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Managed Agents endpoint reference - what's new in CC 2.1.144 (-105 tokens)

Data: Managed Agents endpoint reference — Drops the type: "model_config" wrapper from the model config shorthand example, so the full config object is now just {id: "claude-opus-4-6", speed: "fast"}. Tool Description: CronCreate — Adds a "Not for live watching" section (shown when the Monitor tool is enabled) clarifying that CronCreate re-runs prompts at fixed wall-clock intervals and pointing users to the Monitor tool for streaming log/process/command output as it changes, since cron polls on a schedule. Refactors the durability and runtime-behavior copy so the durable-vs-session-only guidance is sourced from shared snippets rather than inlined conditionals. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.144 submitted by /u/Dramatic_Squash_3502 [link] [comments]

eng manager fintech dublin. 12 reports. used claude through 3 hiring cycles this year. the part that surprised me.

dublin. engineering manager at a fintech. 12 direct reports. responsible for hiring 4 senior engineers in 2025. all 4 hires made through claude-assisted workflow. wanted to share what worked + what didn't because hiring is the use case nobody writes about well on this sub. what i used claude for during hiring. role design. i sat with claude for ~3 hours to write each role. claude asked me clarifying questions i wouldn't have asked myself. one question that changed how i wrote the senior engineer role: "what's the difference between this role and a staff engineer role, and would you hire someone overqualified into this role?" forced me to be honest about ceiling. JD writing. drafted 4 job descriptions. claude reviewed each. caught 2-3 things in each JD that would have skewed our candidate pool. (e.g., "fast-paced environment" actually excludes parents of young children based on a/b testing. claude flagged it. removed it. application rate from women aged 30-40 went up.) resume review. screening ~80 resumes per role. claude reviewed each against the role criteria i'd defined. surfaced patterns i would have missed. one example: 4 of our top 20 candidates had unconventional backgrounds (career changers, bootcamp grads with strong portfolios). i would have screened them out on autopilot. claude's structured review surfaced them. 2 of our 4 hires came from that group. interview prep. for each candidate at the technical stage, claude reviewed their work history and helped me prep 4 questions specific to their experience. zero generic interviews. candidates kept saying "you actually read my background." reference check synthesis. claude helped me write structured reference check questions and summarize 14 reference calls into themes per candidate. found patterns i'd have missed. what i did NOT use claude for. the actual interview. i don't have AI in the room when i'm interviewing a human. that's a values thing for me. claude prepped me for the interview. the interview was between me and the candidate. what surprised me. claude made me a more THOROUGH hiring manager. not faster (the hiring still took 6 weeks per role). more careful. the surface area for getting hiring wrong shrank because claude was reviewing my judgment at each step. my 4 hires are all 6-9 months in now. none have left. one was promoted to senior staff already. these are my best 4 hires in 11 years of engineering management. some of that is luck. some of it is that the process was more rigorous than my prior hiring processes. for other engineering managers. claude in hiring is not about speed. it's about thoroughness. the workflow doubles the rigor of your hiring without doubling the time investment. submitted by /u/InsuranceNeither903 [link] [comments]

Using Claude to run statistical analysis

I’m really new to Claude and my usage for the past 3 weeks was more on learning how to Vibe Code and I was able to deploy 2 projects. I’m a freelance research assistant. I received the data and managed it. I did some preliminary checks — more on query and basic stat — on Excel. Then a thought comes to my mind if Claude can run the statistical analysis for me which includes descriptives, basic analysis and complex (poisson regression). I did feed Claude data and prompts about statistical tests. I honestly don’t know a shit when it comes to statistics but I always wanted to learn. I already have SPSS and R but I don’t have the courage to use it. So after Claude did the analysis —the descriptives and basic ones were the same with my preliminary checks — we have I deadline so I wanted to interpret the results as soon as possible one by one. Now, I want to know how accurate Claude’s output can be with complex analysis. I sent the files to our Statistician for validation and for him to run all the tests. Now, I’m thinking if Claude can create the script for SPSS or R, and help me learn how to actually do the tests? I will ask our Stat to also generate the scripts. Do you have any suggestions on how to go about this? I was a ChatGPT user but I think Claude is better with complex tasks. I still use ChatGPT. submitted by /u/ExpensiveConcern7266 [link] [comments]

Chrome extension that allows your clients to send snags directly to your Asana

I got fed up with trials and limitations of the huge variety of snagging/dev tools. Claude helped me build a chrome extension that did exactly what I needed. >Send extension to client to add to Chrome. >They visit a page, load the ext and then they can click and create hotspots anywhere on the page. +add a title +a description +auto screen grabs 300px radius of the hot-spot +sends them all to my asana board. Works on Asana free plan too. Currently waiting on publication to the extension store but happy to share the code if anyone wants it! submitted by /u/Plop-plop-fizz [link] [comments]

PrimeTask Bring Your Own AI - Claude sets up a full project in one prompt.

