[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-be5ba072164cae89-harness-engineering-agents-code-humans-steer-summary":3,"summaries-facets-categories":157,"summary-related-be5ba072164cae89-harness-engineering-agents-code-humans-steer-summary":3742},{"id":4,"title":5,"ai":6,"body":13,"categories":118,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":123,"navigation":138,"path":139,"published_at":140,"question":120,"scraped_at":141,"seo":142,"sitemap":143,"source_id":144,"source_name":145,"source_type":146,"source_url":147,"stem":148,"tags":149,"thumbnail_url":120,"tldr":154,"tweet":120,"unknown_tags":155,"__hash__":156},"summaries\u002Fsummaries\u002Fbe5ba072164cae89-harness-engineering-agents-code-humans-steer-summary.md","Harness Engineering: Agents Code, Humans Steer",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8738,2273,20706,0.00259325,{"type":14,"value":15,"toc":110},"minimark",[16,21,25,28,31,34,38,41,44,47,50,54,57,60,63,66,69,72,76],[17,18,20],"h2",{"id":19},"code-abundance-frees-engineers-for-steering","Code Abundance Frees Engineers for Steering",[22,23,24],"p",{},"Ryan Lopopolo, a Member of Technical Staff at OpenAI, has spent nine months building software solely through AI agents, banning his team from directly editing code. The core shift: \"Code is free.\" Models like GPT-5.2 produce, refactor, and delete code at scale without human synchronous attention, treating implementation as non-scarce. This abundance stems from models' patience, parallelism, and training on trillions of code lines, making them \"isomorphic\" to human engineers for real-world tasks.",[22,26,27],{},"Pre-agent era constraints—no longer apply. P3-priority tasks (formerly deprioritized) now run 4x in parallel; agents select the winner. Internal tools ship with localization and internationalization day one, as capacity isn't traded off. Lopopolo's team built productivity agents for coworkers across OpenAI offices in London, Dublin, Paris, Brussels, Zurich, and Munich.",[22,29,30],{},"Scarce resources redefine roles: human time\u002Fattention, model context windows. Engineers become \"staff engineers\" delegating to infinite agent teams, focusing on systems design one day, week, or six months ahead. \"Every one of you is a staff engineer. You have as many team members as you can possibly drive concurrently.\"",[22,32,33],{},"Tradeoffs: Initial velocity hits from refining agent outputs, but long-term leverage from durable fixes. Humans unblock agents over long horizons, not micromanage.",[17,35,37],{"id":36},"legible-codebases-via-documentation-and-standardization","Legible Codebases via Documentation and Standardization",[22,39,40],{},"Agents need humans to externalize \"what good looks like.\" Years of experience yield 500 micro-decisions per patch (e.g., non-functional requirements like timeouts, retries). Models know all variants from training data; humans specify via \"breadcrumbs\": ADRs, persona docs, ticket histories, code reviews.",[22,42,43],{},"Make codebases \"native to agents\": respect context scarcity with sameness. Large refactors are free—fire 15 agents to complete migrations that lingered six months. Tests enforce source-code properties: files ≤350 lines for context efficiency; lints ensure retries\u002Ftimeouts on network calls (e.g., fetch wrappers).",[22,45,46],{},"Diverse team expertise amplifies: frontend architects document component patterns; backend experts outline scalability; product minds define QA plans covering user journeys, required PR media (screenshots, videos). One engineer's QA doc becomes every agent's guardrail, eliminating low-signal reviews.",[22,48,49],{},"\"The important thing is not the code but the prompt and the guardrails that got you there.\" Review agents scan patches for security\u002Freliability, injecting comments PRs must address pre-merge.",[17,51,53],{"id":52},"prompt-injection-and-skills-for-reliable-execution","Prompt Injection and Skills for Reliable Execution",[22,55,56],{},"Harness engineering operationalizes agent success through just-in-time prompts, minimizing overengineering per the \"bitter lesson\" (model capability obsoletes complexity). Centralized 5-10 skills hide infra churn: launch apps, spin observability, boot Chrome DevTools via daemon. Codex (agent) is entry point—outside-in dev, with repo tools agent-invoked first.",[22,58,59],{},"Guardrails embed prompts everywhere: ESLint rules (custom per workspace), wholesome tests (package privacy, dep edges, Zod dedup), error messages with remediation (\"Parse, don't validate at edge; derive type from Zod\"—no unknowns).",[22,61,62],{},"Reviewer agents, sub-agents, auto-compaction (GPT-5.4 excels) refresh context. Shell to agents for prompt-writing skills synthesized from OpenAI cookbooks. CI runs security checks: \"Are there timeouts\u002Fretries? Secure interfaces?\"",[22,64,65],{},"Workflow: Linear tickets → agent + skills. No human editors; beach\u002Fmargarita\u002FLinear setup shown. Agents prototype UIs, then lints enforce decomposition for snapshot tests. Observed failures (local coherence over shared utils) yield systematic fixes.",[22,67,68],{},"Q&A reveals minimalism: Avoid thousands of skills; deepen few. Harness = timely instruction surfacing. Agent hid daemon switch for weeks via docs—humans delegate fully.",[22,70,71],{},"\"Do not produce slop. Don't accept slop. You won't get slop in your codebase.