「correction」の検索結果

Use when the user asks to "plan an influencer campaign", "build a campaign blueprint", "track or close a creator campaign", or "record a late campaign correction"; produces the plan and, when requested, a non-canonical evidence tracker with scoped identity, publication, reconciliation, close, and reopen receipts. Not for individual creator briefs — use brief-generator; not for overall product launches without creators — use launch-tier-planner; not for sending, publishing, amplifying, or paying — use the owning execution workflow. 达人营销策划/种草方案/活动追踪与关账

日本語の概要は準備中です。原文の説明を表示しています。

Show session analytics, learning patterns, correction trends, heatmaps, and productivity metrics. Computes stats from project memory and session history. Use when asking for stats, statistics, progress, how am I doing, coding history, or dashboard.

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Surface past learnings relevant to the current task before starting work. Searches correction history, recalls past mistakes, and applies prior patterns. Use when starting a task, saying "what do I know about", "previous mistakes", "lessons learned", or "remind me about".

日本語の概要は準備中です。原文の説明を表示しています。

Provides verified corrections for IAM behaviors that AI agents frequently get wrong — policy evaluation edge cases, trust policy gotchas, STS session limits, Organizations quirks, and SAML/MFA specifics. Also provides structured workflows for IAM role management and baseline policy generation from application source code or a Terraform plan JSON. Covers condition operator safety (ForAnyValue/ForAllValues with Null checks), bucket policy deny patterns (VPC endpoint restrictions, org paths), confused deputy protection, and service role creation for AWS services (Glue, CloudTrail, Lambda, ECS, etc.) with aws:SourceAccount/aws:SourceArn trust conditions. Applies when creating IAM roles, writing IAM or bucket policies, generating policies from application source code or a Terraform plan JSON, working with STS, Organizations, or condition operators, or any task needing a service or execution role. Does not cover non-IAM authorization like Cognito user-pool policies or app-level RBAC.

日本語の概要は準備中です。原文の説明を表示しています。

Auto-extract patterns from coding sessions, track corrections, and build reusable knowledge with confidence scoring

日本語の概要は準備中です。原文の説明を表示しています。

Runs the pay cycle so it is right, on time, and provable — the calendar and cutoffs, what feeds pay from the HRIS and time systems, gross-to-net and the deductions in it, multi-jurisdiction registration and tax filing, off-cycle payments and corrections, and the reconciliation to the general ledger. Use this to design or audit a payroll process, work out what happens when someone is paid wrong, or understand what changes when you hire in a new state or country.

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Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

日本語の概要は準備中です。原文の説明を表示しています。

Use when correcting batch effects in merged bulk expression matrices with sample-level batch metadata while preserving biological group structure and generating before-and-after QC plots. NOT for: single-cell integration, raw FASTQ processing, differential expression without batch labels, or datasets without biological groups.

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Use when normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.

日本語の概要は準備中です。原文の説明を表示しています。

Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.

日本語の概要は準備中です。原文の説明を表示しています。

Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature, at most one PR of proven corrections. Use for /maintain-verification-skill or "audit the verify skill".

日本語の概要は準備中です。原文の説明を表示しています。

Autonomously engage with complex coding tasks — multi-step planning, tool use, self-correction, and solution verification without human intervention.

日本語の概要は準備中です。原文の説明を表示しています。

Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

日本語の概要は準備中です。原文の説明を表示しています。

Draft and review contributions to existing MEX project knowledge. Use when the user asks to capture a discussion or decision in project knowledge, propose an addition or correction for team review, or explicitly invokes /mex-inbox or $mex-inbox. Also supports existing Spec proposals. Do not activate for brainstorming alone, routine GROW upkeep, email inboxes, session logs, or handoffs.

日本語の概要は準備中です。原文の説明を表示しています。

Draft and review contributions to existing MEX project knowledge in the repo wiki. Use when the user wants to capture a team decision, convention, or architecture note, propose an addition or correction for team review, or invokes /mex-inbox or $mex-inbox. Also supports existing Spec proposals. Keywords: mex, project memory, CLAUDE.md, team memory, drift, inbox. Do not activate for brainstorming alone, routine GROW upkeep, email inboxes, session logs, or handoffs.

日本語の概要は準備中です。原文の説明を表示しています。

Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.

日本語の概要は準備中です。原文の説明を表示しています。

Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature, at most one PR of proven corrections. Use for /maintain-verification-skill or "audit the verify skill".

日本語の概要は準備中です。原文の説明を表示しています。

Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs (DiD/event-study, IV, panel). Use when user says "power analysis", "power calculation", "MDE", "minimum detectable effect", "how big a sample do I need", "is my study powered", "power for an RCT", or when /preregister needs a power section for an experiment. Produces a power/MDE table, power curves, and a methods paragraph to paste into a preregistration.

日本語の概要は準備中です。原文の説明を表示しています。

Create, improve, refactor, replace, review, or validate prompts, instructions, agents, subagents, and skills. Use for Copilot customization cleanup, extending HVE workflows with project-specific capabilities, and parent-owned correction of material review findings.

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Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with unit tests per `write-code-eval`).

日本語の概要は準備中です。原文の説明を表示しています。

Routes audio: StepFun ASR/语音识别, StepFun TTS/配音, transcript/妙记→会议纪要, merge/review minutes. Reads one bundled specialist; generic ASR and correction stay direct.

日本語の概要は準備中です。原文の説明を表示しています。

Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation. Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents. Supports AI model specs, technical documentation, statistics, and general factual statements.

日本語の概要は準備中です。原文の説明を表示しています。

Trigger: RDD, receipt-driven development, review authority, receipt/lineage, correction/recovery, delivery gate/kill switch, bounded review defects. Guide work.

日本語の概要は準備中です。原文の説明を表示しています。

Autonomous development with experimental self-correction. Iterates through quality gates until antifragile or blocked.

日本語の概要は準備中です。原文の説明を表示しています。