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AI & Automation
Prompts, workflows, smart helpers
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AI review
#AI Agents#Multi-Agent#Claude Code#Codex#MCP#Developer Workflow
A premium field guide for evaluating and planning a multi-agent orchestration layer for Claude Code and Codex without blindly installing it.
#ai-writing#editing#prompt-engineering#open-source#content-quality
A lightweight MIT-licensed skill file that helps editors and agent workflows remove common AI-writing tells from prose without running third-party code on production systems. What it does Stop Slop is a Markdown-based writing skill for spotting and removing common AI prose patterns: filler openers, generic emphasis, formulaic contrasts, vague importance claims, passive constructions, and punchline-style endings. The Open-source Projects article frames it as a developer-friendly cleanup tool, but the GitHub repo is the source of truth: it currently ships a SKILL.md file plus reference Markdown, not a packaged Python CLI. Who should use it Use it for AI-assisted blog drafts, docs, release notes, PR descriptions, support replies, and prompt outputs that need a sharper editorial pass. It is especially useful when the draft is factually fine but reads like template-generated AI copy. Setup surface The safest setup is to treat Stop Slop as a checklist or system-prompt fragment. Copy the relevant rules into your editor or agent instructions, then adapt them to your house style. Do not blindly clone and execute anything from a third-party project on a production Raspberry Pi or runner. Practical LinkLoot angle For LinkLoot, Stop Slop works best as a pre-publish quality gate. Blog posts and Loot descriptions can use it to remove filler while keeping source citations, technical terms, pricing caveats, and security warnings intact. The useful version is not an aggressive word killer; it is a final pass that asks whether each sentence says something specific. Risk notes The repo is MIT licensed and mostly Markdown, which keeps runtime risk low. The main editorial risk is overcorrection: some rules, such as removing all adverbs or forcing every sentence into active voice, can damage technical accuracy. Treat the rules as review prompts, not absolute automation. The article's Python-script framing did not match the current GitHub repo, so the repository should be checked before recommending an install path. Source links Open-source Projects article: https://www.opensourceprojects.dev/post/stop-slop GitHub repository: https://github.com/hardikpandya/stop-slop Core skill file: https://raw.githubusercontent.com/hardikpandya/stop-slop/main/SKILL.md MIT license: https://raw.githubusercontent.com/hardikpandya/stop-slop/main/LICENSE
#AI agents#code review#security#Cloudflare#Mythos#audit workflow
A practical defensive guide for checking your own codebase with AI agents: narrow scopes, parallel hunts, adversarial validation, reachability tracing, dedupe, gapfill, and governance gates. Built from the core operational lessons in Cloudflare's Project Glasswing write-up.
#codex#openai#goal#ai agents#prompt workflow#developer productivity
A compact prompt workflow for using OpenAI Codex CLI /goal well: set a short persistent outcome, keep acceptance checks visible, pause or clear goals safely, and avoid stuffing long specs into the command. Use this quick-start pack when a Codex task will span multiple turns, resumes, queued follow-ups, or several files. The point is not to make Codex magically smarter; it gives the agent a persistent target to keep checking against while the work continues. Copy-paste starter Best pattern Keep the goal under one screen: outcome, constraints, validation. Put long requirements in a file, then reference it from the goal. Use the normal prompt for the current step; use /goal for the durable north star. Pause the goal when exploring alternatives; resume it when returning to implementation. Clear the goal after the task is done so it does not steer the next task. When to use it Use /goal for migrations, debugging sessions, release preparation, refactors, long review loops, and tasks where you often say 'continue' or resume the thread later. For one-shot questions, a normal prompt is enough. Evidence notes OpenAI documents /goal as an experimental Codex CLI slash command that sets or views a long-running task goal, with pause, resume, and clear controls. The May 2026 Codex changelog says experimental goals became discoverable, stay paused across resume unless the user opts back in, and gained clearer validation and multi-day duration output. Companion article Read the full evidence-based breakdown here: https://linkloot.io/blog/openai-codex-goal-advantage-long-running-coding-tasks
#web scraping#mcp#python#crawler#ai agents#data extraction#automation
A Python crawler for unstable or JavaScript-heavy sites, with selector healing, structured-data extraction, adaptive rate limiting, and an MCP server for agent-driven crawling. Use only for authorized scraping. Anansi is a Python web scraping toolkit designed for sites that change often or need browser rendering. It combines adaptive parsing, structured-data extraction, incremental crawling, proxy support, and an MCP server so an LLM or agent workflow can drive fetch, extract, crawl, pause, resume, export, and metrics actions. Why it is useful Self-healing selectors: stores selector confidence and attempts fallback strategies when a layout changes. Structured extraction first: pulls JSON-LD, Open Graph, and Microdata before relying on brittle CSS selectors. Browser upgrade path: can switch from HTTP fetching to Playwright rendering for JavaScript-heavy pages. Crawler durability: includes an async crawler, SQLite-backed queue, incremental recrawls, ETag/Last-Modified handling, and resumable jobs. Agent-ready interface: ships with an MCP server so compatible LLM tools can operate crawls through tool calls. Best fit Use Anansi when you need a resilient research or data-extraction crawler for websites you are allowed to access, especially where pages change structure or require JavaScript rendering. It is most relevant for developers building data pipelines, monitoring workflows, competitive research dashboards, or agentic browsing systems. Quick evaluation checklist Confirm the target website permits your intended crawling use case. Start with structured data extraction before custom selectors. Enable browser rendering only where HTTP fetching is insufficient. Keep adaptive rate limiting active and respect Retry-After responses. Use the MCP server when you want an agent to orchestrate crawl tasks instead of manually scripting every step. Source notes The GitHub repository describes Anansi as a self-healing web scraper with selector repair, browser rendering fallback, Chrome-like TLS fingerprinting, Pydantic validation, incremental crawling, and an MCP server. The project is written primarily in Python and is licensed under Apache-2.0.