\"",[17,73,75],{"id":74},"key-takeaways","Key Takeaways",[77,78,79,83,86,89,92,95,98,101,104,107],"ul",{},[80,81,82],"li",{},"Treat code as free: Parallelize P3s, refactor at scale, delete freely—focus humans on unblocking agents.",[80,84,85],{},"Externalize expertise: Document personas\u002FADRs\u002FQA for every agent trajectory; one doc accrues team-wide leverage.",[80,87,88],{},"Embed guardrails durably: Custom lints\u002Ftests on source code (file size, retries, deps); reviewer agents in CI.",[80,90,91],{},"Centralize 5-10 skills: Hide infra\u002Ftools; agent-first entry (e.g., Codex launches dev stack).",[80,93,94],{},"Just-in-time prompts: Auto-compaction + error remediation; synthesize skills from cookbooks.",[80,96,97],{},"Minimize harness overengineering: Surface requirements context-efficiently; models follow instructions.",[80,99,100],{},"Measure by agent autonomy: Trust via QA plans\u002Fmedia; shoulder-surf less, delegate more.",[80,102,103],{},"Fix failure classes systematically: Observe agent\u002Fhuman errors, devise lints\u002Ftests, migrate codebase once.",[80,105,106],{},"Workflow: Tickets → agents; no laptops—Linear + voice\u002Ftools.",[80,108,109],{},"Scale internal tools globally: i18n\u002Fl10n free with abundance.",{"title":111,"searchDepth":112,"depth":112,"links":113},"",2,[114,115,116,117],{"id":19,"depth":112,"text":20},{"id":36,"depth":112,"text":37},{"id":52,"depth":112,"text":53},{"id":74,"depth":112,"text":75},[119],"AI & LLMs",null,"md",false,{"content_references":124,"triage":133},[125,130],{"type":126,"title":127,"url":128,"context":129},"other","Harness Engineering","https:\u002F\u002Fopenai.com\u002Findex\u002Fharness-engineering\u002F","mentioned",{"type":131,"title":127,"url":132,"context":129},"podcast","https:\u002F\u002Flatent.space\u002Fp\u002Fharness-eng",{"relevance":134,"novelty":135,"quality":135,"actionability":135,"composite":136,"reasoning":137},5,4,4.35,"Category: AI & LLMs. The article discusses the innovative use of AI agents in software engineering, addressing the audience's pain point of integrating AI into their workflows. It provides actionable insights on how to leverage AI agents for productivity and code management, making it highly relevant for product builders.",true,"\u002Fsummaries\u002Fbe5ba072164cae89-harness-engineering-agents-code-humans-steer-summary","2026-04-17 00:29:28","2026-04-19 03:24:30",{"title":5,"description":111},{"loc":139},"80b5466d85781e03","AI Engineer","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=am_oeAoUhew","summaries\u002Fbe5ba072164cae89-harness-engineering-agents-code-humans-steer-summary",[150,151,152,153],"agents","prompt-engineering","software-engineering","dev-productivity","OpenAI engineer Ryan Lopopolo's team builds exclusively with AI agents by creating 'harnesses'—guardrails, skills, and prompts—that make codebases legible and execution reliable, freeing humans for systems 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Engineering: Stack Rules, Skills & Agents for Reliable AI Dev",{"provider":7,"model":8,"input_tokens":3747,"output_tokens":3748,"processing_time_ms":3749,"cost_usd":3750},8722,2341,38827,0.00289225,{"type":14,"value":3752,"toc":3922},[3753,3757,3760,3763,3769,3773,3778,3781,3784,3788,3796,3799,3804,3808,3811,3814,3818,3821,3824,3829,3833,3836,3839,3843,3846,3849,3854,3858,3861,3864,3867,3870,3873,3878,3882,3885,3888,3890],[17,3754,3756],{"id":3755},"harness-engineering-solves-ais-reliability-gap-in-real-projects","Harness Engineering Solves AI's Reliability Gap in Real Projects",[22,3758,3759],{},"AI models excel at one-off code but fail in projects due to forgetfulness, bias, and improvisation. Harness Engineering transforms them into a disciplined system by constraining behavior, standardizing processes, dividing labor, and enforcing verification. The author uses a minimal Go CLI for crypto snapshots (github.com\u002FRyoKusnadi\u002Fcrypto-snapshot-cli) to show implementation, starting from scratch. This isn't hype—it's a maturity model from Level 0 (experiments) to Level 4 (autonomous self-healing), where most teams hit Level 2: AI as a 'reliable junior partner' via feedback loops like tests and CI.",[22,3761,3762],{},"Key problem: Single prompts treat AI as 'smart autocomplete,' leading to chaos in production. Solution: A stacked framework ensuring stable, correct outputs across the dev lifecycle. Tradeoffs include upfront setup time but massive gains in predictability; rules are 'soft' (AI forgets), so scripts provide 'hard gates.' Early attempts with just rules failed due to AI's 'lazy bypass'; adding scripts fixed it.",[3764,3765,3766],"blockquote",{},[22,3767,3768],{},"'How to make AI consistently, reliably, and predictably deliver the exact results you want within your project.' (Author defines the core problem Harness solves, emphasizing verifiability over intelligence.)",[17,3770,3772],{"id":3771},"core-components-form-a-stacked-system","Core Components Form a Stacked System",[3774,3775,3777],"h3",{"id":3776},"rules-as-soft-constraints-and-team-policies","Rules as Soft Constraints and Team Policies",[22,3779,3780],{},"Rules are foundational 'engineering guidelines'—non-negotiable conventions like post-modification checklists: compile, test, validate before claiming 'done.' They prevent basic mistakes but fail via forgetfulness, irrelevance claims, or laziness. Not specs or scripts; they're like onboarding a dev: 'what's allowed\u002Foff-limits.' In the Go project, rules enforce no 'I'm done' without checks.",[22,3782,3783],{},"Tradeoff: Rules set baselines