Guidance

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

CC 2.1.176 (+4,360 tokens) and 2.1.179 (+5,328 tokens) systmem prompts

REMOVED: System Prompt: Claude in Chrome skill note — Removes the note telling the agent to invoke the claude-in-chrome skill (via the Skill tool) before using any mcpclaude-in-chrome browser tools. Agent Prompt: Coding session title generator — Adds examples to match the session's language (a Korean-session title) and to avoid refusal/error titles or an English title for a non-English session. Data: Claude API reference (all languages) — Adds refusal-fallback guidance for Fable 5, recommending the opt-in server-side fallbacks parameter (beta server-side-fallback-2026-06-01, falling back to Opus) by default so a policy decline is re-served by the fallback model inside the same call; cURL, Python, and TypeScript include runnable examples with switch-point and served-by detection, C# and Go give inline SDK snippets, and Java, PHP, and Ruby point to each SDK's examples/. Notes the parameter is rejected on the Batches API and unavailable on Amazon Bedrock, Vertex AI, and Microsoft Foundry (use the client-side middleware there). Skill: Building LLM-powered applications with Claude — Reframes refusal stop-reason handling to opt into fallbacks by default: new Fable 5 code should include the server-side fallbacks parameter so a refusal doesn't fail the request outright, tell the user it's enabled, and drop it only if they decline, with client-side middleware where server-side fallbacks aren't supported. Skill: Design sync Storybook source shape — Adds a [GRIDOVERFLOW] validation warning and a cardMode: "column" override for stories wider than a grid cell (data tables, full-width bars), plus rebuild rules noting presentation-only keys (cardMode/primaryStory) carry grades via a targeted rebuild while a viewport change re-grades and needs a full build. Skill: /design-sync package source shape — Adds a [GRIDOVERFLOW] validation warning and a cardMode: "column" override for wide components (data tables, full-width bars) that render wider than their grid cell, batching every flagged component into one targeted rebuild. Skill: Model migration guide — Adds "default to opting in" guidance for refusal fallbacks, recommending migrated and new Fable 5 code ship the server-side fallbacks opt-in from day one rather than as a later hardening step. System Prompt: Coordinator mode orchestration — Expands the concurrency guidance: launch independent workers in parallel via multiple tool calls in one message and cover multiple research angles, but don't parallelize simple tasks that are faster in a single worker loop. System Prompt: Fork usage guidelines — Updates the "when to fork" instruction to fork by passing subagenttype: "fork" instead of omitting subagenttype. System Prompt: Forked agent guidance — Explains that calling Agent with subagenttype: "fork" creates a background fork that inherits your full conversation context (rather than omitting the type), and notes that other subagent types — or omitting it — start fresh agents with no context. System Prompt: Subagent delegation examples — Updates the worked examples to pass subagenttype: "fork" when forking and clarifies that a non-fork subagenttype starts a fresh agent. System Prompt: Writing subagent prompts — Reframes the briefing note to say any agent other than a fork starts with zero context (previously "when spawning a fresh agent with a subagenttype"). Tool Description: Agent (simple usage notes) — Notes that a new Agent call starts a fresh agent except subagenttype: "fork", which inherits your context (when forking is available). Tool Description: Agent (usage notes) — Updates the fresh-agent note so a new Agent call starts a fresh agent with no memory of prior runs except subagenttype: "fork", and clarifies that a research-only agent is not aware of the user's intent because it is a fresh agent. Tool Description: Agent (when to launch subagents) — Rewrites the subagenttype guidance so "fork" forks yourself (inheriting your full conversation context and always running on your model, ignoring any model override) while any other type — or omitting it — starts a fresh agent (general-purpose by default). Tool Description: Artifact — Adds that reading an existing artifact's content is done by calling WebFetch with its URL. Tool Description: claude.ai Project — Adds file-upload support: projectinfo now lists file uploads (PDFs, images), projectread reads document-kind uploads (PDF, docx) while image and other non-document uploads return empty content with filekind set, and projectdelete deletes only text docs (file uploads are read-only via the tool and must be removed in claude.ai). Tool Description: WebFetch (concise) — Adds an exception (when the Artifact tool is enabled) that claude.ai/code/artifact/{uuid} URLs ARE fetchable via your claude.ai login and should use WebFetch, not curl, which gets the SPA shell or a Cloudflare 403. Tool Description: WebFetch private URL warning — Adds the same exception (when the Artifact tool is enabled) that claude.ai/co