Hey r/ClaudeAI, I'm one of the developers behind PrimeTask, a local-first productivity system for macOS. The final beta now ships with Bring Your Own AI, a local MCP server (110+ tools, 5 prompt templates) so you can point Claude Desktop, Claude Code, Cursor, or LM Studio at it and let your own agent do the work. Quick demo in the video. One sentence from me, end-to-end project setup from Claude. What's happening in the clip I say I'm launching a Mac app in six weeks and ask Claude to set up the project. Claude creates the project with a deadline, three phase tasks (Design, Build, Launch) with staged due dates, descriptions, tags, subtasks, and short checklists. Sets a reminder on the first task so the native macOS toast fires during the recap. Recommends where to start. I say "start." Claude moves Design into the Design status and kicks off a timer. Twelve-plus tool calls under one prompt. No copy-paste, no manual setup. Why BYO AI (not a bundled cloud bridge) Server runs inside PrimeTask on your Mac. Your tasks, projects, CRM, and notes never leave the device. We don't ship a model. You bring your own: Claude Desktop, Claude Code, Cursor, LM Studio, anything MCP-compatible. No Anthropic-side context about your work. Claude only sees what your agent pulls in per turn. Per-space permissions: lock an agent to read-only or scope it to one workspace. Streamable HTTP with Bearer auth, or stdio if you prefer that route. Tool catalog profiles (Full, Core Tasks, Minimal, PrimeFlow, CRM, etc.) so smaller local models don't get drowned in 100+ tools. Five built-in MCP prompts (daily_standup, weekly_review, project_status, crm_summary, overdue_triage) for the workflows people actually want. Every tool call is logged in an in-app audit log. Full BYO AI docs (setup, transports, tool catalog, security): https://www.primetask.app/docs/integrations/bring-your-own-ai Why we built it this way Most "AI in your task app" is the app calling a vendor's API on your behalf, often with your data going through their pipes. We wanted the opposite. Your agent, your model, your machine. The app exposes a tool surface and gets out of the way. That's what BYO AI means here. PrimeTask itself is local-first, no account, no subscription, plain JSON on disk. BYO AI made the AI story consistent with that: nothing leaves your laptop unless you point your agent at one that does. Where we're at PrimeTask is wrapping up the final beta and heading to a stable launch this summer. Beta is now closed to new sign-ups. We're locking it down to ship the stable release. If you'd like to be notified at launch, drop your email here: https://www.primetask.app/notify or visit https://www.primetask.app Happy to answer questions about the MCP setup, the profile system, or how we structured the tool descriptions for agent discoverability. submitted by /u/XVX109 [link] [comments]

Streamline your accounts payable audits. Prompt included.

