「retry」の検索結果

Runs background jobs in Node.js with BullMQ, a job queue library backed by Redis (or PostgreSQL since v6): delayed and prioritized jobs, retries with backoff, rate limiting, cron-style job schedulers, parent-child flows and concurrency control. Use when a user asks to add a background worker, queue emails or webhooks, schedule recurring jobs, retry failed jobs, rate limit an API client, or migrate from Bull or from BullMQ 5 repeatable jobs.

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In-process semantic search over text files or in-memory strings, using Gemini embeddings via the CF AI Gateway. Use when user wants fuzzy/conceptual search where exact-keyword grep would miss — "sessions discussing regulatory constraints", "code about retry logic", "notes mentioning burnout even if the word isn't there". Complements searching-codebases (regex/AST) and extracting-keywords (YAKE). Do NOT use when an exact string/regex match is what's wanted — grep/rg wins on speed and precision there.

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Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe (evaluator-as-selector, stop on regression). Routes on measured cost-per-completed-task rather than per-token price, because a tier's token count varies more by task shape than price varies across tiers. Covers per-model effort semantics, the concision lever, cascade preconditions, context handoff, and watching a subagent fan-out live. Use when choosing a subagent's model or effort, when choosing how to escalate a failed attempt, or when asked which model or effort a task should get. Grounded in a 2026-07 calibration, a 2026-08 coding-cost study, a 2026-09 seeded-bug repair battery, a 2026-10 Haiku 5.5 ladder priced per spawn, and a 2026-10 retry-versus-escalation ladder on 295 SWE-bench Verified tasks; Managed Agents API specifics are operational, not calibrated.

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Retrieve clean markdown from URLs when web_fetch fails. Converts pages via Jina AI reader service with automatic retry. Use when web_fetch or curl returns 403, blocked, paywall, timeout, JavaScript-rendering errors, or empty content or user explicitly suggests using jina.

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Use when the task has a background job, queue consumer, scheduler or webhook - firing the real trigger, polling for the side effect, and the retry, idempotency and poison-message cases

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Sizes queues and worker pools. Use when estimating Kingman G/G/1 mean waiting time, Erlang or Little's Law sizing, p99 under load, retry storms, worker counts.

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Designing scheduling and time-aware logic for cron, timezone/DST, retry/backoff, and business-calendar systems. Use when schedule design is needed.

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Use when parallel agents in a convoy or across git worktrees hit a write conflict on shared state — a git tree, a config, or a shared document — and you must decide whether the conflict is semantically real. Classifies each conflicting hunk as independent (keep both), redundant (pick one), or a genuine clash (escalate), instead of blind locks, abort-and-retry, or escalate-everything. Sits ABOVE refinery-merge and NEVER blind auto-merges: it wraps an LLM judgment, defaults to escalate on any uncertainty, and unconditionally escalates any hunk touching .planning/, config, credentials, or safety-critical paths. Backed by CoAgent (arxiv 2606.15376v1). Triggers on write conflicts between parallel agents on shared repo state.

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When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.

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Use when designing or validating GitLab CI/CD and automatic release lifecycles, inspecting pipeline and job state read-only, composing non-interactive merge-request commands, or setting up protected refs and release credentials. Also covers reusable components and inputs, GitLab Functions, gitlab-ci-local, glab command composition, and GitLab Flavored Markdown. For pipeline execution or retry requests, provide inspection and command drafting while leaving execution to the operator.

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Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

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Launch and collect independent Codex sessions (gpt-6-luna at high, xhigh or max; gpt-6.1-sol at low to xhigh) for bounded parallel work, from Claude Code or from Codex: one manifest, one detached launcher, one report a session, a drain while it runs, and a retry of the sessions that ended without a report. Owns the Codex transport when another skill sets the questions, as whole-run-investigation does for its lanes. Use when the operator asks for Luna or Sol sessions, a swarm, many sessions, a reasoning effort, a Codex subagent from Claude Code, or a later collection of reports. Tell a request to launch from one to wait and collect.

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Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous systems".

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for queues, async workers, cron jobs, delayed tasks, retry behavior, worker-only config drift, and payload-to-side-effect chains. Use when the user asks to trace a queue payload, inspect async job execution, explain worker-only behavior, follow retries or dead-letter handling, or connect an enqueued item to a later file, cache, email, or privilege-bearing side effect. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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设计幂等接口和重试策略时使用。适用于支付、扣减、订单、关键写操作。优先使用 Idempotency-Key + 业务去重键 + 并发冲突处理（ETag/版本号）。

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Google App Script form-to-sheet integration with auto-retry, toast notifications, and Zalo/contact fallback. Reusable across any static website.

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Transcribes one explicitly authorized Brazilian Portuguese or Spanish audio or video file through the metered BRAINIALL API, then creates speaker-labelled JSON, SRT, and WebVTT with word timestamps and no automatic retry.

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Add controlled retries to pytest runs so agents can contain flaky tests and report final failures without rerunning whole suites by hand.

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Constructs Kubernetes-native workflow DAGs using Argo Workflows CRDs with configurable retry strategies, artifact passing via S3/MinIO, and template composition through WorkflowTemplates and ClusterWorkflowTemplates.

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Drive a whole tdmcp feature BACKLOG to completion as resumable, wave-by-wave releases — the campaign layer ABOVE tdmcp-pipeline/tdmcp-feature-lead. Use whenever the user wants to implement an ENTIRE backlog or discovery file (e.g. _workspace/discovery*/FEATURE_BACKLOG*.md), 'all the features', many features across multiple releases, or a long autonomous build campaign, AND for every follow-up: continue/resume the campaign, run the next wave, re-run a failed wave, fold in QA results, or check campaign status. Backed by a ledger.json so re-running is idempotent (skips shipped work, resumes interrupted work) and resilient (retry-once then quarantine-and-continue). For a SINGLE feature or one small batch, use tdmcp-pipeline instead; this skill is for the whole ledger.

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Use for self-correcting implementation. Implements the reflexion loop: implement, validate, self-critique, retry (max 3 iterations).

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Use when EZ_math_model coder or packaging fails repeatedly with the same error and needs root-cause analysis instead of another blind retry.

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Use when verification depends on agent runtime phases, queued input, tool-call lifecycle, save points, retry, abort, compaction, or branch behavior.

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Analyze Ascend NPU collective communication profiling data with a DB-first workflow. Use when the user provides `cluster_analysis_output/cluster_analysis.db`, rank-level `analysis.db`, rank-level `ascend_pytorch_profiler_{rank_id}.db`, together with `profiler_info.json`, and asks about HCCL or hcom communication cost, collective communication TOP ops, wait time, slow rank/straggler, Notify Wait, bandwidth, retry, relay, SDMA/RDMA/HCCS links, communication matrix, or Ascend communication fault patterns.

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。