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uygnoey

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1. ▌
   Long Running Agent Development · uygnoey bundle

   Build agents whose sessions run for tens of minutes to hours, using Outtake's four-stage process and four hard-won learnings from their Recon Agent. Use when context compaction and behavioral drift are real rather than theoretical, when an agent keeps ignoring a system-prompt instruction no matter how it is worded, when deciding whether to stay in Claude Code or graduate to the Claude Agent SDK, when choosing between purpose-built tools and a filesystem plus bash, when manual transcript review no longer scales, or when the agent must operate in an environment that is actively trying to hijack it.
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2. ▌
   Microsoft 365 Copilot Model Choice · uygnoey

   Help an organization map Microsoft 365 Copilot entry points (Researcher, Copilot Studio) to appropriate Claude model choices mentioned in the announcement.
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3. ▌
   Time Bounded Extraction Context Engineering · uygnoey bundle

   Build reliable, reviewable extraction prompts by assembling the right domain evidence and explicit time anchors (e.g., “most recent value before procedure start time”), and by using granular evaluation to locate failures in prompts vs context vs retrieval.
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4. ▌
   Closing The Patch Gap · uygnoey

   Prioritizes and operationalizes software patching for an environment where AI models quickly reverse patches into exploits. Use when planning patch policy, reducing time-to-patch on internet-exposed systems, triaging large CVE backlogs with KEV and EPSS, or rolling out automated patch deployment.
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5. ▌
   Verification Loop Builder · uygnoey bundle

   Turn the manual checks you repeat after every change into Claude Code skills, so Claude closes its own feedback loop instead of waiting for a human to remember the check. Use when you keep making the same small corrections after Claude implements a feature; when a project-specific rule ("reject any migration that drops a column without a backfill step") is real but no generic linter catches it; when deciding whether a check should run standalone, embedded in the producing skill, chained after another skill, or on every PR; or when adding verification to a skill you cannot edit. Covers the built-in loops to try first (/verify, toolchain signals, Code Review, GitHub Actions, spec validation, rubrics in Claude Managed Agents), the minimal SKILL.md shape for a verification skill, the four deployment patterns with their costs, and the six-step creation process.
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6. ▌
   Healthcare Agent Implementation · uygnoey bundle

   A practical framework for implementing AI agents in healthcare and life sciences, focusing on interoperability, latency, compliance, and maintaining human clinical authority.
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7. ▌
   Managed Agents Private Execution · uygnoey bundle

   Use this skill to decide whether to run Claude Managed Agents with self-hosted sandboxes and/or MCP tunnels, and to collect the minimum deployment details and security considerations for each option.
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8. ▌
   Agent Workflow Patterns · uygnoey bundle

   Helps choose and apply common AI agent workflow patterns (sequential, parallel, evaluator–optimizer). Use when designing multi-step agent systems, deciding whether to parallelize subtasks, or improving output quality with iterative evaluation.
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9. ▌
   Computer Use Best Practices · uygnoey bundle

   Instructions
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10. ▌
    Dynamic Workflows Harness · uygnoey bundle

    Patterns and reusable prompt templates for creating “dynamic workflows” in Claude Code to orchestrate multi-agent harnesses for complex, high-value tasks.
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11. ▌
    Government Deployment Planning · uygnoey bundle

    Plan a government or regulated-environment rollout of Claude Code and Claude Cowork through Claude for Government Desktop — authorization boundary, where data is stored and processed, SCIM-based delegated administration, spend caps, audit logging, and MDM distribution. Use when preparing an ATO package or security review, when a department needs to allocate seats and limits across sub-agencies, when finance needs consumption-based spend bounded against appropriated funds, or when answering IG and auditor questions about usage without exposing sensitive material.
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12. ▌
    School District Rollout · uygnoey bundle

    Move a K-12 school or district from individually verified teacher accounts to a centrally managed Claude for Teachers account — verifying the district, accepting the K-12 terms, connecting the email domain and single sign-on, and understanding what changes for teachers already using it. Use when planning a district-wide rollout, when checking eligibility and the free-access window, when confirming FERPA posture and student data handling before approving a deployment, or when deciding which teaching capabilities to introduce first.
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13. ▌
    Multi Agent Decision Framework · uygnoey bundle