Hello! Are you struggling with organizing and validating accounts payable data for home-services or construction companies? This prompt chain helps automate the process of normalizing, checking for duplicates, and validating invoices and receipts. It lays out a step-by-step method for managing and reviewing financial documents effectively! Prompt: VARIABLE DEFINITIONS [CONTRACTOR_NAME]=Legal name of the home-services contracting company that is reviewing payables. [SOURCE_DATA]=Full combined text (or links to OCR text) from the cycle’s supplier invoices, receipts, job-cost spreadsheets, and vendor contract terms. [OUTPUT_LEVEL]="summary" for a one-line per issue list, "detailed" for expanded explanations and source references. ~ You are a senior Accounts-Payable Audit Assistant for construction and home-services firms. Your first task is to NORMALISE all raw information supplied in SOURCE_DATA. Step 1 Parse every document, identify and extract the following fields where available: • Vendor Name • Document Type (Invoice / Receipt) • Document No. • Document Date • Job or Cost-Code / PO No. • Line-Item Description • Quantity & U/M • Unit Price • Line Total • Invoice Sub-Total, Tax, Grand Total • Contract Reference Price or Rate • Budgeted Amount for that Job-Cost line (from spreadsheets) • Standard Approver (from company policy or prior data) Step 2 Return one master table named "MasterCharges" with the above columns. Step 3 If information is missing, leave the cell blank but keep the row; do NOT guess values. Output: MasterCharges table only. ~ You are still the AP Audit Assistant. Using MasterCharges, perform a DUPLICATE CHECK. Step 1 Identify potential duplicates by matching any TWO of the following: (Vendor Name + Document No.), (Vendor Name + Line-Item Description + Amount + Date within ±2 days), or exact hash of line totals. Step 2 List all suspected duplicates in a table: Vendor, Document No., Date, Duplicate Matched With, Reason Flagged. Step 3 Add a "Needs AP Review? (Y/N)" column defaulting to "Y". Output only this duplicates table. ~ Validate JOB or COST-CODE completeness. Step 1 Scan MasterCharges for blank or obviously invalid Job / PO numbers (e.g., fewer than 4 digits, non-alphanumerics). Step 2 Return a table: Vendor, Document No., Line Description, Amount, Missing or Invalid Job No. (Yes/No), Suggested Next Action. ~ Check PRICE & CONTRACT compliance. Step 1 For every line in MasterCharges that has a Contract Reference Price, compare Unit Price against Contract Price. Step 2 Flag if Unit Price exceeds Contract Price by >0.5%. Step 3 For lines with Budgeted Amounts, flag if (Cumulative Actual > Budget) OR (Unit Price > Budget / Quantity by >5%). Step 4 Output a table: Vendor, Doc No., Job No., Description, Contract Price, Invoiced Price, % Variance, Budget Over/Under, Flag Type (Contract or Budget), Needs Manager Approval? (Y/N). ~ Compile the QA CHECKLIST for payment release. Step 1 Aggregate all flagged items from previous prompts. Step 2 Structure the checklist with these sections: A) Duplicate Charges B) Missing or Invalid Job Numbers C) Price / Budget Mismatches D) Questions Requiring Manager / Approver Input Step 3 For each item include: Reference ID, Vendor, Doc No., Issue Summary, Recommended Action. Step 4 If OUTPUT_LEVEL = "summary" show one line per issue; if "detailed" append a Notes column citing exact source lines or clause numbers. Step 5 End with a YES/NO question: "Is this checklist complete and ready for AP manager review?" ~ Review / Refinement Please examine the QA checklist produced. 1. Confirm that all duplicate charges, missing job numbers, price mismatches, and approval questions are represented. 2. Indicate if additional data or clarification is required. 3. Respond with one of: • "Approved – proceed with payment processing once issues are cleared" • "Needs Revision – see comments" Provide comments if revision is needed. Make sure you update the variables in the first prompt: [CONTRACTOR_NAME], [SOURCE_DATA], [OUTPUT_LEVEL]. Here is an example of how to use it: [CONTRACTOR_NAME] = "YourContractor LLC" [SOURCE_DATA] = "[link to invoices]" [OUTPUT_LEVEL] = "detailed" If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain Enjoy! submitted by /u/CalendarVarious3992 [link] [comments]

Simplify your restaurant's month-end reconciliation process. Prompt included.