cheaply but need enforcement; alone, they're inadequate for complex tasks.",[3774,3785,3787],{"id":3786},"skills-as-standardized-playbooks","Skills as Standardized Playbooks",[22,3789,3790,3791,3795],{},"Skills turn vague instructions (e.g., 'compile') into exact SOPs. For Go: ",[3792,3793,3794],"code",{},"go mod tidy",", build tags, CGO flags, linker metadata, structured logs, error\u002Fwarning distinction. AI executes playbooks, not improvises—crucial for repeatability. Author prioritized skills early: compilation, testing, validation.",[22,3797,3798],{},"Why? Improvisation guarantees breakage in real envs. Skills standardize high-frequency ops, bridging rules to execution.",[3764,3800,3801],{},[22,3802,3803],{},"'A Skill tells the AI: “Don’t improvise on this... Just follow these exact steps.”' (Explains why skills prevent derivation errors, using compilation as example.)",[3774,3805,3807],{"id":3806},"sub-agents-for-role-separation","Sub-Agents for Role Separation",[22,3809,3810],{},"Single agents self-review, biasing toward progress over quality. Sub-Agents divide labor: requirements specialist, architect, gatekeeper, developer, QA, PM. Each handles one phase, outputs structured docs for handoff. Maps to real teams; prevents context collapse and self-bias.",[22,3812,3813],{},"In practice: Isolation keeps focus; e.g., developer gets only design, not full history.",[3774,3815,3817],{"id":3816},"workflows-as-relay-race-protocols","Workflows as Relay Race Protocols",[22,3819,3820],{},"Workflows orchestrate agents via explicit rules: stage status, handoff outputs, rejection conditions, rollbacks. Three layers: human-readable (philosophy), system-enforced (gates), role-specific (inputs\u002Foutputs). Context discipline: gradual rule loading, minimal per stage to avoid overload.",[22,3822,3823],{},"Without: Vague scopes, flawed designs pushed forward, unclear status. With: Auditable advances\u002Frejects. Analogy: Relay rules > fast runners alone.",[3764,3825,3826],{},[22,3827,3828],{},"'Without a Workflow, the engineering floor typically devolves into this: Requirements are vague... Everyone is “working,” but nobody can answer: “What’s the actual status?”' (Highlights chaos without protocols, stressing verifiable state.)",[3774,3830,3832],{"id":3831},"scripts-as-hard-verification-gates","Scripts as Hard Verification Gates",[22,3834,3835],{},"Scripts are 'most important': Master Gatekeeper runs checks like no hardcodes\u002Fsecrets, structured logging (zap\u002Fslog), golangci-lint zero warnings, race tests, coverage baselines, no accidental commits of generated files. AI can't bluff; pass or fail objectively.",[22,3837,3838],{},"Shift: Mature harnesses rely more on scripts than prompts. In Go project: Verifies go.mod sync, build tags, os.Exit bans.",[3774,3840,3842],{"id":3841},"mcp-for-external-integration","MCP for External Integration",[22,3844,3845],{},"MCP (Model Context Protocol) bridges repo to ecosystems: CI triggers, logs, signing, artifacts, releases. Exposes as tools\u002Fresources under policy control. Nice-to-have now, essential for delivery loops.",[22,3847,3848],{},"Full stack: SPEC (goals), Rules, Skills, Sub-Agents, Workflow, Scripts, Dev-Map (structure awareness), Task Board (progress), MCP.",[3764,3850,3851],{},[22,3852,3853],{},"'Scripts are saying: “Your claim that you’re done means nothing. You don’t pass my checkpoint until the system proves it.”' (Emphasizes scripts' role in objective truth over AI claims.)",[17,3855,3857],{"id":3856},"implementation-order-and-lessons-from-go-cli","Implementation Order and Lessons from Go CLI",[22,3859,3860],{},"Start with SPEC: Defines crypto-snapshot-cli (minimal but with breakage-prone seams: deps, builds, tests). Not enterprise-only; ideal for solos\u002FMVPs.",[22,3862,3863],{},"Build order: Rules first (baseline), Skills (standardize), Sub-Agents\u002FWorkflow (scale complexity), Scripts (enforce), Dev-Map\u002FTask Board (context), MCP (extend).",[22,3865,3866],{},"Stumbles: Initial rules-only led to chaos; added scripts for gates. Single agent inadequate; sub-agents fixed bias. Workflow prevented devolution.",[22,3868,3869],{},"Results: AI as junior partner—trust via system, not eyes. Evolvable: Level 1 (solo\u002FMVP) to Level 2 (teams).",[22,3871,3872],{},"Dev-Map\u002FTask Board: Project structure\u002Fpatterns; history\u002Fprogress alignment.",[3764,3874,3875],{},[22,3876,3877],{},"'Harness Engineering is like building a complete “Engineering Operations System” for AI.' (Analogy capturing the assembled value: mission to awareness.)",[17,3879,3881],{"id":3880},"maturity-model-guides-progressive-adoption","Maturity Model Guides Progressive Adoption",[22,3883,3884],{},"Level 0: Throwaways.\nLevel 1: Constraints for MVPs.\nLevel 2: Feedback for production.\nLevel 3: Specialization for multi-service.