    Decide when to use multi-agent architectures (context protection, parallelization, specialization), how to decompose work by context boundaries, and how to add a verification subagent pattern with explicit pass/fail criteria.
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14. ▌
    Business Admin Controls Overview · uygnoey bundle

    Summarize the Claude blog announcement about Claude Code availability via premium seats and the new admin controls, analytics, and Compliance API for Team/Enterprise plans.
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15. ▌
    Agentic Coding Trends 2026 Brief · uygnoey

    ---
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16. ▌
    Opus 4 7 Code Best Practices · uygnoey

    Applies the Opus 4.7 prompting and session-tuning guidance from "Best practices for using Claude Opus 4.7 with Claude Code". Use when opening a Claude Code session with Opus 4.7, choosing an effort level (high / xhigh / max), batching questions, or deciding whether to nudge toward more tool use and parallel subagents.
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17. ▌
    Writing Quality Vuln Reports · uygnoey

    Writes upstream-quality vulnerability reports when a model has assisted in finding the bug. Use when preparing to send a finding to an open-source maintainer, vendor, or internal security team, especially if AI was involved in discovery or drafting.
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18. ▌
    Model Class Selection · uygnoey bundle

    Choose which Claude model class — Mythos/Fable, Opus, Sonnet, or Haiku — to run a workload on, and use effort level to dial in the quality/speed/cost balance. Use when picking a model for a new production workload, when revisiting a model choice after evals show a gap, when a workload is latency- or cost-sensitive enough that a lower class is worth testing, or when deciding whether to pair a cheaper worker model with a more capable advisor. Default recommendation is to start with the most intelligent generally available model and tune down, because cost-per-task is often lower on more capable models even when price-per-token is higher.
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19. ▌
    Structured Outputs Reliability Guide · uygnoey bundle

    Checklist and guidance for using structured outputs (schema/tool-conformant responses) to reduce parsing errors and tool-call failures in production.
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20. ▌
    Hackathon Builder Patterns · uygnoey bundle

    Reusable planning and iteration patterns drawn from multiple winning projects in Claude’s “Built with Opus 4.7” hackathon.
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21. ▌
    Secure AI Native Sdlc · uygnoey bundle

    Design and operate security controls for a software development lifecycle where agents author most of the code. Use when the security review queue has become the bottleneck on shipping, when deciding where human gates belong once the build step takes hours instead of months, when setting agent identity and permission boundaries (including agent-to-agent access), when introducing AI reviewers that must earn trust before they can block or approve, or when a security program needs an audit trail for decisions made by agents rather than people. Covers the Plan, Code, Test/CI, Deploy/CD, Monitor, and Governance stages with an enduring principle for each.
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22. ▌
    Cobol Modernization Planning Playbook · uygnoey bundle

    A structured workflow for using AI assistance (e.g., Claude Code) to map, plan, test, and execute incremental COBOL modernization with human review and stepwise validation.
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23. ▌
    Agentic Analytics Warehouse · uygnoey bundle

    Use this skill when the user asks for business analytics that requires querying a data warehouse (SQL and/or governed semantic layer), and the work must be reliable under entity ambiguity, freshness/staleness risk, and retrieval gaps.
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24. ▌
    Spec Driven Agent Verification · uygnoey bundle

    Shift an agentic codebase from "agents emit code" to "agents emit verifiable specifications." Use when agents generate code faster than the team can review it and verification has become the real bottleneck, when control logic is scattered across routes, database constraints, service code, background jobs, and docs so no one can state the system's state machine, when agents need to change operational behavior safely without a full CI cycle, or when designing an autonomous build-and-operate loop and deciding what humans must still coordinate. Based on Datadog's Temper, a "universal machine tool" that verifies agent-emitted specs through four independent layers before anything runs.
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25. ▌
    Skills And MCP Architecture Playbook · uygnoey

    Guidance for designing workflows where MCP servers provide tool access and skills encode procedural expertise, sequencing, and output standards.
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26. ▌
    Code Case Study To Notes · uygnoey bundle