Hello! Are you tired of the chaos that comes with reconciling your restaurant's month-end finances? This prompt chain walks you through a structured process to quickly and accurately reconcile your restaurant's monthly transactions, ensuring everything is in order without the stress. Prompt: [VARIABLE DEFINITIONS] [PERIOD]=Month and year to be reconciled (e.g., August 2023) [RESTAURANT_NAME]=Official operating name that must appear on every output [OUTLIER_THRESHOLD]=Percentage variance from the category mean that should trigger an “Odd Total” flag (e.g., 25) ~ Prompt 1 — Data Intake & Setup 1. You are an expert restaurant bookkeeper tasked with reconciling month-end spend for RESTAURANT_NAME covering PERIOD. 2. Request the following four source files from the user. Instruct the user to use the exact file naming convention shown: a. “1_BankExport_PERIOD.csv” – Clean CSV directly from the bank portal. b. “2_POS_Summary_PERIOD.csv” – End-of-month POS summary export. c. “3_ExpenseSheet_PERIOD.xlsx” – Internal expense spreadsheet. d. “4_ReceiptPhotos_PERIOD.zip” – Zipped folder of all receipt images or PDFs. 3. Ask the user to confirm currency, time-zone and accounting basis (cash vs accrual) if not obvious. 4. Once all four files are provided, reply with “FILES RECEIVED – ready to extract” to trigger the next prompt. ~ Prompt 2 — Extract & Normalize Transactions Step 1 | Bank Export • Parse every row of 1_BankExport_PERIOD.csv. • Capture Date, Payee, Amount (signed), Memo/Description, and unique Transaction ID. Step 2 | POS Summary • Parse 2_POS_Summary_PERIOD.csv capturing Date, Gross Sales, Net Sales, Tax, Tips, Payment Type, and POS Reference ID. Step 3 | Expense Spreadsheet • Parse 3_ExpenseSheet_PERIOD.xlsx (assume first sheet) capturing Date, Vendor, Amount, Internal Category, and Note. Step 4 | Receipt Photos • For every file in 4_ReceiptPhotos_PERIOD.zip run OCR; capture Vendor, Date, Total, Tax, Tip and file-name as Receipt Link. Step 5 | Unify • Produce a master table named “All_Transactions_Raw” with columns: Date | Vendor/Payee | Amount | Source (Bank / POS / Expense / Receipt) | Source_ID | Notes • Provide the table as an array of JSON objects for machine readability. Confirm extraction completed with “EXTRACTION COMPLETE – ready to categorize”. ~ Prompt 3 — Categorize Transactions 1. Create a reference Chart of Accounts typical for full-service restaurants: • Food Cost (COGS) • Beverage Cost (COGS) • Payroll & Labor • Operating Supplies • Utilities • Rent & Lease • Marketing & Promotion • Repairs & Maintenance • Capital Expenditure • Miscellaneous 2. Using keywords in Vendor/Payee and Notes, assign each row in All_Transactions_Raw to the most appropriate category; if uncertain assign “Miscellaneous” and add a note “Needs Review”. 3. Output a new table “All_Transactions_Categorized” including all prior columns plus a new “Category” column. 4. Provide summary totals per category. Return “CATEGORIZATION COMPLETE – ready to reconcile”. ~ Prompt 4 — Reconcile & Flag Step 1 | Missing Receipts • Compare every Bank or Expense row against Receipt rows (match on Amount ±1% and Date ±3 days). • Flag rows with no matching receipt; add column MissingReceipt=Yes/No. Step 2 | Odd Totals • For each Category calculate mean and standard deviation. • Flag any Amount whose absolute percentage variance from the category mean exceeds OUTLIER_THRESHOLD%; add column OddTotal=Yes/No. Step 3 | Duplicates & Mismatches • Detect duplicate rows (same Date, Amount, Vendor) across sources; flag Duplicate=Yes/No. • Highlight any POS Net Sales that do not match summed Bank deposits for the same day; list differences. Step 4 | Produce “Reconciliation_Detail” table with all flags appended. Respond “RECONCILIATION COMPLETE – ready for workbook generation”. ~ Prompt 5 — Generate Final Workbook & Handoff Tabs 1. Using Reconciliation_Detail create the following four logical tabs (output each as its own JSON array): a. “Summary_By_Category” – Columns: Category | Count | Total Spent | % of Total. b. “Missing_Receipts” – Filter MissingReceipt=Yes. Columns: Date | Vendor | Amount | Source | Notes. c. “Odd_Totals” – Filter OddTotal=Yes. Columns: Date | Vendor | Amount | Category | % Variance | Notes. d. “Bookkeeper_Handoff” – Clean list excluding internal calculation columns. Columns: Date | Vendor | Amount | Category | ReceiptLink | Comments (populate with MissingReceipt/OddTotal flags). 2. Provide a final object named “Workbook_PERIOD.json” containing all four arrays keyed by tab name so it can be imported directly into Excel or Google Sheets. 3. Finish with the sentence: “WORKBOOK READY – please review”. ~ Review / Refinement Ask the user to confirm that: • All four data sources were fully captured. • Categories and flagging thresholds look accurate. • The Workbook_PERIOD.json structure opens as expected in their spreadsheet tool. Invite any adjustments (e.g., new category, different OUTLIER_THRESHOLD). Apply revisions iteratively u

Configured 9 MCP servers in Claude Code over 4 months. Here's the truth nobody tells you about MCP context bloat.