#GPT-5.5#Prompt Engineering#ChatGPT#AI Workflows#Prompt Templates#AI Writing
A compact English prompt guide for GPT-5.5 built around OpenAI’s new advice: start fresh, stay outcome-first, keep roles short, add clear stop rules, and avoid old step-by-step prompt clutter. GPT-5.5 rewards a different prompting style than older GPT stacks. The short version: start from scratch, define the outcome, keep the role brief, and stop over-explaining the process. Fast rules before you prompt start with the smallest prompt that still preserves the task define the goal, success criteria, constraints, and output shape use ALWAYS / NEVER only for true invariants prefer decision rules over micromanaging every step add stop rules so the model knows when enough work is enough for research or factual work, define a retrieval budget and when to ask for missing evidence Outcome-first general task prompt Use this when you want GPT-5.5 to solve a task without forcing a rigid process. Legacy prompt cleanup prompt Use this when an old prompt feels bloated or overly procedural. Role + personality + collaboration template Use this for customer-facing, coaching, or assistant-style workflows. Research and citation prompt Use this when factual grounding matters more than fluency. Long-task preamble prompt Use this for tool-heavy or multi-step tasks where the user should see quick progress. Drafting prompt with safe placeholders Use this for marketing, documentation, or content drafts when facts may be incomplete. Why this matters GPT-5.5 seems better when you describe the destination instead of scripting the entire route. That is the real upgrade: less prompt theater, more clear intent.
#Claude Code#CLAUDE.md#AI Coding#Prompting#Developer Workflow#Karpathy
A concrete CLAUDE.md example that pushes coding agents toward clearer assumptions, simpler solutions, narrower edits, and better success criteria. Useful for teams that want LLM coding behavior to become more reproducible. Yes — this is Loot-worthy, because the value is unusually concrete. It is not another vague “AI coding tips” thread. It is a single CLAUDE.md file that tries to reduce four very real failure modes in coding agents: silent assumptions, overengineering, broad unrelated edits, and weak success criteria. The proven value The repo’s four principles are tight and practical: Think Before Coding → surface assumptions and ambiguity Simplicity First → cut speculative abstractions Surgical Changes → avoid touching unrelated code Goal-Driven Execution → define success criteria and verify them Why it is getting traction maps directly to pain developers already recognize instantly usable as a CLAUDE.md drop-in lightweight enough to merge with project-specific rules gives a measurable outcome: smaller diffs, fewer rewrites, more clarification before breakage
#AI Business#Founder Workflow#Prompting#Product Strategy#AI Agents#Decision Making
A compact course for founders and creators who want to use AI as a critical tool for market checks, positioning, pricing, and product decisions instead of treating it as a validation machine. A compact course for founders, creators, and operators who want to use AI as leverage without letting it become a false validator. What this course teaches Ask for pain, not praise Stop asking AI for “cool product ideas.” Ask it to surface painful problems, buyer friction, objections, and real-world demand signals. Use AI as a critic, not a cheerleader Your prompts should invite destruction: weak assumptions, bad positioning, fake differentiation, and pricing flaws should be attacked early. Give AI stable business context Do not re-explain yourself every chat. Keep one reusable context pack: audience, offer, positioning, proof, pricing, and constraints. Never ship the first answer The first output is usually a warm-up. Push for sharper, more human, more specific, more commercially useful drafts. Do not hand the wheel to autopilot AI agents can support execution, but you must still own direction, quality control, and business judgment. Best takeaway
#Codex++#iOS Simulator#macOS Dev#AI Coding#Open Source#Developer Workflow
Graphify for Codex++ adds direct iOS Simulator control inside Codex-oriented workflows. It is aimed at developers who want tighter feedback loops when inspecting, testing, and iterating on mobile app behavior. If you use Codex++ on macOS, this tweak is a genuinely useful upgrade: it embeds a mirrored iOS Simulator directly into Codex’s right panel, so you can inspect UI, test interactions, and iterate on app behavior without constantly juggling windows. Why it is good iOS Simulator inside Codex’s side panel taps, swipes, and hardware buttons are forwarded back to the device headless mirrored view instead of a separate Simulator.app workflow built for real tweaking: add features, fix bugs, validate UI changes faster Trade-offs macOS only needs full Xcode, not just Command Line Tools depends on Codex++ first best fit for people already deep in iOS or tweak-heavy workflows
