「actors」の検索結果

State-first design pass — inventories and implements all non-happy states (loading, empty, error, partial, conflict, offline) before the happy path, and refactors impossible boolean state to proper state machines. Use when starting a new screen, reviewing an existing one for edge-case gaps, or when the user says "handle the error state" / "add loading states" / "what happens when data is missing". Invoke when the user asks for unhappy on their UI, or mentions 'unhappy' alongside design / UI / frontend work.

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How to write modern Swift well — modeling with value types, Swift 6 data-race safety and approachable concurrency (@concurrent, main-actor-by-default, actors, task groups), protocols and generics (some vs any), API design, performance and ARC, Swift Testing, macros, and the modern language features agents don't know about yet. Use when writing, reviewing, or migrating Swift. Only runs when explicitly invoked; it does not trigger on its own.

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Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.

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BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-database.

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Calculate code occupant loads by area with gross/net factors and jurisdiction checks. Use for "how many people can this space hold," IBC Table 1004.5, egress inputs, or occupancy-load reports; not for workplace headcount planning.

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Predict project completion dates using ML models. Forecast schedule delays based on current progress, historical patterns, and risk factors.

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Provide data-driven decision support for construction. Analyze multiple factors and recommend optimal project decisions.

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Calculate construction labor rates with overhead, benefits, and productivity factors. Regional rate databases and crew composition.

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Calculate carbon footprint of construction projects. Estimate CO2 emissions from materials, transportation, and construction processes using emission factors databases.

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Manage construction unit price databases: update prices, track vendors, apply location factors, maintain historical records. Essential for accurate estimating.

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Prequalify subcontractors based on safety, financial, and performance criteria.

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Generate material procurement lists from CWICR data. Calculate quantities with waste factors, group by supplier categories, and create purchase orders.

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Assist with quantity takeoff using CWICR data. Calculate quantities from dimensions, apply waste factors, and suggest related work items.

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Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.

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Estimate embodied carbon and produce ESG/climate reporting for construction: LCA per work item, material-based carbon factors, EU taxonomy and CSRD alignment. Use when a project needs carbon estimates or sustainability reporting.

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Analyze weather data impact on construction schedules. Predict weather delays, optimize work scheduling based on forecasts, and calculate weather-related risk factors for project planning.

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Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.

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Generates a Mermaid dependency graph showing import relationships between modules. Use when analyzing coupling, finding circular deps, or planning refactors.

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Builds estimates, quotes, and bids for trades and field-service businesses (HVAC, plumbing, electrical, roofing, landscaping, painting, cleaning, remodeling, general contracting, IT/AV installs). Turns a scope into line items with waste factors, burdened labor, materials, equipment, subs, permits, mobilization, contingency, overhead, profit, and tax, then writes a client-ready xlsx (live formulas) and PDF plus an internal cost sheet, with good/better/best options, add-ons, a payment schedule, exclusions, and assumptions. Use whenever someone asks for a quote, estimate, bid, proposal price, or job price, asks "how much should I charge for this job", wants to check a price or margin on a job, or is confused about markup vs margin. Computes deterministically with a script instead of mental math, because models routinely treat a 30% markup as a 30% margin and drop labor burden, waste, and mobilization.

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Create multiple choice, true/false, fill-in-blank, matching quizzes. Auto-generate plausible distractors. Instant grading with explanations.

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Use for any task that spans more than a couple of steps, files, or sessions — refactors, migrations, multi-file features, anything that must survive a context reset or crash. Trigger when the user asks for a plan, says "keep track of this", "don't lose the thread", or when a task is large enough that forgetting step four is likely.

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Get a local business found on Google -- audit and optimize the Google Business Profile, local pack ranking factors, review velocity, NAP consistency across directories, and location pages. For businesses whose customers search "near me," this outranks everything else in marketing.

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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

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Swift concurrency patterns — actors, structured concurrency, cancellation, async streams, GCD migration, strict-concurrency diagnostics, common bug patterns. Use when writing, reviewing, or debugging concurrent Swift code.

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