    Turn a Claude Code project case study into reusable implementation notes (problem, approach, agents, tests, constraints, and takeaways) without inventing details.
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27. ▌
    MCP Production Integration Patterns · uygnoey bundle

    Patterns for connecting agents to production systems, including when to use direct APIs vs CLIs vs MCP, and how to design MCP servers and clients for scale, security, and context efficiency.
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28. ▌
    Instruction Placement Framework · uygnoey bundle

    Decide where to place Claude Code instructions (CLAUDE.md, rules, skills, subagents, hooks, output styles, or appended system prompt) based on load timing, compaction behavior, context cost, and authority.
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29. ▌
    Code Org Adoption Playbook · uygnoey bundle

    A reusable playbook for adopting Claude Code across an organization, based on practices highlighted in Brex’s case study (context files, documentation hygiene, and context-aware commands).
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30. ▌
    Verifiable AI Trust Layer · uygnoey

    Design an auditable AI system by pairing Claude’s interpretation and reasoning with deterministic retrieval, computation, provenance, and evaluation so every claim can be traced to source.
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31. ▌
    Skills Packaging Principles · uygnoey bundle

    Package domain expertise into scalable Agent Skills using progressive disclosure (metadata, SKILL.md, and on-demand references).
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32. ▌
    Skill Evals Maintenance Playbook · uygnoey bundle

    Maintain Agent Skills over time by defining evals, running benchmarks, comparing variants, and tightening skill descriptions to reduce false triggers.
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33. ▌
    M365 Cross App Collaboration · uygnoey

    Collaborate with Claude across Microsoft 365 apps (Excel, PowerPoint, Word, Outlook) while keeping one continuous conversation context and updating related artifacts.
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34. ▌
    Onboarding Cc · uygnoey

    Treat Claude Code onboarding like developer onboarding by maintaining durable, versioned project context (e.g., a root CLAUDE.md plus a small skill library and references to deeper documentation).
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35. ▌
    Firm Wide AI Rollout · uygnoey bundle

    Plan and run a firm-wide AI assistant rollout the way a professional-services firm does it — pick the surface set rather than a single tool, pilot before going broad, wrap the launch in enablement and governance, and measure adoption by active use within weeks instead of seats provisioned. Use when deploying an AI assistant across an entire workforce, when a pilot needs to be turned into a general availability plan, when adoption has stalled after licences were handed out, or when a delivery organization wants its internal deployment to become the basis for client-facing work.
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36. ▌
    Frontier Defense Partner Evaluation · uygnoey bundle

    Evaluate and shortlist the Claude Opus-powered defensive cybersecurity partner offerings that Anthropic publicized on May 21, 2026. Use when a security buyer or CISO is choosing among the three families of offerings — continuous offensive testing at production scale, closing the find-to-fix gap, and getting AI into production with governance — and needs a structured comparison against the partners Anthropic explicitly named (Wiz, Palo Alto Networks/Unit 42, CrowdStrike, Accenture, TrendAI, Deloitte, PwC), plus an awareness flag for BCG, Infosys, and SentinelOne.
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37. ▌
    Native Sandboxing Basics · uygnoey

    Establish safer, more autonomous boundaries for agentic coding by applying filesystem and network isolation (a "native sandboxing" approach) so the agent can operate with fewer per-action permission prompts.
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38. ▌
    Usage And Spend Governance · uygnoey bundle

    Set up visibility and cost control for an enterprise AI deployment using admin analytics, model defaults and entitlements, spend-threshold alerts, and programmatic access through the Analytics API and Admin API. Use when an org's usage and cost patterns stop looking like a chat tool's and start looking like agentic work, when finance or IT needs usage and cost data alongside the rest of cloud spend, when routine work is defaulting to the most expensive model, when users are hitting spend limits mid-task, or when someone asks what value the deployment is actually returning per team or per seat.
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39. ▌
    Supervised Risk Analyst · uygnoey bundle

    Build an analyst agent that works alongside human experts in a high-stakes domain — surfacing insights and forming opinions on exposure, while logging its reasoning, testing actions in sandboxes, and requiring expert approval before decisions count. Use when designing an AI teammate for risk, compliance, or another regulated analytical function; when outputs must be auditable back to their reasoning; when analysis depends on proprietary data plus judgment the firm will not delegate; or when the job is to explain how numbers changed since yesterday rather than answer one-off questions.
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40. ▌
    Large Codebase Rollout · uygnoey bundle