#Graphify#Claude Code#Knowledge Graph#AI Agents#Developer Tools#Open Source
Graphify turns a folder into a queryable knowledge graph so AI coding agents can navigate project context more deliberately. It helps with codebase understanding, dependency discovery, and more grounded agent responses. Graphify is a sharp idea for agent-heavy workflows: point it at a folder and turn code, docs, PDFs, markdown, and images into a navigable knowledge graph instead of forcing the model to reread raw files every time. What you get interactive knowledge graph Obsidian-ready vault wiki-style markdown map plain-English Q&A over the project Why people care The project claims up to 71.5x fewer tokens per query versus reading raw files directly, which is exactly why it caught attention so quickly in the Claude Code crowd. Fast start Good questions to ask What calls this function? What connects these two concepts? What are the most important nodes in this project?
#gpt#prompts#productivity#strategy#decision-making#systems#leverage
A compact prompt bundle with 7 high-value GPT prompts for leverage, bottlenecks, second-order thinking, asymmetric opportunities, execution speed, systems design, and brutally honest strategic feedback. 7 Strategic GPT Prompts to Unlock More Leverage Use this prompt bundle when you want GPT to think more like a strategist, operator, and systems advisor instead of a generic chatbot. These prompts are designed to help you cut noise, find leverage, identify constraints, compress execution, and make better decisions. Replace the placeholders in brackets with your real context. Give GPT concrete goals, constraints, and background. Ask for specific output formats when needed: bullets, tables, prioritization, scorecards, or action plans. For best results, copy one prompt at a time and add your current situation beneath it. Leverage Extraction Engine Find the highest-leverage moves when you feel busy but not effective. Bottleneck Eliminator Use this when progress has stalled and you want the true limiting factor, not surface-level advice. Second-Order Thinking Model Use before committing to important decisions with downstream consequences. Asymmetric Opportunity Scanner Use when you want smarter bets with strong upside potential and controlled risk. Execution Compression Protocol Use when your plan is too bloated, slow, or operationally messy. System Builder (Inputs - Outputs) Use when you want to stop relying on motivation and start building repeatable outcomes. Brutally Honest Advisor Use when you need clarity more than comfort. Pro tip: If you want even stronger output, add this line after any of the prompts: Do not give generic advice. Prioritize specificity, tradeoffs, and concrete next actions. This usually makes GPT sharper, more practical, and less repetitive. These seven prompts work especially well for founders, creators, operators, consultants, and anyone trying to get more results from limited time and attention. They are simple on purpose: short enough to use quickly, strong enough to produce higher-quality thinking.
Blog
Articles in AI & Automation
6/20/20263 min
WorkClaw Launches AI Coworkers for Slack, Teams, and 3,000+ Apps
WorkClaw is positioning AI coworkers as shared team members inside Slack and Microsoft Teams, with cloud-hosted workspaces, customizable skills, admin controls, and a Product Hunt launch push.
6/20/20267 min
Midjourney Medical turns AI profits into a 60-second ultrasound scanner
Midjourney has announced Midjourney Medical, an Ultrasonic CT project that aims to make whole-body scanning as quick as a spa visit. The ambition is real, but diagnostic claims still need clinical evidence and FDA clearance.
6/19/20264 min
Hugging Face Shows How to Benchmark Whether Tools Are Agent-Friendly
Hugging Face published an agent-evaluation harness that tests whether coding agents can use a library efficiently, not only whether they reach the right answer.
6/19/20263 min
OpenAI adds ChatGPT Enterprise spend controls for AI credit usage
OpenAI added ChatGPT Enterprise usage analytics and spend controls so admins can track ChatGPT and Codex credit consumption by user, product, and model.