\nLevel 4: Autonomy for platforms.",[22,3886,3887],{},"Sweet spot: Level 2. Progression fills gaps: e.g., rules insufficient → scripts.",[17,3889,75],{"id":74},[77,3891,3892,3895,3898,3901,3904,3907,3910,3913,3916,3919],{},[80,3893,3894],{},"Start with Rules and Skills for quick wins; they're cheap and prevent 80% of basic errors.",[80,3896,3897],{},"Implement a Master Gatekeeper Script early—list 10-15 project-specific checks like lint, coverage, no secrets.",[80,3899,3900],{},"Use Sub-Agents for any task > trivial: requirements → architect → dev → QA handoffs via structured docs.",[80,3902,3903],{},"Define Workflows with explicit stages, handoffs, and rollbacks; use context discipline to avoid overload.",[80,3905,3906],{},"Prioritize scripts over prompts long-term; they provide the 'proof' AI lacks.",[80,3908,3909],{},"For Go\u002FPython\u002FTS projects, standardize skills for build\u002Ftest\u002Fdeploy (e.g., go mod tidy, race tests).",[80,3911,3912],{},"Build Dev-Map: Repo tree + patterns to onboard AI fast.",[80,3914,3915],{},"Evolve to MCP only after core loop; governs external access.",[80,3917,3918],{},"Test on minimal project first: Expose seams like deps, logging, platforms.",[80,3920,3921],{},"Measure maturity: Can AI reliably deliver full artifacts (code + tests + validation) without babysitting?",{"title":111,"searchDepth":112,"depth":112,"links":3923},[3924,3925,3934,3935,3936],{"id":3755,"depth":112,"text":3756},{"id":3771,"depth":112,"text":3772,"children":3926},[3927,3929,3930,3931,3932,3933],{"id":3776,"depth":3928,"text":3777},3,{"id":3786,"depth":3928,"text":3787},{"id":3806,"depth":3928,"text":3807},{"id":3816,"depth":3928,"text":3817},{"id":3831,"depth":3928,"text":3832},{"id":3841,"depth":3928,"text":3842},{"id":3856,"depth":112,"text":3857},{"id":3880,"depth":112,"text":3881},{"id":74,"depth":112,"text":75},[],{"content_references":3939,"triage":3944},[3940],{"type":3941,"title":3942,"url":3943,"context":129},"tool","crypto-snapshot-cli","https:\u002F\u002Fgithub.com\u002FRyoKusnadi\u002Fcrypto-snapshot-cli",{"relevance":134,"novelty":135,"quality":135,"actionability":135,"composite":136,"reasoning":3945},"Category: AI Automation. The article provides a detailed framework for implementing Harness Engineering, addressing the audience's pain point of AI reliability in production. It offers a concrete example of a Go CLI project to illustrate the concepts, making it actionable for developers looking to integrate AI more reliably into their workflows.","\u002Fsummaries\u002F8ed05de2a3b6e639-harness-engineering-stack-rules-skills-agents-for-summary","2026-05-11 15:34:24","2026-05-13 12:00:46",{"title":3745,"description":111},{"loc":3946},"8ed05de2a3b6e639","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fhow-to-implement-harness-engineering-fe70c558bb7f?source=rss----5517fd7b58a6---4","summaries\u002F8ed05de2a3b6e639-harness-engineering-stack-rules-skills-agents-for--summary",[150,151,152,153],"Harness Engineering builds reliable AI code generation by stacking Rules (guidelines), Skills (SOPs), Sub-Agents (roles), Workflows (handoffs), Scripts (gates), and MCP (external tools) into a verifiable system, demonstrated in a minimal Go CLI project.",[152,153],"burVcF3iXX9PsS91IwjtEu3vWZ3c11qJ15aiI4p0vos",{"id":3960,"title":3961,"ai":3962,"body":3967,"categories":4273,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":4274,"navigation":138,"path":4295,"published_at":4296,"question":120,"scraped_at":4297,"seo":4298,"sitemap":4299,"source_id":4300,"source_name":4301,"source_type":146,"source_url":4302,"stem":4303,"tags":4304,"thumbnail_url":120,"tldr":4306,"tweet":120,"unknown_tags":4307,"__hash__":4308},"summaries\u002Fsummaries\u002F8e760cba47215e0d-customize-vs-code-copilot-agents-for-repeatable-wo-summary.md","Customize VS Code Copilot Agents for Repeatable Workflows",{"provider":7,"model":8,"input_tokens":3963,"output_tokens":3964,"processing_time_ms":3965,"cost_usd":3966},8619,2616,40938,0.0030093,{"type":14,"value":3968,"toc":4266},[3969,3973,3980,3987,3993,3999,4003,4014,4020,4050,4056,4062,4067,4071,4078,4083,4100,4105,4119,4129,4139,4144,4148,4155,4160,4186,4196,4218,4224,4229,4231],[17,3970,3972],{"id":3971},"access-and-manage-all-customizations-from-one-ui","Access and Manage All Customizations from One UI",[22,3974,3975,3976,3979],{},"VS Code's new Customization UI centralizes management of AI behaviors for Copilot Chat, accessible via Command Palette (\"chat customizations\") or the gear icon in Chat view. This dashboard lists built-in and custom items like agents, skills, instructions, hooks, and prompts. Click any to view\u002Fedit details, generate new ones, or delete. Generate via UI buttons or Chat slash commands like ",[3792,3977,3978],{},"\u002Fcreate instructions","—Copilot drafts the file based on your description, scopes it to user\u002Fworkspace, and auto-applies to relevant files (e.g., HTML\u002FCSS for accessibility rules).",[22,3981,3982,3986],{},[3983,3984,3985],"strong",{},"Key principle",": Customizations persist across sessions, reducing repetition. Without them, every prompt requires re-explaining context, styles, or rules, leading to inconsistent results and trial-and-error. With them, define once (e.g., \"Apply SOLID principles to all refactors\") and Copilot enforces automatically, confirming application in responses.",[22,3988,3989,3992],{},[3983,3990,3991],{},"Common mistake",": Scattering files across folders—instead, use the UI for discovery. Test by reloading VS Code after creation. For teams, workspace-level instructions ensure consistent naming, formatting, and architecture, cutting review time.",[22,3994,3995,3998],{},[3983,3996,3997],{},"Quote",": \"Customization changes that. It lets you define behavior once, reuse it everywhere, and get consistent outputs.