AI & ML

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Decide which work gets a frontier model and which gets a smaller one by measuring task completion ratio alongside cost per task, then sending the frontier model only the work where the extra capability changes the outcome. Use when frontier pricing limits how widely a model can be deployed, when justifying a more expensive model for a subset of work, or when comparing models whose per-token price differs but whose token usage and wrong-turn rate also differ.

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Hand an agent a whole task instead of a pre-chunked sequence, and let it run unattended for hours or a full day — because the model re-checks its own assumptions mid-run, returns to first principles at each step, and carries memory of past failures between runs. Use when deciding how large a unit of work to delegate, when a long agentic run keeps failing on an early wrong turn, when setting up agents to run overnight without a human checking in, or when the number of tasks is rising faster than the human judgment available to review them.

uygnoey Skill

Turn a connector feature announcement (like MCP Apps interactive connectors) into a practical evaluation and rollout checklist your team can reuse.

uygnoey Skill

Applies three patterns for building applications on a model whose intelligence keeps improving — use general tools the model already knows, remove scaffolding the model can now do itself, and set boundaries only where UX, observability, or security demand it. Use when the user is designing an agent, LLM-powered app, or tool layer and is deciding how much structure to impose around the model.

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Decide when browser control is the right tool for a task and understand the safety layers behind it — autonomous approval of safe actions, prompt-injection defenses, and enterprise domain restrictions. Use when a workflow depends on an internal dashboard, legacy system, or vendor portal with no native integration, when weighing whether to let an agent act in a browser session, or when an admin needs to scope which domains are in reach.

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Checklist and implementation notes for adopting Claude API capabilities for agentic applications (code execution, MCP connector, Files API, and extended prompt caching).

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Apply the five operating rules fast-growing startups use to ship with agentic coding — everyone ships, automate the tedium, trust but verify, build for rebuilding, and prototype-dogfood-productionize. Use when deciding how a small team should organize around Claude Code; when non-engineers want to contribute product changes; when choosing which parts of the SDLC to hand to agents and how to gate them; when a rewrite keeps losing the prioritization fight; or when you want internal agent experiments to graduate into customer-facing product.

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Ship a non-deterministic commerce agent with snapshot evals instead of conversation simulation. Covers constructing test state directly and grading the final state and response, why simulated-user evals are a poor measurement tool and what they are good for, testing from long messy contradictory histories, the five coverage areas for commerce agents, and how to select a CI suite when many teams share one agent. Use when building an eval suite, when deciding what to run per pull request, or when an agent regresses in production without any test catching it.

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Use the upgraded Anthropic Console as a single workspace to build, evaluate, and iterate Claude prompts before shipping. Walks through Workbench authoring, the prompt generator, side-by-side evaluation, prompt improvement, "Get Code" export, shareable prompts for team collaboration, and the extended thinking budget for Claude 3.7 Sonnet — based strictly on the March 6, 2025 launch post.

uygnoey Skill

Keep Claude Managed Agents tool execution and connectivity inside your security perimeter using self-hosted sandboxes and MCP tunnels.

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Hand a task to Claude inside a chat thread — turning a long Slack discussion into a finished document, consolidating requests scattered across channels, or running a first-pass review in a dedicated channel — and keep the output trustworthy through verification and standing instructions. Use when a decision is buried in a 15-message thread, when the same request keeps surfacing in different channels and nobody has a consolidated list, when a review queue is the bottleneck before a specialist looks at something, or when deciding what to delegate to an in-chat agent versus what to keep for human judgment.