    Best-practice playbook for making Claude Code reliable in large repositories by building a harness (CLAUDE.md layering, hooks, skills, plugins, MCP, LSP) and establishing ownership.
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41. ▌
    Agent Fleet As Code · uygnoey bundle

    Run a fleet of production agents as code in a git repository — every agent a config file plus a prompt, every change a reviewed pull request, every improvement harvested from human feedback rather than retraining. Use when AI automations are scattered across individual laptops with no shared view of what exists or what it costs, when non-developers need to ship production agents without the dev team becoming the bottleneck, when agents need version history and an audit trail instead of an admin screen, or when you need a defensible way to decide which tasks deserve an agent at all.
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42. ▌
    Human Agent Team Practices · uygnoey bundle

    Set up and run a team where people and agents work together in shared channels. Use when agents have workspace access but return shallow answers because context lives in private threads; when deciding which work an agent owns and where a human reviews; when defining roles for several specialized agents instead of one general assistant; when adoption is stalling and needs peer demonstration rather than mandate; or when leadership asks for evidence of value and only usage metrics are available.
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43. ▌
    Agent Prompt Caching Best Practices · uygnoey bundle

    Keep prompt cache hit rate high in long-running agents by following the patterns Claude Code's team uses — lay out static-then-dynamic, push updates into messages instead of editing the system prompt, never change models or tools mid-session, model state transitions as tools (Plan Mode), defer tool loading instead of removing tools, and implement compaction as cache-safe forking. Treat cache breaks as incidents.
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44. ▌
    Sales Cowork Workflows · uygnoey

    Turn recurring sales leadership routines (meeting prep, weekly forecast rollups, and large-scale account scoring) into scheduled, repeatable workflows that assemble data from multiple systems and produce standardized outputs with human approval.
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45. ▌
    Hackathon Project Verification Patterns · uygnoey bundle

    Patterns observed in Build Day projects for planning, verification with independent sub-agents, and cost/latency optimization under tight time constraints.
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46. ▌
    Agent Orchestration Planning Layer · uygnoey bundle

    Add a structured planning-orchestration layer before code generation to make outcomes, assumptions, edge cases, and validation criteria explicit, producing a reviewable PRD-style plan.
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47. ▌
    Cowork Enterprise Rollout Playbook · uygnoey bundle

    Plan and run a Claude Cowork enterprise rollout the way Anthropic's own April 29, 2026 deployment guide recommends — pick a first business function, structure a pilot, walk the five-level maturity model, and execute a six-month champion-to-org-wide roadmap, anchored on the post's named internal teams and customer references (Thomson Reuters, Zapier, Jamf).
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48. ▌
    Context Engineering For New Models · uygnoey bundle

    Rightsize the context you hand a newer-generation model — system prompt, CLAUDE.md, skills, tool descriptions, and references — by removing rules the model no longer needs and replacing them with judgment, interface design, and progressive disclosure. Use when a system prompt or CLAUDE.md has grown long, when instructions contradict each other, when a skill over-constrains the model, when tool guidance is duplicated between the system prompt and the tool description, or when migrating an agent built for an older model generation.
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49. ▌
    Desktop Enterprise Rollout · uygnoey

    Roll out Claude Desktop across an organization with cloud-hosted inference and enterprise controls, based on a provider-backed deployment (AWS, Google Cloud, or Microsoft Foundry).
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50. ▌
    Early Model Testing Playbook · uygnoey

    A practical playbook for running a short pre-launch model evaluation window that blends automated evals with hands-on “vibe checks.”
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51. ▌
    Long Horizon Agent Runs · uygnoey bundle

    Hand an agent work that runs for hours unattended and get something you would actually keep — by stating the invariants up front, giving the agent the real debugging tools, and requiring it to name what it does not know instead of asserting the first plausible answer. Use when delegating a migration or a backlog of bugs overnight, when an agent drifts off-task on long runs, when triage answers stop being trusted, or when deciding whether a task is safe to leave running.
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52. ▌
    Defensible AI Outputs · uygnoey bundle