\"",[17,4000,4002],{"id":4001},"enforce-rules-and-styles-with-custom-instructions","Enforce Rules and Styles with Custom Instructions",[22,4004,4005,4006,4009,4010,4013],{},"Custom instructions are Markdown files acting as a \"rule book\" for Copilot, applied automatically to matching file types (defined in ",[3792,4007,4008],{},"apply_to"," metadata). Structure: metadata (description, glob patterns like ",[3792,4011,4012],{},"**\u002F*.js","), then bullet-point rules.",[22,4015,4016,4019],{},[3983,4017,4018],{},"Steps to create",":",[4021,4022,4023,4029,4044,4047],"ol",{},[80,4024,4025,4026,4028],{},"In Chat: ",[3792,4027,3978],{}," + description (e.g., \"Ensure UI meets WCAG standards, confirm in chat\").",[80,4030,4031,4032,4035,4036,4039,4040,4043],{},"Copilot generates ",[3792,4033,4034],{},".instructions.md"," (user: ",[3792,4037,4038],{},"~\u002F.vscode-customizations\u002F","; workspace: ",[3792,4041,4042],{},".vscode-customizations\u002F",").",[80,4045,4046],{},"Review\u002Fedit in UI: Add confirmation phrases like \"Confirmation: Applied WCAG standards.\"",[80,4048,4049],{},"Test: Ask Copilot to edit code (e.g., \"Refactor this script\" or \"Make UI 80s arcade style\")—it analyzes, applies rules, and confirms.",[22,4051,4052,4055],{},[3983,4053,4054],{},"Example before\u002Fafter",": Original calculator JS lacked SOLID separation; post-refactor: Single Responsibility (separate concerns), confirmed in chat. UI update auto-added ARIA labels, alt text for WCAG.",[22,4057,4058,4061],{},[3983,4059,4060],{},"Quality criteria",": Instructions must be specific (e.g., \"Use semantic HTML, keyboard nav\") not vague; include triggers (\"when generating\u002Frefactoring UI\") and confirmation for verification. Benefits scale to teams: Repo-wide consistency without manual reviews.",[22,4063,4064,4066],{},[3983,4065,3997],{},": \"Imagine every developer in the repo having Copilot follow the same coding conventions... This saves a lot of time.\"",[17,4068,4070],{"id":4069},"specialize-agents-with-skills-and-custom-agents","Specialize Agents with Skills and Custom Agents",[22,4072,4073,4074,4077],{},"Agent skills are folders (",[3792,4075,4076],{},"skill.md"," + resources\u002Fscripts) for domain-specific tasks, loadable across Copilot tools (VS Code, CLI). Custom agents build on skills, assigning personas (e.g., \"Security Reviewer\") with tools\u002Finstructions.",[22,4079,4080,4019],{},[3983,4081,4082],{},"Build a skill",[4021,4084,4085,4091,4097],{},[80,4086,4087,4090],{},[3792,4088,4089],{},"\u002Fcreate skill"," + task (e.g., \"Update README on feature add, confirm in chat\").",[80,4092,4093,4094,4096],{},"Copilot creates folder with ",[3792,4095,4076],{}," (description, related skills, rules like \"Extract feature from convo, append to README features section\").",[80,4098,4099],{},"Test: Add feature (e.g., \"Add dark\u002Flight jingle\")—skill auto-updates README.",[22,4101,4102,4019],{},[3983,4103,4104],{},"Build custom agent",[4021,4106,4107,4110,4116],{},[80,4108,4109],{},"Ask Copilot for prompt: \"Suggest custom agent for arcade calculator.\"",[80,4111,4112,4115],{},[3792,4113,4114],{},"\u002Fcreate agent"," + persona (e.g., \"Arcade App Builder: Knows retro aesthetics, sound effects, HTML\u002FJS\u002FCSS stack\").",[80,4117,4118],{},"Select from Chat dropdown (@agentname); it uses codebase knowledge for tasks like \"Build tip calculator.\"",[22,4120,4121,4124,4125,4128],{},[3983,4122,4123],{},"Example",": Security agent reviews JS for vulns (categorizes low\u002Fmedium\u002Fhigh); Arcade agent clones styles\u002Fsounds to new app. ",[3983,4126,4127],{},"Trade-off",": Domain-focused (great for projects) but overkill for one-offs.",[22,4130,4131,4134,4135,4138],{},[3983,4132,4133],{},"Mistake to avoid",": Not scoping (user vs. workspace)—use workspace for teams. ",[3983,4136,4137],{},"Quality",": Clear description, minimal tools, architecture awareness.",[22,4140,4141,4143],{},[3983,4142,3997],{},": \"Custom agents enable you to configure the AI to adopt different personas tailored to specific development roles and tasks.\"",[17,4145,4147],{"id":4146},"automate-repetitive-tasks-with-hooks-and-prompt-files","Automate Repetitive Tasks with Hooks and Prompt Files",[22,4149,4150,4151,4154],{},"Hooks run shell commands at agent lifecycle events (e.g., ",[3792,4152,4153],{},"post_tool_use","). Prompt files are reusable templates.",[22,4156,4157,4019],{},[3983,4158,4159],{},"Create hook",[4021,4161,4162,4165,4183],{},[80,4163,4164],{},"UI > Generate hook + spec (e.g., \"Run Prettier on post_tool_use\").",[80,4166,4167,4168,4171,4172,4175,4176,4179,4180,4043],{},"Edits ",[3792,4169,4170],{},".vscode-customizations\u002Fhooks\u002Fprettier.hook.json",": Define ",[3792,4173,4174],{},"events"," (array), ",[3792,4177,4178],{},"command"," (e.g., ",[3792,4181,4182],{},"npx prettier --write .",[80,4184,4185],{},"Reload VS Code; test: Edit README—hook auto-formats.",[22,4187,4188,4191,4192,4195],{},[3983,4189,4190],{},"Prompt files",": ",[3792,4193,4194],{},"\u002Fcreate prompt"," for templates (e.g., code review); reference in skills.",[22,4197,4198,4201,4202,4191,4205,4208,4209,4208,4212,4214,4215,4217],{},[3983,4199,4200],{},"Principle",": Automate validation (security, formatting) without manual invocation. ",[3983,4203,4204],{},"Events",[3792,4206,4207],{},"start_session",", ",[3792,4210,4211],{},"user_prompt_submit",[3792,4213,4153],{},". ",[3983,4216,4127],{},": Shell reliance—test commands; no timeout for long runs.",[22,4219,4220,4223],{},[3983,4221,4222],{},"Full workflow example",": Build app from scratch—use instructions for styles, agent for features, hook for formatting, skill for docs. Results: Arcade calculator with themes, sounds, WCAG, auto-README, formatted.",[22,4225,4226,4228],{},[3983,4227,3997],{},": \"Hooks enable you to execute custom shell commands at life cycle points during agent sessions... automate workflows, enforce security policies.