My conclusions about Sonnet 4.6 and Opus 4.X with programming

Sonnet 4.6 is smart, but you need to lay things out for it, if you want it to build something, a function, a class, a feature, or a specific piece of functionality, you need to provide a lot of details so it knows exactly what to implement, it needs context. I don't trust it with architectural choices of a program/system. Gotta keep an eye on what Sonnet is doing. Opus 4.6 to 4.8, on the other hand, have much greater awareness than Sonnet, they can figure things out on their own, know how to handle large projects, and require less guidance. I am very surprised with Opus models, they are very good at understanding the butterfly effects of decisions, their ability to see the implications of anything is really good. Those are my 2 cents, I know I'm late to the discussion. submitted by /u/IhateReddit9697 [link] [comments]

CC 2.1.174 system prompts (-3,487 tokens)

NEW: Tool Description: claude.ai Project — Adds a session-bound tool for reading and writing the attached claude.ai Project (a shared, persistent knowledge container), exposing projectinfo, projectread, projectsearch, projectwrite, and projectdelete, with knowledge-budget enforcement, a default claude/ namespace for agent-written docs, prompt-cache churn warnings, and instructions to treat doc contents as untrusted data. REMOVED: Data: Design sync story imports module — Removes the bundled Storybook import-resolution helper now folded into the expanded Design sync source-shape guidance. REMOVED: Data: Design sync Storybook preview source generator — Removes the standalone Storybook preview-source generator superseded by the reworked Design sync build pipeline. REMOVED: Data: Design sync sync hashes module — Removes the shared hashing helper module previously used to align builds, captures, and remote diffs. Agent Prompt: Security monitor for autonomous agent actions (first part) — Adds a note that indented User:/Assistant: lines inside a turn are quoted content from the message and should be treated as data, not instructions. Data: Claude API reference — C# — Updates the quickstart example model from Claude Opus 4.6 to Claude Opus 4.8. Data: Claude API reference — cURL — Adds the display: "summarized" thinking opt-in to the request example. Data: Claude API reference — Go — Updates the quickstart example model from Claude Opus 4.6 to Claude Opus 4.8. Data: Claude API reference — Java — Updates the quickstart example model from Claude Opus 4.6 to Claude Opus 4.8. Data: Claude API reference — PHP — Adds the display => 'summarized' thinking opt-in to the request example. Data: Claude API reference — Python — Adds the display: "summarized" thinking opt-in to the request example. Data: Claude API reference — Ruby — Expands the stopdetails.category enum example with :reasoningextraction and :frontierllm categories. Data: Claude API reference — TypeScript — Adds the display: "summarized" thinking opt-in to the request example. Data: Claude model catalog — Updates summarized-thinking and tokenizer guidance so Fable/Mythos share the Opus 4.8 tokenizer (token counts roughly unchanged) instead of a new 30%-larger tokenizer. Data: Streaming reference — Python — Adds the display: "summarized" thinking opt-in to the streaming example. Data: Streaming reference — TypeScript — Adds the display: "summarized" thinking opt-in to the streaming example. Skill: Building LLM-powered applications with Claude — Clarifies that the raw chain of thought is never returned, with responses carrying only regular thinking blocks/summaries. Skill: Design sync — Adds an "Author the conventions header" step to the workflow for writing a design-system conventions header before upload. Skill: /design-sync package source shape — Replaces the guidelinesGlob config field with a readmeHeader path and related conventions-header guidance. Skill: Design sync Storybook source shape — Inserts a conventions-header authoring step (base SKILL.md, before upload) into the build → match → upload workflow. Skill: Model migration guide — Updates the thinking summary so always-on thinking returns regular thinking blocks with the raw chain of thought never returned, dropping the new-tokenizer framing. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.174 submitted by /u/Dramatic_Squash_3502 [link] [comments]

