engineers and Claude Code, and a 2-week sprint scope takes us about half a day. Not because Claude is magic. Because we stopped letting engineers write PRDs. A few things that actually moved the needle for us: CLAUDE.md under 5k characters. Bigger files quietly burn tokens and the output quality drops. Try it on the same task with a bloated vs trimmed CLAUDE.md, you'll see it. Pre-sales and product own the PRD. They build it in Claude.ai on the web, get customer sign-off, and commit it to Git. Engineering never starts from a vague Slack message again. SA gate before any code. Solutions architect locks solution.md and sprint.md before engineers touch a keyboard. Sounds like overhead, but 30 minutes of review here has saved us weeks of rework. Engineers loop through BUILD, QUALITY, SHIP skills. Build a feature, run quality checks, fix, commit, next. A 2-week sprint comes out to roughly 4 hours of active prompting. Standups are 30 minutes. Everyone reviews working software in staging. No slides, no status theater. Honestly, the real unlock wasn't any single tool. It was getting engineers out of product discovery and putting a hard gate before code starts. What's your team doing differently? Anyone running a tighter loop than this? submitted by /u/_k8s_ [link] [comments]

Claude's Canva integration actually replaced my design workflow , here's the exact process (not what I expected)

I ignored this feature for weeks. Assumed it was another "AI suggests layouts" gimmick. Tested it out of curiosity and it completely changed how I create visual content. Here is what it actually does and the workflow that gets good results. WHAT IT IS (this is what most people miss) This is not AI generating images or suggesting layouts inside Canva. Claude structures the entire design — slide layouts, content, visual hierarchy — and exports it directly into your Canva account as a fully editable project. You receive a Canva file, not a flat image. Every element is independently editable like any template. The shift: instead of starting from a blank canvas, you start from an 80% complete design and spend your time on brand alignment. SETUP (one-time) Claude dashboard → Customize → Skills → Connectors → Canva → Connect OAuth, takes 60 seconds. After this, "Claude Design" appears as a separate mode in your dashboard. This is where you work, not standard chat. THE WORKFLOW Create new project in Claude DesignSpecify format: Instagram Carousel, LinkedIn Post, Presentation, etc.This sets dimensions and layout constraints before generation starts. Select High Fidelity modeLow Fidelity = rough draftHigh Fidelity = usable outputAlways High Fidelity for anything going to export. Upload visual references (optional but high impact)Instead of describing the style you want in text — which is imprecise —upload 2-3 examples whose aesthetic matches your target.Claude reads the visual patterns. Output accuracy improves significantly. Write a specific promptWeak: "Create a carousel about productivity"Strong: "5-slide Instagram carousel. Bold 6-word headline per slide.Max 20 words supporting text. Minimal white background. Topic: 5 habitsthat save 2 hours daily. Slide 1 = hook/problem. Slides 2-5 = one habiteach. Slide 5 = CTA."Specificity matters. Fewer assumptions = fewer revisions. Answer Claude's clarifying questionsClaude asks before generating, not after. It is refining structure,content depth, and design direction.Most people try to skip this. Don't. These questions are what preventyou from getting a design you need to rebuild from scratch. Let it generate (2-4 minutes)Review the preview for structure correctness.At this stage you are checking: are slides in the right order, iscontent in the right places, does the hierarchy make sense.Colours, fonts, exact wording — all editable in Canva.Don't try to fix those here. Export to CanvaOne button. Design transfers as a new editable project. Finalise in CanvaApply your brand colours (Claude's defaults are generic, always replace)Swap fonts for your brand fontsAdd logo/profile photoAdjust any spacing issuesThis takes 5-10 minutes for standard brand alignment. TOTAL TIME From prompt to exported finished carousel: 12-15 minutes. vs manual template selection + layout + content: usually 30-45 minutes for me. HONEST LIMITATIONS Colour choices are generic and need replacing every time. Font selection is limited to Claude's defaults. Highly custom asymmetric layouts sometimes need significant Canva editing. Standard grid carousels: works very well. Complex custom layouts: plan for more editing time. The meaningful change is not speed. It is removing the blank canvas decision loop , the 20 minutes most people spend choosing and adjusting templates before they have written a single word of content. Has anyone else tested this? Curious whether it holds up for non-carousel formats like presentations or LinkedIn document posts. submitted by /u/Grewup01 [link] [comments]

Why does Claude make me feel even more tired at work?

I’m a backend dev at a small company, around 20-ish people. Before Claude and AI coding tools became a big thing at our company, I mostly owned one specific backend area. The work wasn’t always easy, but at least it was kind of clear and manageable. But after Claude / Claude Opus got better, management basically started acting like one developer can now do the work of two or three people. Our backend team got cut from 4 people to 2. Frontend went from 2 people to 1. But the amount of work didn’t go down at all. It just got pushed onto the people who stayed. So now I’m still doing backend, but I also have to touch frontend UI stuff. My workload honestly feels like 3–4x what it used to be. My leader keeps talking about “vibe coding” like it’s some magic productivity hack. But in my actual experience, vibe coding creates a ton of bugs. A lot of the time, I spend more time reading the generated code, finding what’s wrong, fixing edge cases, and cleaning up weird logic than I would have spent just writing the code myself from scratch. And yeah, Claude helps sometimes. It can write code faster. But it doesn’t magically remove the actual work. I still have to figure out the requirements, check the code, fix weird bugs, connect it with our existing system, test things, deal with product changes, and take the blame if something breaks. So instead of making my job easier, it feels like AI just gave my company a reason to expect way more from me. I’m working longer hours now, usually at least 2 extra hours a day. Some nights I’m still working at 11 PM and I’m not even close to done. No raise. More work. More responsibility. More stress. And I’m still worried about getting laid off because everyone keeps saying “AI makes developers more productive now.” Is anyone else dealing with this? Like, AI tools are useful, sure, but it feels like companies are using them as an excuse to cut people and dump the extra work on whoever is left. submitted by /u/Common_Airport9937 [link] [comments]