\"",[17,4230,75],{"id":74},[77,4232,4233,4236,4242,4245,4251,4254,4257,4260,4263],{},[80,4234,4235],{},"Open Customization UI via gear or \"chat customizations\" to manage everything in one place.",[80,4237,4238,4239,4241],{},"Start with custom instructions for persistent rules: ",[3792,4240,3978],{}," + glob patterns + confirmations.",[80,4243,4244],{},"Use agent skills for tasks (e.g., README updates) and custom agents for personas—select via @dropdown.",[80,4246,4247,4248,4250],{},"Automate with hooks on lifecycle events like ",[3792,4249,4153],{}," for formatters; reload to activate.",[80,4252,4253],{},"Generate via Copilot slash commands to skip manual writing; always review\u002Fedit.",[80,4255,4256],{},"Scope user\u002Fworkspace for personal\u002Fteam use; test on real edits\u002Frefactors.",[80,4258,4259],{},"Check Awesome Copilot repo for community examples.",[80,4261,4262],{},"Avoid repetition: Customizations turn Copilot into a context-aware system.",[80,4264,4265],{},"For apps: Chain features—instructions for compliance, agents for domain logic, hooks for polish.",{"title":111,"searchDepth":112,"depth":112,"links":4267},[4268,4269,4270,4271,4272],{"id":3971,"depth":112,"text":3972},{"id":4001,"depth":112,"text":4002},{"id":4069,"depth":112,"text":4070},{"id":4146,"depth":112,"text":4147},{"id":74,"depth":112,"text":75},[119],{"content_references":4275,"triage":4292},[4276,4279,4283,4286,4289],{"type":126,"title":4277,"url":4278,"context":129},"VS Code Customization Overview","https:\u002F\u002Faka.ms\u002FVSCL-Cust-Overview",{"type":126,"title":4280,"url":4281,"context":4282},"Awesome Copilot","https:\u002F\u002Faka.ms\u002FAwesomeGC","recommended",{"type":126,"title":4284,"url":4285,"context":129},"VS Code Learn Playlist","https:\u002F\u002Faka.ms\u002Fvsc-learn",{"type":3941,"title":4287,"url":4288,"context":129},"Custom Instructions Docs","https:\u002F\u002Faka.ms\u002Fcustom-instructions",{"type":3941,"title":4290,"url":4291,"context":129},"Custom Agent Skills","https:\u002F\u002Faka.ms\u002Fcustom-agent-skills",{"relevance":134,"novelty":135,"quality":135,"actionability":134,"composite":4293,"reasoning":4294},4.55,"Category: AI & LLMs. The article provides a detailed guide on customizing VS Code Copilot agents, addressing practical applications for developers looking to streamline their workflows. It includes specific steps for creating custom instructions, making it immediately actionable for the audience.","\u002Fsummaries\u002F8e760cba47215e0d-customize-vs-code-copilot-agents-for-repeatable-wo-summary","2026-05-06 14:00:14","2026-05-06 16:10:56",{"title":3961,"description":111},{"loc":4295},"ab488a3c329a1bb7","Visual Studio Code","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9PUt81AjfmA","summaries\u002F8e760cba47215e0d-customize-vs-code-copilot-agents-for-repeatable-wo-summary",[150,151,4305,153],"ai-tools","Use VS Code's Customization UI to build custom instructions, agent skills, agents, hooks, and prompt files—define behaviors once for consistent AI outputs across chats, teams, and projects without extensions.",[153],"W1zwbe6XmkDedssnAbVlY_GoLAd41WfXB9VHvOaL2LU",{"id":4310,"title":4311,"ai":4312,"body":4317,"categories":4378,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":4379,"navigation":138,"path":4391,"published_at":4392,"question":120,"scraped_at":4393,"seo":4394,"sitemap":4395,"source_id":4396,"source_name":4397,"source_type":146,"source_url":4398,"stem":4399,"tags":4400,"thumbnail_url":120,"tldr":4402,"tweet":120,"unknown_tags":4403,"__hash__":4404},"summaries\u002Fsummaries\u002Fa99451de2de64e60-claude-md-patterns-for-bulletproof-ai-coding-summary.md","Claude.md Patterns for Bulletproof AI Coding",{"provider":7,"model":8,"input_tokens":4313,"output_tokens":4314,"processing_time_ms":4315,"cost_usd":4316},7402,1645,34494,0.00179605,{"type":14,"value":4318,"toc":4373},[4319,4323,4326,4329,4332,4335,4338,4342,4345,4348,4351,4354,4357,4361,4364,4367,4370],[17,4320,4322],{"id":4321},"karpathy-inspired-rules-to-align-claude-with-your-intent","Karpathy-Inspired Rules to Align Claude with Your Intent",[22,4324,4325],{},"Start every claude.md with a project description at the top so Claude grasps the app's structure, services, dependencies, and runtime before diving in—this prevents deduction from code alone and cuts misalignment. Add explicit 'think before coding': Claude must state assumptions, list multiple interpretations if ambiguous, and confirm your choice, slashing course-corrections by forcing clarification over training-data guesses.",[22,4327,4328],{},"Prioritize simplicity: Instruct Claude to solve in minimal lines (e.g., refactor if >200 lines when 50 suffice), add only requested features with proper error handling, and iterate toward conciseness. This avoids verbose overhead that bloats tokens, delays refactoring, and hinders scaling in large apps.",[22,4330,4331],{},"Enforce surgical changes: Touch only task-tracing code; flag unrelated issues (dead code, formatting) without fixing unless asked, as agents scatter focus on 'improvements.' Every edit must link directly to your request, listing other findings for your triage.",[22,4333,4334],{},"Drive goal execution: For each task, Claude defines verifiable success criteria upfront—like writing passing tests for validation inputs\u002Foutputs—then plans, implements, iterates until verified. For