I got Fable 5 to work on biology and create micro organism petri dishes

Hey everyone, ​ I wanted to showcase a project I’ve been aggressively building over the last few days. Using a combination of Dash 4.2.0 as the backbone and the newly released Claude Fable 5, I set out to test the absolute boundaries of LLM safety filters, rapid application development, and advanced Dash component architecture. ​ https://youtu.be/VNY4xFC7Ojk?is=outM5VbO4b9R8Byh ​ What came out of this high-stakes mini "game jam" genuinely shocked me, and I want to share a glimpse of where this tech is heading—along with the open-source components that made it possible. ​ The Challenge: "Bio-Hacking" Fable 5 When Fable 5 dropped, I immediately wanted to see where its guardrails lived. I chose a project concept that deliberately straddled the line of restricted categories: a biology/simulation workspace. My goal was to see if the model would flag or block deep development on a complex, cellular-level iteration of Conway’s Game of Life. ​ Not only did it bypass the expected friction, but it also helped me build out a fully realized digital ecosystem: https://2plot.ai Petri Dish. ​ 🔬 2plot.ai2plot.ai Petri Dish — An AI-vs-AI Evolution Arena Claude and Gemini pilot rival cell colonies in a live, interactive petri dish. You can spectate, co-pilot, or take total manual control—splicing organelles, growing cell families, and trying to out-evolve the rival LLM. You can even store, trade, or land your specimen on the high-score leaderboard. ​ The Tech Stack & Architecture Building a real-time, LLM-driven cellular simulation requires serious frontend performance and UI flexibility. To pull this off in just a few days, I avoided heavy database bloat and leveraged a modular backend combined with a suite of cutting-edge, custom Dash components. ​ Here is the exact stack that made it happen: ​ 2plot.ai Petri Dish — The live, running simulation arena. ​ Dash Documentation Boilerplate v1.0.0 — Markdown-driven docs with pluggable backends (Flask / FastAPI / Quart) and native LLM integration. ​ Dash Pannellum 0.4.0 — Revived specifically for Dash 4.2+ to handle 360° panoramas, virtual tours, and immersive video. ​ Dash MUI Charts — Seamless integration of MUI X Charts, Tree Views, and Time Pickers for Dash (complete with live demos). ​ Deep Customization via Pip Install Python For the core interactive elements, I leaned heavily on the ecosystem we've been building out at Pip Install Python. If you aren't utilizing custom components to break out of standard layout limitations, you're missing out on Dash's true power. ​ Pip Install Python - Custom Dash Components — Interactive documentation for 18+ custom components including dash-gauge, dash-model-viewer, and dash-pannellum. ​ Dash Mantine Components — Crucial for sleek UI layout, offering 100+ highly customizable components built on the React Mantine library with native dark/light mode support and flawless consistency. ​ Moving Forward We are entering an era where application architectures are becoming leaner, smarter, and incredibly dynamic. This project proves that with the right component foundation, you can prototype and launch fully functional, LLM-piloted simulations in a fraction of the time it used to take. ​ I’m happy to offer my guidance, answer questions about the component configurations, or talk through how to handle real-time state updates with Dash 4.2.0 and Fable 5. ​ Let me know what you think, or what you'd try to splice into the petri dish! 2plot.ai submitted by /u/Soolsily [link] [comments]

How i am using Claude in development

I've been building Ethos mostly solo using Claude Code and Codex, and a few things have made a huge difference in keeping AI useful instead of creating chaos. 1. Spend most of the effort on planning. I probably spend 80% of the effort on planning and 20% on execution. Before writing any code, I use the strongest model available to think through the problem, architecture, edge cases, testing strategy, and gaps in requirements. Once the plan is clear, implementation becomes straightforward. 2. Don't let the model review its own work. I usually have Claude implement and Codex review, or the other way around. Different models have different strengths and blind spots. The disagreements between them are often where the most interesting bugs are hiding. 3. Use deterministic checks for anything that must happen. One lesson that really stuck with me: I had "always use git worktrees" written in AGENTS.md, and Claude kept ignoring it. When I asked why, the answer was simple — instructions are guidance, not guarantees. Models can drift. If something absolutely must happen, don't rely on instructions. Enforce it with a hook. I moved the rule into a hook and never had the problem again. 4. Keep tasks and PRs small. Smaller tasks lead to better execution. Smaller PRs are easier to review for both humans and AI. Review quality drops quickly as scope increases. The biggest mindset shift for me has been treating AI like another engineer on the team. Great at reasoning, fast at execution, useful in reviews — but anything that requires a guarantee belongs in automation, not in a prompt. submitted by /u/myth007 [link] [comments]

I built an MCP server + Claude Code skill that hands Claude a token-budgeted context bundle instead of making it grep

Anthropic's own harness guidance steers agents toward grep/glob to find code. That works, but it's token-hungry: grep gets near-perfect recall and basically zero token efficiency because it pulls in everything around the match. archex is a local MCP server that does the retrieval step properly and hands Claude back a finished bundle — ranked, deduped, dependency-closed, with a token budget you set. Then Claude spends its context on reasoning, not on crawling imports. What's wired up for Claude Code specifically: MCP server (stdio, 14 tools): query, scout, analyze, compare, symbol lookup, graph neighbors/path/stats. Optional --watch mode keeps the index fresh as you edit. In-repo skill + /archex command so you can query/scout from inside a session without dropping to a terminal. scout protocol: a two-phase map → fetch flow. Capped at a token budget, it returns a structural map plus exact fetch handles, so the agent grabs only the bodies it actually needs. archex doctor: checks index health, grammar support, model cache, and MCP registration when results look stale. Fully local, deterministic, no API key, Apache 2.0. Head-to-head vs the nearest OSS tool: the agent needed 922 extra tokens to complete a task from an archex bundle vs 11,188 from theirs (~12x). submitted by /u/tom_mathews [link] [comments]