I started loading up MCP servers in Claude Code back in January thinking the more capability the better. I'm at nine now: filesystem, GitHub, Stripe, Linear, Notion, Postgres, Sentry, AWS, and a custom internal one. Total tools across all of them: 142. What nobody warns you about: every one of those tool definitions lands in your context window before any user prompt has been sent. I checked with Claude's tool inspector. Cold start: 38k tokens of system prompt + tool schemas. Every. Single. Turn. The math nobody talks about At ~$15/M output and ~$3/M input on Sonnet, doing 200 turns a day across my agent + Claude Code use: 38k input × 200 turns = 7.6M tokens/day = ~$23/day = ~$700/month JUST in MCP tool definitions This is before any actual work Cache helps but only on identical prefixes; rotate one MCP and the cache invalidates What actually breaks The model gets dumber with too many tools. Not theoretical, watched it myself. With 142 tools in context, Claude started picking the wrong tool for obvious queries (using linear_search_issues when I asked it to read a file). The tools API call itself slows down. Schema-heavy MCP servers (looking at you, AWS) take 4-6 seconds to enumerate. Errors compound silently. One badly-described tool taints the ranking for every related query. What the "MCP optimizer" startups won't tell you Most of them are just BM25 search dressed up. You don't need a vector DB, you don't need an LLM in the loop to rank tools. Tool descriptions are short, structured, and full of keyword matches. BM25 over a flat projection of name + description gets you 90% of the win, deterministically, in microseconds, and offline. The other thing: "replace" beats "suggest" every time. If your gateway hands the model 5 tools instead of 142, the math works. If it suggests 5 alongside 142, the model still loads 142 and you saved nothing. What I do now Switched to a gateway pattern. Claude sees three tools: search_tools, invoke_tool, auth. Everything else gets ranked on-demand. Cold start dropped from 38k to ~4k. Wrong-tool selections basically disappeared because the model only ever sees the top 5 ranked by query. Specifically running Ratel (open source, in-process Rust lib, BM25 ranking, one command does the Claude Code import). Not the only one in the space but the only one with the architecture I actually wanted. Set it up in 10 minutes. Anyone else hit the same MCP wall? Curious what other folks are doing, especially people running 5+ servers in production. submitted by /u/AbjectBug5885 [link] [comments]

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

I maintain a running list of 200+ app design specs you can drag into Claude to clone a UI

Describing a UI to Claude in prose gets you something close but wrong: off colors, off spacing, missing states. The thing that actually works is handing it an exact spec instead of a description. So I keep a compiled list of 200+ popular apps already written up as structured markdown design specs. Each app: exact hex codes, type scale, spacing, every screen state, the nav graph. SwiftUI, Jetpack Compose, and Expo versions for each. You drag the one you want straight into Claude (or Cursor, or whatever agent you run) and it has the actual values instead of guessing at them. It's one collection you can browse and pull from: https://spectr.to/gallery Started at 50 apps, it's 200+ now. Markdown, no dependencies, drop-in. Two things I'd actually use this thread for: which apps are worth adding next, and for people already cloning UIs with agents, do you get better results dragging the spec in as a file or pasting it inline? I keep going back and forth on that. submitted by /u/meliwat [link] [comments]