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Give a commerce agent long-term memory that survives the session, without paying latency for it. Covers storing facts as typed records in a production database rather than in model context, writing memory asynchronously from a separate extraction pass instead of through an agent tool, the three read layers (always in context, pre-fetched per turn, lookup tool), and the data-handling requirements for retention, correction and deletion. Use when users have to repeat constraints across sessions, or when designing memory extraction and retrieval for a shopping or merchant agent.

uygnoey Skill

Purpose

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Run a repository security scan in Claude Security and triage the findings through to a human-approved patch. Use when deciding whether a codebase scan is the right access path to frontier defensive capability; when an enterprise admin needs to enable Claude Security and point users at a repository; when triaging scan findings that arrive with a CWE category, confidence and severity ratings, and a suggested fix; or when routing an accepted finding into interactive patching while keeping a human in the approval path. Covers the scan-to-patch workflow, what the model does and does not become accessible through, the billing and eligibility terms, and the other access paths for defenders — partner-integrated products, the Defender Advantage Fund, and the Cyber Verification Program.

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Design and improve agents using a repeatable loop (context → action → verification) with practical verification patterns.

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Classify agent sessions into the 20-category work taxonomy published in "How people are using Claude Cowork", report category shares as shares of a sample rather than volumes, and attach the sampling and classification caveats that make such a report honest. Use when auditing what an agent is actually being used for, when deciding which tasks to delegate based on a published distribution rather than a guess, or when writing or reviewing a usage-share report.

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Compose computer use, the browser use tool, the Skills API, and the Files API into agents that operate software, apply your team's expertise, and return finished files. Use when an agent has to work in an application that exposes no API; when choosing between pixel-level computer use and the structure-aware browser use tool for a web task; when packaging team procedure as an uploadable, versioned skill instead of stuffing it into a prompt; or when documents need to persist across requests by ID rather than being re-sent each turn.

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Helps plan, structure, test, and distribute Claude Agent Skills. Use when drafting SKILL.md frontmatter, organizing companion files (scripts/references/assets), or setting up a testing and iteration plan for a skill.

uygnoey Skill

Applies Anthropic's prompt engineering techniques to fix prompts that return generic, off-topic, inconsistent, or hallucinated output. Use when a user asks how to write better prompts, wants to improve an existing prompt, asks about chain-of-thought, response prefilling, output format control, or prompt chaining, or is debugging unreliable model behavior. Covers five core techniques and four advanced techniques with examples and a troubleshooting cheatsheet.

uygnoey Skill

A practical playbook for choosing and evolving between five multi-agent coordination patterns (generator-verifier, orchestrator-subagent, agent teams, message bus, shared state) based on task structure, context boundaries, and information flow.

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Architect a production commerce agent as a single model in a standard agent loop, with agent skills for modularity instead of subagents. Covers the frequency rule for deciding what belongs in the system prompt versus a skill, how to design tools that call existing commerce systems, how to reshape tool results so they read as context, and how to expose UI components as tools. Use when designing a shopping or merchant agent, when tempted to split a commerce agent into subagents, or when deciding where a capability should live.

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A checklist and guidance for taking a tool-using Claude agent from prototype to production by separating the harness (agent runner) from the execution sandbox, with emphasis on sessions, credentials, and observability.

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Use voice mode as a sounding board for problems you cannot type your way through — rehearsing a pitch, weighing competing offers, reviewing your own reasoning out loud, or brainstorming — by picking the right model for the conversation, connecting the tools that let talking turn into doing, and setting the language up front. Use when preparing for a high-stakes conversation, when you want gaps found in your own logic, when a spoken session should end in a calendar change, a draft, or a one-pager, or when deciding between the free and paid voice tiers.

uygnoey Bundle

Tune when a Slack-resident agent should speak up and when it should stay quiet, now that it judges channel-wide context rather than one message at a time — pick the right response mode, write standing instructions in plain language, and use the suppression controls when it is contributing too often.

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Enforce commerce agent safety in the harness rather than in the prompt. Covers the four principles - the model stages while a person or policy applies, writes and renders accept only server-issued IDs, capped transactions hold under repeated and parallel requests, and all third-party content is sanitized before reaching the model. Use when an agent can touch money, carts, orders, prices, promotions or budgets, when defending against prompt injection from listings, reviews or seller messages, or when reviewing an agent design before production.

uygnoey Skill

Use Claude Managed Agents scheduled deployments and vault environment variables to run routine work on a cron schedule and securely authenticate CLI tools without exposing secrets to the model.