Regretting that commit? Don't panic! Here's how to fix it. Check out our latest GitHub for Beginners episode, answering some of your most commonly asked questions 👇 https://t.co/p87G5DFU0a https:/

Regretting that commit? Don't panic! Here's how to fix it. Check out our latest GitHub for Beginners episode, answering some of your most commonly asked questions 👇 https://t.co/p87G5DFU0a https://t.co/4KpK64eWG2

Aionforge Memory - Long Term Agent Memory

TLDR -> Aionforge Memory is a Rust memory layer for agent systems. It stores episodes, facts, notes, skills, bad patterns, work items, core memory, and audit events in selene-db, then retrieves relevant context with lexical anchors, vector search, graph traversal, recency, importance, and trust signals. Embedded GraphDB with native JSON, Vector and BM25 text search. Aionforge Memory The Details: Selene DB I have been doing a lot of exploration around long horizon tasks and agents mainly in the energy and smart buildings space. One of the needs was a GraphDB capable of living at the edge and on a constrained device whereas most of what I could find on the market was either cloud purpose or used their query language style which was the vendor lock in I wanted to avoid. I was crazy enough to build a graph database, well as a lesson on overreach and confidence it was archived and fully rewritten from the ground up to what is the current form being used here: Selene DB This is using the 2024 ISO GQL spec (wasn't a cheap one to buy either haha) and the natural procedure calls to support the vector, JSON and semantic search features. As far as vectors go I have to give a big shout out to TurboVec as well. TurboQuant compression paper and follow up rust work is foundational for the compression savings in the vector space here. Aionforge Memory The main application here is the memory system. This was built carefully after a lot of research via Arxiv and a lot of dogfooding with my own agents across this and a few other projects. The core of this idea in this project is storing memory but recently I have added work item support as I flesh our more of the multi agent space. This application supports private, team and global namespaces with provenance. I have been very deliberate in red teaming and trying to carefully keep the namespaces clean and isolated which is still a fine tuning in progress. The application supports OAuth as well as standard no login methods. There is also a plugin for most major CLI tools that support skills and trying to guide and/or nudge the agents into storing memories regularly. My own testing with Claude Code and Codex shows they do pretty good with little guidance at catching most everything that is useful. I would definitely appreciate some user UX feedback on the plugins as they have some hooks and I would prefer not to have the system be overbearing or overly opinionated for users! This project is still pretty early on but I would love for some feedback and user stories/issues from the community. The next big push and piece I plan to get out this week is a operator console UI packaged that allows users to start the application with a --ui flag to enable the endpoints for the SPA. Check it out, give me feedback! submitted by /u/HVACcontrolsGuru [link] [comments]

Building a web app with Claude as my co-developer… what am I missing?

Hi all, I’m not a developer, but I’m technically literate. I’ve been a non-technical co-founder on a SaaS product and have designed enterprise technology systems. So I have a reasonable sense of how developers approach building, even if I can’t write the code myself. I’m using Claude to help me build a web app from scratch. It helped me define the tech stack, though I’ll be honest, I don’t know enough to push back meaningfully on those choices. Where I’ve had more genuine input is on user experience and data architecture, both of which Claude helped me think through and refine rather than just dictate. So far we have: • Built a detailed product design document (largely written while travelling, away from a desk) • Defined a sensible iterative build approach • Set up all environments • Designed and created the database schema in Supabase (ten tables, with clear data flows between them) Next step is to populate the tables and build a thin slice of the end-to-end pipeline so I can get it in front of real users. My working principles so far: • No code gets committed unless I understand what it does & I use the light grey inline comments heavily for this • I’ve read and understood the agent guidance documents we wrote together • I’m not trying to become fluent in the language. I’m trying to understand the system Why I’m doing this: partly because I think the app has real potential, partly because it solves a problem I have myself, but mostly because I want to learn how to build things properly. What should I be doing differently? And what should I make sure I don’t skip as the build progresses? Obviously I have asked Claude this, but I thought I should ask you too! submitted by /u/Sad_Bar4397 [link] [comments]