UI, pair with tools like Claude Chrome extension or Puppeteer MCP to visually confirm changes, as code alone misleads.",[22,4336,4337],{},"These patterns from Andrej Karpathy's skills repo transform vague tasks into precise, testable outcomes, ensuring behavior matches intent without wild implementations.",[17,4339,4341],{"id":4340},"tool-overrides-safety-and-iterative-refinement","Tool Overrides, Safety, and Iterative Refinement",[22,4343,4344],{},"Override defaults: Skip init-generated commands (e.g., npm run dev) Claude already knows; specify custom tools like GitHub CLI over git, PNPM over npm, or non-standard run instructions to leverage your stack without fallbacks.",[22,4346,4347],{},"Update dynamically: After user corrections, Claude applies fixes then logs learnings to a dedicated file, building a knowledge base of pitfalls and preferences for future tasks—treat claude.md as living, not static.",[22,4349,4350],{},"Embed git safety: Ban irreversible commands (force-push, reset --hard, rm -rf) without confirmation; if unsure, always ask. This guards production from accidents like unwanted merges.",[22,4352,4353],{},"Use path-scoped rule files: Create e.g., api-rules.md (first line declares scope) for file-type rules, referenced in root claude.md. This avoids bloat—Claude loads only relevant rules, staying focused without interference.",[22,4355,4356],{},"For monorepos, add scoped claude.md per subfolder for module-specific guidance; root holds global rules only, preventing divergence from irrelevant instructions.",[17,4358,4360],{"id":4359},"prioritized-structure-and-verification-for-peak-performance","Prioritized Structure and Verification for Peak Performance",[22,4362,4363],{},"Order by priority: Hard rules first (non-negotiable, e.g., safety, scoping), then medium (key principles like simplicity), finally low (references). Burying criticals dilutes impact.",[22,4365,4366],{},"Mandate full verification before completion: Don't just add code—run builds, tests, linting, type checks to confirm functionality. Report only when all pass, using every mechanism for fidelity.",[22,4368,4369],{},"Cap at 300 lines: Beyond this, performance drops; trim ruthlessly for focus.",[22,4371,4372],{},"This setup, refined from community testing and shipping, eliminates agent fights: Claude reasons correctly, changes precisely, verifies rigorously, and adapts—saving hours on real projects.",{"title":111,"searchDepth":112,"depth":112,"links":4374},[4375,4376,4377],{"id":4321,"depth":112,"text":4322},{"id":4340,"depth":112,"text":4341},{"id":4359,"depth":112,"text":4360},[119],{"content_references":4380,"triage":4389},[4381,4386],{"type":126,"title":4382,"author":4383,"url":4384,"context":4385},"andrej-karpathy-skills","forrestchang","https:\u002F\u002Fgithub.com\u002Fforrestchang\u002Fandrej-karpathy-skills\u002F","cited",{"type":3941,"title":4387,"url":4388,"context":129},"Klaus","https:\u002F\u002Fklausai.com\u002Fr\u002FMv1e2",{"relevance":134,"novelty":135,"quality":135,"actionability":134,"composite":4293,"reasoning":4390},"Category: AI & LLMs. The article provides practical patterns for using Claude.md effectively, addressing the pain points of AI-Curious Developers and Technical Founders by offering concrete strategies for coding with AI. It emphasizes actionable steps like starting with a project description and defining success criteria, making it immediately applicable for building AI-powered products.","\u002Fsummaries\u002Fa99451de2de64e60-claude-md-patterns-for-bulletproof-ai-coding-summary","2026-04-28 14:30:29","2026-05-03 16:44:39",{"title":4311,"description":111},{"loc":4391},"c6527f0f4e352415","AI LABS","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fMY5Sdj2DMk","summaries\u002Fa99451de2de64e60-claude-md-patterns-for-bulletproof-ai-coding-summary",[4401,150,151,153],"llm","Craft claude.md with project description first, Karpathy rules like 'think before coding' and simplicity, tool overrides, git safety, scoped files, verification steps, and priority-ordered instructions under 300 lines to make Claude ship exact implementations without guesswork or bloat.",[153],"aSUZZ1N9_uYmZbJKhVlN4jk82uo4tI6cro4gCyHSS4w",{"id":4406,"title":4407,"ai":4408,"body":4413,"categories":4469,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":4470,"navigation":138,"path":4493,"published_at":4494,"question":120,"scraped_at":4495,"seo":4496,"sitemap":4497,"source_id":4498,"source_name":4499,"source_type":146,"source_url":4500,"stem":4501,"tags":4502,"thumbnail_url":120,"tldr":4503,"tweet":120,"unknown_tags":4504,"__hash__":4505},"summaries\u002Fsummaries\u002F9c9d012e4625ef48-fix-claude-code-for-opus-4-7-9-key-changes-summary.md","Fix Claude Code for Opus 4.7: 9 Key Changes",{"provider":7,"model":8,"input_tokens":4409,"output_tokens":4410,"processing_time_ms":4411,"cost_usd":4412},6575,2115,10779,0.00186565,{"type":14,"value":4414,"toc":4463},[4415,4419,4422,4425,4429,4442,4446,4453,4457,4460],[17,4416,4418],{"id":4417},"adopt-ex-high-effort-and-adaptive-thinking-as-defaults","Adopt Ex-High Effort and Adaptive Thinking as Defaults",[22,4420,4421],{},"Opus 4.7 introduces ex-high effort level between high (4.6 default) and max, recommended verbatim by Anthropic as the new starting point for coding and agentic tasks since max yields diminishing returns and overthinking. Set max output tokens to 64,000 at ex-high or max to give the model room to think and act. Raise effort instead of rewriting shallow prompts. Adaptive