    Build AI output that a professional can put their name on — grounded in authoritative content, checked for citation validity before a human ever reviews it, and transparent enough to defend when the stakes are high. Use when the work product has to hold up under professional review (legal, tax, accounting, compliance), when deciding what to require of a model before trusting it with that work, or when deciding what the human in the loop is actually accountable for.
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53. ▌
    Dogfood Model Evaluation · uygnoey bundle

    Decide whether a new model is actually better by putting it through a real day of work with your highest-taste engineers and asking whether the code is something they would keep, rather than trusting a benchmark score. Use when a model aces a benchmark but you are unsure it will hold up in practice, when building an anti-slop internal benchmark, when a score jump needs corroboration, or when a team keeps arguing for weeks about whether a release is an improvement.
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54. ▌
    Inbound Reply Drafting · uygnoey bundle

    Run a sales inbox as an hourly drafting loop — a thin system prompt, a curated knowledge base of common questions and best answers, and a per-rep voice profile — so every thread needing a reply gets a draft the rep reads, edits, and sends. Use when inbound volume is consuming hours a day on repeated questions; when replies must stay factually anchored to approved product answers; when drafts should sound like the person sending them; or when adding lighter monitors for no-shows, prospects going dark, and new leads that need a first touch.
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55. ▌
    Event Build · uygnoey bundle

    Set up an event, webinar, or integrated campaign end to end across the CRM, the marketing automation platform, the event platform, and email — including the integrations between them, which are rarely complete out of the box. Use when a build request has been routed from the intake queue. Posts two Slack updates (on pickup, and when the landing page is ready for review), then hands off to a fresh audit agent rather than marking its own work complete.
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56. ▌
    Discovery Call Scorecard · uygnoey bundle

    Score a discovery call against the team's playbook and return a scorecard — top three things done well, top three areas to improve, an explicit pass or fail against the criteria, and the single highest-leverage thing to practice next. Use when call coaching does not scale past the calls a manager can personally listen to; when feedback needs to be specific to what was said rather than generic; or when a team wants a consistent, playbook-anchored standard applied to every discovery conversation.
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57. ▌
    Finance Narrative Integrity Layer · uygnoey bundle

    Keep finance narratives (board decks, monthly reviews) coherent as numbers refresh by using Claude as an integrity layer for reconciliation, consistency checks, and first-pass variance commentary.
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58. ▌
    Account Prospecting Sweep · uygnoey bundle

    Run an overnight prospecting sweep across a rep's whole book so each morning starts with a brief, a score, and an outbound play per account. Use when outbound coverage is capped by manual research across a hundred-plus accounts; when account state lives scattered across CRM, sales tools, call recordings, and a data warehouse; when research must be validated against curated outbound guidance and ICP criteria; when a memory file is needed to stop duplicate outreach; or when handling one-off book-wide analysis requests like undiscovered usage sweeps and event invite lists.
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59. ▌
    Agent First Product Rebuild · uygnoey bundle

    Rebuild a product from sequential, separate skills into a single agent that plans and orchestrates across tools in real time, and test candidate models on the ability that architecture depends on — planning and effective tool use. Use when a product is a collection of one-shot features that users have to sequence themselves, when evaluating whether a model can carry an agent across hundreds of tools, or when deciding what to measure about an AI initiative beyond cost per task.
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60. ▌
    Pipeline Hygiene Proposals · uygnoey bundle

    Keep a CRM current by reading the team's own guidance on opportunity stages, checking it against what is actually happening in email and call recordings, and proposing each update with the evidence behind it for a rep to approve. Use when pipeline data drifts because updating stages is manual; when a stage change should be justified by evidence rather than memory; when an agent must propose rather than write to the system of record; or when rejected proposals should teach the skill instead of being silently discarded.
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61. ▌
    Enterprise AI Retail Rollout Playbook · uygnoey bundle

    A practical, executive-friendly rollout playbook for moving from AI pilots to enterprise-wide transformation in retail, based on Anthropic’s retail guide summary.
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62. ▌
    Domain Model Benchmarking · uygnoey bundle