Fable for iterative worldbuilding: song → setting → a 34 page finished solo TTRPG

My processs: (1) ask for Suno prompts "inspired by Amagi-goe but cyberpunk, with worldbuilding depth closer to Iudora/Cadigan than neon tropes" (2) each new song had to be sung from a different position in the society — a widow, an elite, a hacker collective — which forced the world to grow an economy, a festival, a resistance; (3) write a lengthy creative brief inspired by rather obscure indie TTRPGs. Have it build the story chapter by chapter, tweaking my guidance after reading what was written. (4) a continuity/foreshadowing pass, then layout, then I asked marketing copy. At a high level, I feel it is an evocative, interesting, if short (34 pages), journaling game. Obviously, people’s tastes differ. Fable’s output avoided many of the typical hallmarks of AI content. That doesn’t mean I think it is generally competent at creative writing—there are lots of reasons to believe that both long and short form creative writing are among the last things that such models could do. Fable was helped here by the fact that the task required limited mechanics, shallow narrative structure, and was orientated around prompts and a creative brief that focused on estrangement in speculative setting. The illustrations were exactly what you’d expect if you’ve been following the field. I don’t know enough about enka or city pop to judge the music, but other people have told me that it was strikingly expressive. The layout was clear and appealing and organized information in a way that made it easy to follow and use. It is better than I can accomplish, but far from the state of the art. Obviously these are my subjective impressions. If you want to judge for itself, you can download it for free from itch. The games is called Tender. As Fable wrote: You are a tender — a night attendant at a body-inn in the Northern Ward, keeping watch over the warm, sleeping bodies of people who have crossed into the Far Shore and are never coming back for them. Across one year — from the week after one Night of Open Ports to the night of the next — you will move between the cold wards and the warm crown, see what the city is, learn what is being planned for the people in your care, and decide what a person with keys and steady hands owes the world. The title means all three things: the work, the feeling, and the money. submitted by /u/Dramatic15 [link] [comments]

Try it out yourself. Anyone currently on a paid Copilot plan can use the GitHub Copilot app now. 👇 https://t.co/wybcNiydm8

Try it out yourself. Anyone currently on a paid Copilot plan can use the GitHub Copilot app now. 👇 https://t.co/wybcNiydm8

What can you do with the GitHub Copilot app? @kdaigle shares some of his favorite features 🎤 https://t.co/53N0HtoHub

What can you do with the GitHub Copilot app? @kdaigle shares some of his favorite features 🎤 https://t.co/53N0HtoHub

Get your hands on the IRL versions here 👇 https://t.co/dVYcyJVE3b

Get your hands on the IRL versions here 👇 https://t.co/dVYcyJVE3b

claude for medical/healthtech writing in 2026. the rules i learned the hard way.

healthtech founder, series A. 18 months building. weve used claude across product, docs, and a small amount of patient-facing material. wanted to share the rules i wish id known going in, because the stakes here are real and the standard "use AI to write" content doesn't address healthcare specifics. rule 1: claude doesnt know your regulatory context. HIPAA, FDA guidance, your specific clinical context. claude has read a lot of these documents but cant reason about whether your specific use case falls inside or outside. our compliance counsel is non-negotiable for anything touching patient data. rule 2: the "plain language" output is too plain for clinical context, too jargon-y for patient-facing. medical writing has a register that sits between. claude defaults to one of the extremes. we built a custom style for "clinical-informed patient language" with 6 examples and re-tune it monthly. rule 3: never let claude generate dosing, indications, or contraindications text. even when "just drafting." the hallucination rate on dosing-adjacent text is real and the consequences arent worth the time savings. we hand-write these and have claude only edit for clarity. rule 4: every output that touches patients goes through 3 reviews. draft (often claude-assisted) → clinical reviewer (always human) → compliance reviewer (always human). claude can be in the first step. claude cannot be in the second or third. rule 5: documentation matters more in healthcare. log your prompts. weve been logging the prompts behind every piece of patient-facing material since q1. if a regulator asks how we wrote it, we can show. nobodys asked yet, but the act of logging changed my prompts (i write more carefully when i know its logged). rule 6: the founder cant be the last reviewer on clinical content. this is the one i learned the hardest way. early on i was approving content myself. clinical advisor reviewed something i'd missed an obvious red flag on. ive since added two clinical reviewers on rotation and removed myself from the chain except for product/marketing copy. healthtech is one of the few areas where the "AI to move fast" instinct can actively kill the company. interested in how other regulated-industry founders are drawing the line. submitted by /u/Mountain-Half-6032 [link] [comments]

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