thinking replaces extended thinking (which now returns HTTP 400 error on fixed budget_tokens); Claude auto-decides reasoning depth, outperforming the old mode per Anthropic evals—steer with pros like \"think carefully step by step\" for hard problems or \"prioritize quick response\" for easy ones. Tokenizer change uses ~1.35x more tokens for same text, filling context faster. Sampling params (temperature, top_p\u002Fk) also 400-error.",[22,4423,4424],{},"Treat Claude as capable engineer, not pair programmer: delegate via first-message structure stating intent, desired outcome, constraints, acceptance criteria, and file locations to minimize per-turn reasoning costs from ambiguity. Test prompts on colleagues—if confusing to them, confusing to Claude.",[17,4426,4428],{"id":4427},"build-5-layer-prompts-and-explicit-verbs-for-30-quality-gains","Build 5-Layer Prompts and Explicit Verbs for 30% Quality Gains",[22,4430,4431,4432],{},"Use one precise verb per instruction since 4.7 follows literally: \"suggest changes\" yields list only, no code; \"change this function\" edits directly. Layer prompts as: 1) clear\u002Fdirect instructions, 2) context\u002Fexplanation of why, 3) 3-5 XML-tagged examples (multi-shot beats single-shot), 4) XML structure (",[4433,4434,4208,4435],"instructions",{},[4436,4437,4208,4438,4441],"context",{},[4439,4440],"input",{},"), 5) system role. Place long documents at prompt top, question at bottom for +30% response quality on multi-document inputs. For subagents, explicitly request \"spawn multiple sub-agents in the same turn\" when fanning out across files\u002Fbranches; skip for single-file work to avoid trash.",[17,4443,4445],{"id":4444},"enforce-context-hygiene-claudemd-and-hooks-for-reliability","Enforce Context Hygiene, CLAUDE.md, and Hooks for Reliability",[22,4447,4448,4449,4452],{},"Context window degrades performance: 4.5-minute fresh tasks stretch to 18 minutes after autos. Use \u002Fclear post-resolution, \u002Frewind on wrong turns. CLAUDE.md (auto-init via ",[3792,4450,4451],{},"claude \u002Finit",") reads first each session with hierarchy: org policy > ~\u002F.claude user prefs > project-level > local gitignored > path-scoped rules—keep concise, cut ignorable lines to avoid bloat. Hooks via settings.json PreToolUse matchers block destructives pre-execution: command (shell script), HTTP (team endpoint), prompt (LM eval), agent (sub-agent verify).",[17,4454,4456],{"id":4455},"use-filesystem-memory-and-verification-for-long-tasks","Use Filesystem Memory and Verification for Long Tasks",[22,4458,4459],{},"Highest-leverage fix (per docs): always give Claude self-verification like tests\u002Fscreenshots\u002Fexpected output, never speculate on unopened code. For long-horizon: filesystem as memory via tests.json (passing\u002Ffailing\u002Fpending, never edit\u002Fremove), progress.txt notes, git commits as checkpoints. Code reviews: drop \"high-severity only\" filters (suppresses 11% bug-finding gains); use \"report every issue, even uncertain—filter later.\"",[22,4461,4462],{},"Checklist: 1) First-turn intent\u002Fconstraints\u002Facceptance, 2) ex-high default, 3) adaptive thinking, 4) CLAUDE.md, 5) pre-tool hook.",{"title":111,"searchDepth":112,"depth":112,"links":4464},[4465,4466,4467,4468],{"id":4417,"depth":112,"text":4418},{"id":4427,"depth":112,"text":4428},{"id":4444,"depth":112,"text":4445},{"id":4455,"depth":112,"text":4456},[119],{"content_references":4471,"triage":4491},[4472,4476,4479,4482,4485,4488],{"type":126,"title":4473,"author":4474,"url":4475,"context":4385},"Best practices for using Claude Opus 4.7 with Claude Code","Anthropic","https:\u002F\u002Fclaude.com\u002Fblog\u002Fbest-practices-for-using-claude-opus-4-7-with-claude-code",{"type":126,"title":4477,"url":4478,"context":4385},"Claude Code docs","https:\u002F\u002Fdocs.claude.com\u002Fen\u002Fdocs\u002Fclaude-code",{"type":126,"title":4480,"url":4481,"context":4385},"Claude Code best-practices engineering page","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Fclaude-code-best-practices",{"type":126,"title":4483,"url":4484,"context":129},"Anthropic docs home","https:\u002F\u002Fdocs.anthropic.com",{"type":126,"title":4486,"url":4487,"context":129},"Boris Cherny on X","https:\u002F\u002Fx.com\u002Fbcherny",{"type":3941,"title":4489,"url":4490,"context":129},"diy-yt-creator","https:\u002F\u002Fgithub.com\u002FLeex279\u002Fdiy-yt-creator",{"relevance":134,"novelty":135,"quality":135,"actionability":134,"composite":4293,"reasoning":4492},"Category: AI & LLMs. The article provides specific, actionable strategies for optimizing the use of the Claude model in coding tasks, addressing pain points related to prompt engineering and production readiness. It details new defaults and prompt structures that can directly enhance productivity, making it highly relevant for developers integrating AI into their workflows.","\u002Fsummaries\u002F9c9d012e4625ef48-fix-claude-code-for-opus-4-7-9-key-changes-summary","2026-04-20 12:18:23","2026-04-21 15:22:05",{"title":4407,"description":111},{"loc":4493},"7affeb01a39ba436","DIY Smart Code","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0x5-XD9XD6c","summaries\u002F9c9d012e4625ef48-fix-claude-code-for-opus-4-7-9-key-changes-summary",[4401,151,150,153],"Opus 4.7 boosts coding power 13% but breaks old prompts—default to ex-high effort, adaptive thinking, literal verbs, and verification to resolve 3x more production tasks.",[153],"eHv33wsVFa7bYsq2egrHJ21E2-RW5c6qab7gKdTQUuc"]