    Decide whether a new model is worth adopting by running it against a domain-specific benchmark head-to-head with the model it would replace, rather than reading public scores — build tests that replicate the real jobs your users do, expand the benchmark with each release, and separate retrieval quality from synthesis quality when reading the results. Use when a new model ships and someone asks whether to switch, when building an internal eval for a high-accuracy domain, or when a benchmark result jumps and you need to know what actually improved.
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63. ▌
    Document Grounded Analysis · uygnoey bundle

    Run analysis over large sets of dense, unstructured documents so that every answer is traceable to its source — decompose a plain-language request into per-step questions, answer each one in its own cell, cite it back to the document it came from, and keep the human able to see and steer each step. Use when working through data rooms, credit agreements, public filings, or any pile of proprietary documents where a missed detail is a real cost, and when turning that analysis into first-draft memos or covenant reviews.
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64. ▌
    Visual Ideation Workflow · uygnoey bundle

    Explore, iterate on, and share visual ideas early — product prototypes, slide decks, landing pages, one-pagers, animations — by treating HTML as an interactive visual medium and directing the aesthetic explicitly. Use when engineering throughput has outrun design, when you need many versions of a flow fast for feedback, when generated visuals keep drifting to a generic look, or when deciding whether work belongs in early ideation or in production code.
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65. ▌
    Report Proofreader · uygnoey bundle

    Check that every number in a draft report traces back to a verified source before the report ships. Use on any metrics review, leadership slide, or data-backed narrative assembled from mixed sources — dashboards, a data warehouse, Slack messages, call transcripts. Marks each figure verified, unverified, or mismatched, and never reconciles two disagreeing sources on its own. The post recommends building this skill first, before any other reporting automation.
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66. ▌
    Weekly Metrics Report · uygnoey bundle

    Assemble a weekly marketing metrics review when the numbers are scattered across a dashboard, a data warehouse, Slack, and call transcripts. Use to run the Sunday-night data hunt, produce metrics tables with suggested headlines, expand a confirmed headline into narrative with supporting detail, generate the leadership slide from the same data, and turn follow-ups into tasks. Also use at the quarter turn, when quarterly plans lead instead of weekly movement. Flags mismatches between sources rather than reconciling them silently.
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67. ▌
    Slack Analytics Agent Deployment · uygnoey bundle

    Deploy a Slack-based data analytics agent that a whole company can ask ad hoc questions — keep its skill files refreshed like a data model, give it analytical skills beyond table access, wire it into business context, permission its service account deliberately, and instrument every answer so adoption and correctness are measurable.
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68. ▌
    Senior Scope Delegation · uygnoey bundle

    Hand a model the work that was previously reserved for your most senior or most specialized engineers — core changes touching interdependent parts — by briefing the goal and the why rather than every step, front-loading a question round, letting it run unattended, and gating on human review before shipping. Use when a core system change is blocked waiting for a specific engineer to free up, when deciding whether a non-engineer can own a piece of technical work, or when calibrating how much specification a model actually needs.
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69. ▌
    Marketing Ops Dispatcher · uygnoey bundle

    Hourly intake triage for a marketing operations request queue. Reads the intake channel, picks the most urgent request, stamps the ticket so the work is not duplicated, and hands off to the right specialist skill — event build, data import, apply-to-attend, or approval support. Does no setup work itself. Use when requests arrive in a shared channel through an intake form and several specialist workflows exist behind them.
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70. ▌
    App Generation Model Evals · uygnoey bundle

    Evaluate a new model for an app-generation product by running it across different app types and measuring latency, cost, and build errors, plus stress builds that exercise unusual capabilities — then read the signals that matter for production, such as turns to completion, first-prompt completeness, and whether prompt changes break the cache. Use when a new model ships and someone asks whether to switch the generation engine, when designing an eval suite for a codegen or app-building product, or when an eval passes but production cost or latency regresses anyway.
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71. ▌
    Personalized Weekly Digests · uygnoey bundle