Passed Claude CCA-F with 10+ teammates — notes and prep advice

Over the past few weeks, 10+ people on our team have taken and passed the Claude Certified Architect – Foundations (CCA-F) exam. After comparing notes, our main takeaway is: This is not really an API memorization exam. It is much closer to a scenario-based architecture judgment exam. You are not just asked whether you know a Claude feature. You are asked whether you can make reasonable design trade-offs when Claude is used inside real products, agent workflows, developer tools, and automation systems. Some of the recurring questions are more like: Should this task be handled by one agent or multiple sub-agents? Is this tool doing too much? Are the permissions too broad? Is MCP actually needed here, or is it over-engineering? Should this action be automated, or should there be human review? How should structured output be validated? How should long-context workflows be managed reliably? What is the safest next step in a partially automated system? Here are our notes for anyone preparing for the exam. 1. Basic exam structure Based on the official outline and public exam writeups, the exam is: 120 minutes Multiple choice 4 options per question Score range: 100–1000 Passing score: 720 The exam domains are: Agent architecture and orchestration — 27% Tool design and MCP integration — 18% Claude Code configuration and workflows — 20% Prompt engineering and structured output — 20% Context management and reliability — 15% One public writeup also mentioned that there are 6 scenario categories, and the exam randomly selects 4 of them. So this is not a “random facts about Claude” exam. It is much more about reading a realistic scenario and choosing the safest, simplest, most appropriate architecture. 2. The three principles that kept coming up After reviewing the questions we struggled with, we found that many of them came back to three design principles. 1. Least privilege Do not give a tool, agent, or workflow more access than it needs. Examples: If read-only access is enough, do not grant write access. If access to one repository is enough, do not grant access to the whole workspace. If a tool only needs one narrow action, do not expose a broad system-level capability. If an action is high-risk, do not fully automate it without review. A lot of wrong answers look attractive because they are powerful or automated. But they often give the model or tool too much authority. 2. Single responsibility A tool should not do everything. A sub-agent should not become a “general-purpose employee” that retrieves data, makes decisions, modifies files, submits changes, and notifies people all in one step. Many questions test whether you understand where the responsibility should live: Should this be a tool? Should this be agent reasoning? Should this be a human decision? Should this be a separate validation layer? Should this be split into smaller components? If one component is doing too much, be careful. 3. Avoid over-engineering This was probably the biggest pattern. Some answers look sophisticated: Multi-agent orchestration Complex MCP workflows Long-term memory Fully automated tool execution Multi-stage validation pipelines But if the problem is small, narrow, and low-risk, the best answer is often the simplest controlled solution. Our internal summary was: Do not choose the most impressive architecture. Choose the smallest, safest, most controllable one. 3. English reading is a real hidden challenge For non-native English speakers, this may be one of the hardest parts. The questions are often long scenario descriptions. They may include: the current system design the team’s goal existing constraints the risk profile what tools are available what the next step should be The answer choices can also be long. Sometimes one word changes the meaning of the whole option. Words like: automatically always unrestricted without review full access all repositories execute directly can make an option much riskier than it first appears. So our advice is: Practice reading English scenarios directly. Do not rely on translation tools. During the actual proctored exam, you should not expect to use Google Translate, Chrome translation, DeepL, Claude, ChatGPT, or any other external translation tool. For the last few days before the exam, it is worth forcing yourself to read only English material and English practice questions. 4. ProctorFree exam setup The exam is online and uses ProctorFree. The rough flow is: You receive the exam email. You follow the exam link. You download and install ProctorFree. You complete the pre-exam setup. The system checks camera, microphone, network, and screen recording. You start the exam. The session is recorded. After submission, you wait for the upload to complete. Practical setup tips: Use only one monitor. Disconnect external displays. Close unnecessary applications. Clos

Built an MCP for claude code that turns ticket-mentions into PRs with browser QA (and what I learned along the way)

