「failures」の検索結果

Applies insights from MetaGPT paper on how structured communication, role specialization, and SOPs prevent coordination failures in multi-agent LLM systems

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Modifies DAG structure during execution in response to failures, new requirements, or runtime discoveries. Supports node insertion, removal, and dependency rewiring. Activate on 'replan dag', 'modify workflow', 'add node', 'remove node', 'dynamic modification'. NOT for initial DAG building (use dag-graph-builder) or scheduling (use dag-task-scheduler).

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Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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。

Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

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Use when applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.

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Interprets GitHub Actions run status and logs, diagnoses CI failure patterns, and suggests targeted fixes. Handles dependency install failures, test timeouts, build OOM, flaky tests, and workflow misconfigurations. Activate on: 'CI failing', 'build broken', 'workflow error', 'GitHub Actions debug', 'flaky CI', 'pipeline timeout', 'CI red'. NOT for: writing new workflows from scratch (use github-actions-pipeline-builder), CI caching strategy (use ci-cache-optimizer), deployment orchestration (use devops-automator).

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 crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

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Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.

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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.

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Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.

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End-to-end workflow orchestrator. Walks the 11-phase pipeline, invoking each phase skill in order inside an internal loop, yielding at consent gates (/approve-direction, /approve-swarm, /grant-commit), and exiting cleanly on yield/failure/done. Decides swarm-vs-solo at Phase 6. Auto-loops /tdd on integrate failures that don't require a spec change. The harness_continuation Stop hook is a safety net that re-fires harness only when the loop exited mid-flow.

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Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

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。

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

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Test a changed user interaction for realistic sequence failures such as double submission, stale responses, navigation, and recovery; use for interaction QA rather than general code review.

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Recall project knowledge, past decisions, failures, and preferences when they can help a coding task. Capture changed files before responding.

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Systematically find root causes and fix bugs. Use when debugging errors, investigating test failures, reproducing bugs from issue trackers (GitHub, Linear, Jira), or when stuck on a problem after failed fix attempts. Also use when the user says 'debug this', 'why is this failing', 'fix this bug', 'trace this error', or pastes stack traces, error messages, or issue references.

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Use when spawning a task()/agent()/workpool() subagent with an outputSchema, to reduce yield-tool retry failures by stating the exact result envelope and required fields in the task prompt.

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Investigate and fix failing CI. Takes a PR reference, one or more CI run/job URLs, or freeform text describing where the failures are, then enumerates failing workflows/jobs/steps, determines the root cause (often one shared cause behind several red checks), proposes a fix plan, applies code changes, commits, pushes, watches the new runs, and repeats until checks pass. Use when the user says "fix CI", "CI is red", "checks are failing", "the build is broken on my PR", "fix the failing jobs", or pastes a failing CI run URL. Do NOT use for authoring new workflows, or for read-only diagnosis with no fix intent — use gha-log-reader for that.

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Quality gate for agent-flow. Runs the repo's test, typecheck and lint commands in the issue worktree, re-runs failures once, reports raw output as JSON. Never modifies code. Use when launched with AGENT_FLOW_ROLE=qa after a review is approved.

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Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

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Load this skill when writing, reviewing, or interpreting behavioral accessibility automation — tests that manipulate a rendered page (resizing the viewport, pressing real keys, capturing and comparing screenshots) rather than only inspecting static markup or a single computed accessibility tree. Covers Reflow risk (SC 1.4.10) and Focus Visible risk (SC 2.4.7) checks. Absolutely always report indicator results separately from confirmed failures. Under no circumstances treat a clean behavioral scan as WCAG conformance, or collapse `cantTell`/test-error/indicator outcomes into a single pass/fail signal.

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[Testing] Use when you need to drive an integration-test suite to fully green — each round runs /integration-test-verify (whole system by default, or the target named in the prompt), and on ANY failure combines /debug-investigate + /integration-test-review (report-only) to adjudicate the fault (test wrong · test not optimal · source wrong), then /fix to resolve it at the owning layer, then /changes-review on that round's fix diff, then re-runs a FRESH full verify — looping until the whole suite passes its 2-consecutive-green-runs gate with zero failures.

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At the end of a long working session, produce a counterfactual trace note — a backward walk through the session that identifies the small number of load-bearing moments (observations, failures, accidents, decisions) without which the final outcome would not exist. Use this whenever the user asks to "close the session", "run the butterfly", "trace back", "what actually mattered here", asks for a session retrospective, or asks how the session arrived at its result. Also use it when a session ends with a significant outcome (a fix, a design decision, a shipped artifact) and the user wants a note capturing why it happened, not just what happened. Do NOT use for simple summaries of short sessions — this skill is for causal tracing, and it only earns its cost when the session was long enough to contain real forks.

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Patient safety evaluation harness for healthcare application deployments. Automated test suites for CDSS accuracy, PHI exposure, clinical workflow integrity, and integration compliance. Blocks deployments on safety failures. Use when a healthcare deployment must be gated on patient-safety tests for CDSS accuracy, PHI exposure, and workflow integrity.

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。