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Decide which model and which effort level to use in Claude Code, and diagnose which of the two to change when a result misses the mark. Use when a session produced a wrong or shallow answer and the instinct is to reach for a dial, when setting a team default for a kind of work, or when deciding whether a routine stretch of work can drop to a smaller model. The core diagnostic is "did it not know enough, or did it not try hard enough?" — not knowing enough points at the model, not trying hard enough points at effort, and both point upstream at context first.

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Make a commerce agent fast and affordable by attacking task-completion latency, perceived latency, and prompt-caching hit rate separately. Covers the three latency levers (fewer turns, faster tools, faster tokens), pre-loading context, parallel tool use, eager dispatch, progressive streaming and progress lines, the three-segment cache layout that reaches 90-99% hit rates, and how to sweep models and effort levels against quality, latency and per-task cost. Use when an agent feels slow, when per-task cost is too high, or when choosing a model and configuration.

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Create a domain-expert partnership plan to benchmark and improve model performance on specialized agentic tasks.

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Get more out of each Claude Code session by controlling what enters the context, how long it stays there, and what breaks the prompt cache. Use when sessions feel expensive, when a long conversation is dragging unrelated files into every turn, when deciding between /clear, /compact, and /rewind, when choosing model and effort, when tool output or MCP definitions are crowding the context, or when working out whether a job belongs in a subagent. Efficiency here means spending tokens on the task you actually asked for, not spending fewer tokens overall.

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Build agent skills that improve themselves from human feedback, using a base (inner) skill that does the work and an improver (outer) skill that runs on a schedule, reads accumulated feedback, and proposes small reviewable edits to the base skill. Use when an agent's output is roughly right but noisy, when feedback keeps getting lost at the end of a session, when hand-editing prompts or AGENTS.md no longer scales, or when deciding how many improver loops to run and how to guard against bad feedback.

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Decide which model a workload should run on, and which cost controls to apply, using cost-per-outcome rather than token count as the metric. Use when rolling a product out to an organization, setting or enforcing a spend budget, reconciling spend against invoices, choosing between a frontier and a smaller model for a workload, or reducing the cost of a production API workload without losing quality where it matters. Covers the model family, enterprise admin controls (access gating, model controls, hard spend caps), usage observation tools, and API-side levers (prompt caching, batch processing, the effort parameter, and the advisor strategy).

uygnoey Skill

Guides building and deploying AI agents in financial services with emphasis on integration, regulation, and real-time risk controls. Trigger when the user asks how to safely operationalize agents for banking, insurance, wealth management, fraud, compliance, or financial operations.

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Decide whether to run agentic coding sessions on self-hosted infrastructure instead of the vendor-hosted default, then size and operate that environment. Use when network isolation, pre-installed internal toolchains, or keeping source and build artifacts on controlled infrastructure are hard requirements; when choosing between fixed and on-demand runner capacity; when clarifying the data boundary (what stays local vs. what is sent for inference); or when assigning ongoing ownership of runner images and orchestration to a platform, DevEx, or developer-productivity team.

uygnoey Bundle

Evaluate a new frontier model on your own private repositories instead of trusting public benchmarks, then decide which work to route to it. Use when a new model ships and you need to answer "is this better for us, and where do we actually use it" — building a private eval set, scoring quality/cost/speed on separate leaderboards, comparing head-to-head against your current default, measuring steps-to-solution and code that runs but is wrong, and setting a deployment posture around the model (harness-level safety, security testing against your own products, data-retention tradeoffs). Based on how JetBrains evaluated and deployed Claude Fable 5.

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Design AI training that increases learner agency and survives model releases. Use when writing internal AI enablement, an onboarding module, or a course and the draft keeps turning into a feature tour; when a program teaches tool operation but not which work should be delegated; when "safe use" has been reduced to prompt hygiene and omits task selection, skill preservation, and disclosure; or when you need an onboarding plus continuing-education structure rather than a one-time session.