    Build and operate a recurring, per-person digest (weekly sales updates, field-marketing briefings, team roll-ups) with Claude Code. Use when a repeating update has to be personalized per recipient from CRM, event, and content data, when a manual Sunday-night briefing needs to become an automated Monday-morning send, or when an existing digest needs to be piloted, corrected through feedback, and scaled to more teams. Covers framing the business problem instead of the technical one, showing the output format by example, wiring real data sources, converting each piece of recipient feedback into an explicit content rule, and surviving schema drift in source spreadsheets.
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72. ▌
    Solo To IOS Shipping Workflow · uygnoey bundle

    Build and ship a real app with Claude Code by iterating from a fast prototype to a production-ready iOS release, coordinating specialist subagents and using Claude for engineering + launch tasks.
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73. ▌
    Agent First Product Transformation · uygnoey bundle

    Move a product or an internal platform from bolted-on AI features to an agent-first design where people and agents work on the same items. Use when AI features shipped, adoption looked fine for a month, and usage has flattened into occasional summarization; when deciding whether to embed agents into an existing workflow or rebuild the workflow around them; when agents live in a chat surface parallel to the real work and context must be pasted in by hand; when pilots stall before production because governance, permissions, and reliability were never designed in; or when you need concrete per-function agent jobs instead of one general assistant.
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74. ▌
    Artifacts Ideation Playbook · uygnoey

    Turn a conversational idea into a shareable, interactive artifact by iterating on scope, interactivity, and user flow in short cycles.
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75. ▌
    Project Context Files · uygnoey bundle

    Authors and audits CLAUDE.md project-context files that get auto-loaded into every Claude Code conversation. Use when a developer asks how to create a CLAUDE.md, wants to stop re-explaining project structure or conventions each session, needs a team-wide configuration for consistent AI behavior in a repo, or asks to review/clean up an existing CLAUDE.md.
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76. ▌
    Tool Use Ga Integration Playbook · uygnoey bundle

    Practical checklist for evaluating and integrating Claude tool use (now generally available) into an application, based on the official announcement.
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77. ▌
    API Web Search With Citations · uygnoey

    Reusable implementation notes derived from the Claude blog post “Introducing web search on the Anthropic API”.
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78. ▌
    Remote MCP Quickstart · uygnoey

    Help a user understand and adopt remote MCP servers in Claude Code, focusing on benefits, security, and low-maintenance setup.
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79. ▌
    Team Plan Updates Summary · uygnoey

    Summarize the Claude Team plan updates from the referenced post, focusing on seat pricing and included features.
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80. ▌
    Background Task Delegation · uygnoey bundle

    Hand Claude Cowork a job that outlives one sitting — write the delegation brief, schedule it, and set the approval gates — so the work continues while you are away and only the decisions reach you. Use when a task accumulates over days rather than finishing in one session, when work should run overnight or before you arrive, when you will be moving between desk, phone, and web mid-task, or when the output must not ship until you have reviewed it. Built around the pattern of pointing Claude at the folder, thread, or half-finished deck and describing what done looks like.
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81. ▌
    Large Scale Code Migration · uygnoey bundle

    Plan and execute a large-scale code migration — porting a codebase to a new language, framework, or architecture — using parallel agent loops, a written rulebook, a dependency map, adversarial review, and a mechanical verification harness. Use when a migration is too large to do by hand, when a port or rewrite keeps stalling, when deciding how to parallelize translation work across agents, or when a migration is producing plausible-looking code that drifts from the original's behavior. Covers the six-step process (rulebook and gap inventory, stress test, translate, compile, run, match behavior), how to design resumable work queues, when to fix the rulebook instead of the file, and how to build a judge that answers for both codebases.
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82. ▌
    Prompt Caching Design Patterns · uygnoey bundle

    Decide when prompt caching helps, structure prompts so the cacheable prefix and dynamic suffix are cleanly separated, estimate cost and latency wins from the benchmarks reported in Anthropic's launch post, and pick the right Claude model among those that support caching.
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83. ▌
    Message Batches Job Design · uygnoey

    A reusable checklist for deciding when and how to use the Anthropic Message Batches API for asynchronous, high-volume processing at reduced cost.
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84. ▌
    Connected Tools Research Playbook · uygnoey bundle