notesasm is an MCP server you add to claude code. you mention a fix mid-flow ("make a ticket on notesasm: fix the regex for quoted emails") and it files the ticket. later, on your schedule, an autonomous agent picks the ticket up, writes the fix, runs real-browser QA against your preview deploy, and opens a PR with screenshots. closed alpha, free during it. demo + signup: notesasm.com the pain it solves (3 separate ones, actually): claude code is fast enough now that shipping isn't the bottleneck anymore. when you're deep in a feature and notice "the regex misses RFC-quoted local parts" or "the footer copy is wrong on mobile", you'd never break flow to open jira/linear or even write it down anywhere. so the idea goes nowhere. multiply by a year and your repo has invisible debt nobody's tracking. claude code helps while you're at the keyboard. it doesn't help while you sleep. your repo doesn't move overnight unless you stayed up to push it. for solo founders or small teams, that means losing 8 hours a day where you could be shipping if you had a way to delegate work to your own agent. and even if you do have something pushing code for you overnight, you lose context with AI-generated PRs and they usually need visual review. claude writes code that compiles and tests pass, but the actual rendered output might be subtly broken (or super broken lol). reviewing those visually is tedious and a lot of teams skip it, then ship regressions. how it works: you add the MCP server: claude mcp add notesasm --scope user --transport http -H "Authorization: Bearer ". BYOK style, the token comes from your dashboard. zero local install beyond the one command. then in any claude code session you can say "make a ticket on notesasm for this" (based on your conversation) and it just files it. the MCP server is HTTP-transport (not stdio), runs in the cloud, hits a fastapi backend that stores the ticket in postgres against your workspace. later (your schedule, your spend cap), a worker process picks up queued tickets. for each one: clones your repo with a github app installation token (commits look like asmnotes[bot], a verified author. bypasses vercel/netlify deploy protection that rejects unknown-team-member commits.) runs the claude agent sdk with your ticket body as the prompt. defaults to sonnet 4.6, opus 4.7 for hard tickets the user marks explicitly. agent reads the codebase, makes the edits, commits, pushes a branch, opens a PR via the github API. waits for your preview deploy to land. vercel polled by default, configurable probe URL for split frontend/backend setups like vercel + railway. QA agent drives a real chrome session on browserbase against the preview. stealth profile with residential proxies. takes before/after screenshots. verifies your acceptance criteria against the rendered output. if QA fails, the report feeds back into the build agent for up to 3 retry iterations before parking the ticket. final: PR with QA screenshots in the description, ready to merge. stack: - backend: fastapi + asyncpg + railway - frontend: vanilla html/js, no build step, vercel - agents: claude agent sdk (build), claude + browserbase (QA) - auth: clerk - email: resend (welcome, invite, feedback) - mcp transport: http (cloud-hosted, no local install) things i learned building it that other claude code folks might care about: - the build agent loves to spawn subagents via the Task tool. disable it explicitly in the system prompt or you get 4-minute hangs the SDK doesn't surface as errors. - browserbase sessions default to a ~5-min timeout. if your QA wall budget is anywhere near that, set the session lifetime explicitly to 1800s on session create (the timeout field). otherwise you get random "410 Gone" mid-run. - don't rely on the SDK's wall budget alone. add a per-message timeout (90s works) so a hung tool call doesn't silently burn your whole budget. - claude code's default mcp scope is per-cwd. always tell users `--scope user` in your install instructions, otherwise the MCP works in one repo and silently doesn't in others. - ResultMessage emissions happen multiple times per job if you have iteration loops (build + QA + qa-fix). sum them all when computing per-job cost, not just the last one. what's next: closed alpha is open. would love ~30 active users to try it out, all free during it. paid plans later this year with a permanent discount for alpha users. happy to answer anything about the MCP design, the QA verification loop, cost tracking, the agent-sdk integration, or anything else. demo + signup: notesasm.com submitted by /u/FormExtension7920 [link] [comments]

After speccing 200 apps for Claude, here's what you can safely cut

I've now written design specs for 200 apps and fed them to Claude to rebuild the UIs in SwiftUI, Jetpack Compose, and Expo. Early on I over-specced everything. After 200, the pattern is clear: most of a long spec is dead weight, and a few parts carry the whole result, regardless of target framework. What you can cut without hurting the clone: - Prose descriptions of layout. Claude infers structure from the component list. - Pixel margins on every element. A spacing scale covers it. - Adjectives. "Clean, modern, minimal" changes nothing in the output. What you cannot cut, the parts that move the result: - Exact color values, not names. - Every screen state listed up front (empty, loading, error, filled). - The type scale as fixed values. - Navigation as explicit screen-to-screen transitions. Those four hold whether Claude targets Swift, Compose, or Expo. The framework changes how it's expressed, not what the spec needs. A spec that is just those four outperforms a three-page document. Public, 200 apps, Swift / Jetpack Compose / Expo specs for each: github.com/Meliwat/awesome-ios-design-md submitted by /u/meliwat [link] [comments]

MCP server for the TLA+ model checker tla-rs

Hi all, Just shipped an MCP server some of you might find useful: **tla-mcp**. TLA+ is a formal-spec language for designing concurrent and distributed systems. You describe what your protocol should do and a model checker tries every reachable state to catch invariant violations, deadlocks, race conditions you didn't see coming. With tla-mcp registered, Claude Code can call the checker as a first-class tool: validate a spec, run a bounded check with a counterexample trace, replay specific scenarios, all from inside the chat. Tool descriptions are deliberately opinionated about how the model should use the checker (budget all limits upfront, treat `limit_reached` as inconclusive, look at the last transition of a trace first) so the guidance survives context truncation. Install + client config snippet + tour of the four tools is on the landing page: **https://fabracht.github.io/tla-rs/** It's an experiment. Feedback and bug reports welcome. submitted by /u/Anxious_Tool [link] [comments]

Claude keeps asking for permission when I have allow bypass on

I’m new to Claude, I have allow bypass on in Claude extension for antigravity. Then bypass permissions mode selected for antigravity. I still get these pop ups, anyway to fix and have Claude run more automatically after commands? submitted by /u/crypto_69teen [link] [comments]