Results for “bounded”

MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".

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Experimental workflow for summarizing work that still needs human judgment across configured pull requests, issues, feedback, errors, and delivery signals. Use for a human decision queue or bounded review.

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Set up a bounded iterate-until-done run with the loop plugin's /loop command. Use when the user wants the same task retried or refined across several replies until a check passes, such as "keep going until the tests pass" or "do this up to 5 times".

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Break a large, parallelizable goal into bounded work units, coordinate existing agent/worktree machinery, integrate, and verify. Explicit-only.

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Use when independent bounded tasks benefit from delegation during implementation

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Create a PostHog endpoint with the right shape on the first try — covers query kind choice, name conventions, what to expose as variables (HogQL code_name vs insight breakdown), data_freshness_seconds, and whether to materialise on day one. Use when the user says "create an endpoint", "expose this query as an API", "turn this insight into an endpoint", or asks for help structuring a new endpoint. Steers away from common mistakes: materialising a query with cohort breakdowns or compare mode, inline-only variables on a materialised endpoint, unbounded date ranges, ambiguous names.

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Design what an employer will have a candidate do during an Otameshi Tenshoku trial: turn a business problem into a bounded paid assignment or an unpaid learning visit, with scope, completion criteria, candidate hours, company staff hours, duration, compensation and change conditions, and check the planned cost against the budget. Use when a hiring manager, recruiter or small-business owner wants to decide what work to offer, how much time it takes, what it costs, or how to adjust a plan when materials are late or the scope grows; do not use to write the public listing (trial-listing-draft), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post, contact or contract on the user's behalf.

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Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.

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Deliver a small bounded change inside an initialized SDD Composy workspace through the quick path: at most five implementation files, a registered TASK, TDD, evidence, trace, and a verified verdict, escalating otherwise. Use for a precise objective with no existing task — 'ajuste rápido via SDD', 'quick fix under SDD', 'small change to X in the SDD flow'. Do NOT use for an already registered task (sdd-execute), for architecture, migrations, or destructive work, or in repositories without SDD Composy (other plugins' quick).

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Run a bounded, provider-neutral SDD Composy correction loop on one approved task (EXECUTE, QA, EVIDENCE, REVIEW, VERIFY; at most 3 iterations) with explicit approval, safe-stop guards, and fresh verification. Use to continue an approved TASK autonomously — 'rodar o loop na TASK-003', 'continuar a task até passar', 'start a loop on task 4'. Do NOT use for the host's recurring loop runner, for fleets (sdd-fleet), or for a task without human approval.

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Design bounded performance profiles with explicit load authorization, contextual samples, percentiles, and agreed thresholds.

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Design authorized, bounded, reproducible security checks without turning scanner findings into penetration-test permission.

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Delegate a bounded repository task to an allowlisted external coding CLI through the guarded Flow runner. Use for automatic provider selection or explicit Codex, OpenCode, Kimi, Gemini, or Kiro delegation that requires timeout handling and independent local review.

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Deliver a small, bounded code change through inspection, a mini-plan, implementation, testing, review, and adversarial verification. Use for bug fixes, configuration changes, and features expected to touch no more than five files.

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Use when the user wants a small, well-bounded change done now without a plan file — a bugfix, config change, rename, doc update or simple refactor touching 1–5 files — 'corrigir rapidinho', 'quick fix no X', 'renomear Y'. Presents a mini-plan, implements after approval, verifies and commits. Do NOT use for more than 5 files, architectural decisions, migrations or new UI flows (use feat-feature / feat-backend / feat-frontend).

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Design, critique, set, create, activate, or run durable Codex goals for persistent or long-running objectives. Use when the user says "set a goal", "start a goal", "activate goal mode", "persistent goal", "long-running objective", "goal tree", or asks for a goal with verifiers, durable state, approval gates, completion proof, bounded delegation, or parent/child subagent goals.

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Load bounded working context at the start of a coding session. FIRES ON EVERY FIRST USER MESSAGE in a fresh session, regardless of content — including bare greetings ("hi", "hello", "hey"), task-only openers, or "let's start". Also triggers on: "fresh session", "session start", "first message in a new thread", "new chat", "cold start", "begin work in this repo", "starting work", "continue from prior session", "what were we working on", "resume", "what happened last time", "load memory", "recall context", "create a handoff", "prior session context", "latest handoff", "current state", "decision context". Skip ONLY if the user explicitly says "fresh start" or "ignore prior context". Self no-ops if invoked mid-session when context is already loaded.

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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Interpret and design bounded FIPA communicative-act exchanges using XC00037H semantics while keeping delivery, authority, and external effects separate. NOT for proving delivery, task authorization, or external effect completion.

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Designs and audits data retention and file compaction for an embedded SQLite datastore in a long-lived service — a unified retention registry (one declared policy per table), TTL / absolute-age / row-count-cap policies, a fail-loud coverage guard, WAL/checkpoint tuning, and auto_vacuum/incremental-vacuum so pruning actually shrinks the file. Use when a SQLite DB grows unbounded, a table has an index but no DELETE, a pruned DB never shrinks on disk, metrics are stored as raw events forever, or a new table needs bounding. Keywords retention policy, TTL, reaper, prune, VACUUM, auto_vacuum, incremental_vacuum, WAL checkpoint, freelist, DB bloat, row cap, per-tenant quota. NOT for Postgres/MySQL/server-DB partitioning or TTL, log-line rate-limiting/sampling, backup/replication, schema migrations, or query-performance indexing.

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Synthesizes evidence-linked feedback from validation results, confidence evidence, and iteration triggers. Proposes bounded revision guidance for a stated acceptance failure. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).

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Specifies bounded DAG execution contracts and evaluates executor receipts for dispatch, cancellation, joins, and effect reconciliation. Use when an implementation must bind a planned DAG to named runtime controls. NOT for claiming an executor is active, spawning agents, or certifying an effect from a plan.

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