    Use Claude's Integrations and advanced Research together to pull context from your work apps and produce comprehensive cited reports. Apply when deciding which third-party app to connect via remote MCP servers, when planning multi-source investigations that span the web, Google Workspace, and connected services, or when standardizing how a team uses Integrations + Research instead of building bespoke automation.
    0 installs
85. ▌
    Create And Edit Deliverable Files · uygnoey

    Create and iteratively refine ready-to-use deliverable files (spreadsheets, documents, slide decks, PDFs) from user instructions and uploaded source materials.
    0 installs
86. ▌
    Context Editing Memory Tool · uygnoey bundle

    Apply practical rules for what to keep in an agent’s context vs what to persist to memory when building long-running workflows on the Claude Developer Platform.
    0 installs
87. ▌
    Advisor Strategy Playbook · uygnoey bundle

    Pair a lower-cost executor model with a higher-capability advisor model to get targeted guidance for hard decisions without paying frontier-model cost for every token.
    0 installs
88. ▌
    Browser Agent Safety Playbook · uygnoey

    Instructions
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89. ▌
    Thinking Partner Workflow · uygnoey

    Use Claude as an iterative thinking partner by combining memory, voice, and artifact creation to move from ideas to finished work.
    0 installs
90. ▌
    Agent Tool Design Playbook · uygnoey

    Practical heuristics from the Claude Code team for designing, iterating, and pruning agent tools by thinking from the model’s perspective (“seeing like an agent”), with an emphasis on progressive disclosure.
    0 installs
91. ▌
    Desktop Browser Routing · uygnoey bundle

    Choose between the browser built into Cowork on the desktop app and Claude in Chrome for a given web task, and work safely in either one. Use when a web task should run on its own while you keep working elsewhere, when the work belongs on pages you already have open and signed into, when deciding which logins to import into the built-in browser, or when weighing the prompt-injection risk of letting an agent act on a website.
    0 installs
92. ▌
    Long Context Needle Finding · uygnoey bundle

    Improve long-context retrieval by requiring the model to first quote the most relevant sentence from the provided context before answering.
    0 installs
93. ▌
    Finance First Pass Deliverables · uygnoey bundle

    Produce structured first-pass finance deliverables and iterate with human validation, keeping work close to spreadsheets, decks, and source files.
    0 installs
94. ▌
    Interactive Visual Prompts · uygnoey

    Helps the user prompt Claude to generate and iteratively refine temporary, inline interactive visuals (charts, diagrams, and visualizations) during a conversation. Trigger when the user asks to draw a diagram, visualize a trend over time, or wants an explanation supported by interactive visuals.
    0 installs
95. ▌
    AI Native Startup Playbook · uygnoey bundle

    Stage-based playbook for using Claude across Idea, MVP, Launch, and Scale to build an AI-native startup.
    0 installs
96. ▌
    Agentic Risk Assessment · uygnoey bundle

    Assess and bound the security risk of an agentic AI deployment before approving it. Use when a team asks to connect an agent to internal systems, when reviewing a personal agent harness or an autonomous service agent, when writing an agent security review process, or when deciding which controls a vendor must demonstrate. Applies a four-question risk assessment (untrusted content, actions and identity, blast radius, observability), places the deployment on the identity spectrum from service account to human credential, and checks it against seven deployment controls covering identity, connector allowlists, per-action approval, sandboxed execution, egress allowlisting, telemetry, and an off switch.
    0 installs
97. ▌
    Console Prompt Iteration Playbook · uygnoey

    A lightweight playbook for iterating on prompts in the Anthropic Console using prompt improvement, structured examples, and evaluations.
    0 installs
98. ▌
    Connectors Directory Browser · uygnoey bundle

    Use a lightweight checklist and prompt templates to evaluate a Claude connector from a directory, clarify required permissions, and define safe success criteria before you connect it.
    0 installs
99. ▌
    Code Routines · uygnoey bundle

    Design and deploy routines in Claude Code — automations bundling a prompt, repo, and connectors that run on a schedule, via an API call, or in response to GitHub events. Covers trigger selection, prompt shape, and common patterns from the research preview announcement.
    0 installs
100. ▌
     Connectors Discovery · uygnoey

     Guidance for discovering and using Claude connectors (especially everyday-life connectors) in conversations, with an emphasis on user control over what data is accessed and shared.
     0 installs