「trace」の検索結果

Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.

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Think like Fable 5 — natural, flowing, purposeful reasoning distilled from 4,665 real chain-of-thought traces with mathematical precision. Use this skill EVERY TIME before writing code, making decisions, or taking action.

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。

Research a technical topic thoroughly by exploring the codebase and summarizing findings. Use when asked to investigate how something works, research an area of the codebase, trace a data flow, or understand a component in depth.

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。

Break a plan, spec, or the current conversation into a set of tracer-bullet tickets, each declaring its blocking edges, published to the configured tracker — edges as text in one file per ticket locally, or native blocking links on a real tracker.

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 the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"

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 the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"

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。

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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。

Enforce traceability from natural-language intent to machine predicates, final observables, evidence, and owner-readable acceptance. Use when conducting any audit, review, QA pass, parity or compliance check, security assessment, code or architecture review, visual or behavioral comparison, data validation, system verification, completion gate, or claim that an implementation satisfies a human request.

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 the user asks for a phase-gated refactoring or redesign loop - session-blind workers map and trace the area, empirical probes precede the design, an approval gate guards implementation, and a conformance audit re-checks the branch after any design change. State persists in .episteme/DESIGN-<slug>.md. Requires the refactor agent roles registered by scripts/install-codex.sh. Keywords: refactor loop, redesign, design gate, enumerate before design, conformance audit.

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。

Measure real frontend performance — Core Web Vitals (LCP, CLS, INP), page-load traces, CPU and network throttling, Lighthouse accessibility/SEO scores, and heap growth — using chrome-devtools-mcp against a live browser. Trigger when asked "is the page fast enough", "measure Core Web Vitals", "why is this route slow", "run Lighthouse", "check for a memory leak", before any performance-gated certification (Built for Shopify, Core Web Vitals thresholds, store listing review), and before any launch or traffic-multiplier event. Measurement only — this skill never drives functional journeys.

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Diagnose and fix CJK (Korean, Japanese, Chinese) text-wrapping issues in web UIs. Systematically traces mid-syllable breaks, orphaned glyphs, and awkward line splits through a layered fix strategy covering global CSS cascade, component-library overrides, headline balancing, and translation-level phrase binding. Produces an evidence-based diagnosis and verification report before any code change is applied.

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Use BEFORE editing code that touches user data — traces the value from entry → validation → transformation → storage → egress, every hop cited with file:line.

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。

Diagnóstico e resolução sistemática de erros usando análise de primeiros princípios. Use quando encontrar qualquer mensagem de erro, stack trace ou comportamento inesperado. Suporta funcionalidade de replay para registrar e reutilizar soluções.

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 the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"

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 the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"

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。

[Code Intelligence] Use when investigating what happens when code executes, understanding blast radius, or tracing frontend-to-backend flows.

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。

[Code Quality] Use when you need to review integration tests for assertion quality, bug protection, repeatability, and test-spec traceability — AND verify the review target (changed production code) has test coverage (integration-first) with spec↔test↔code alignment.

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。

[Fix & Debug] Use when investigating a bug's root cause — reproduce the symptom, trace it end-to-start through the code, form and test hypotheses, and pinpoint the defect before any fix.

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。

Break a plan, spec, or the current conversation into a set of tracer-bullet tickets, each declaring its blocking edges, published to the configured tracker — edges as text in one file per ticket locally, or native blocking links on a real tracker.

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 for deterministic terminal or CI browser automation, repeatable regressions, traces, snapshots, screenshots, and unattended playwright-cli batches. Route interactive or signed-in Chrome work through browser-automation-router first.

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Design and implement unit, conformance, integration, timing, boot, trace, video, audio, and regression tests for EMULATORS processors, chips, peripherals, and complete machines. Use when creating a verification strategy for a new emulator, converting hardware documentation into tests, adding diagnostic programs or golden artifacts, measuring behavioral coverage, or proving that components interact correctly.

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。

Parse and interpret execution debug logs generated by the EMULATORS framework, correlating each CPU instruction block with its VIC-II, CIA, SID, interrupt, stop, raster, badline, and memory-access cycles. Use for .LOG traces from EMULATORS when diagnosing cycle timing, raster effects, CPU stalls, IRQ/NMI entry, instruction/chip synchronization, or when reconstructing execution around a PC or mapped register.

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。

Discipline for repairing a false claim once it has propagated: select what depends on it, produce a replacement, and accept with an expiring verdict. Trigger when: - A claim, citation, premise, or documented fact is found wrong and its downstream consequences must be traced and fixed. - A refutation, a failed re-run, a retracted external source, or a human correction lands against something already closed or already promoted. - Prompt contains: /remediation, remediation, retraction, false claim, propagated error, scrub, correction propagation, born-false, became-false, currency verdict, impeachment queue.

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How to MEASURE this repo instead of eyeballing it — interpret the loop-station context-budget trend (history.jsonl, thresholds, the tokenizer-switch gotcha), read a session trace, use the eval ledger (cost-per-success), and pick the right evidence tool (app-shots, smoke-deployed, db-counts, --check modes). Use when asked "is the harness bloating", "how big is CLAUDE.md really", "did this agent run actually succeed", "prove this works", "what evidence do we have", or when tempted to declare a capability (browser, tool) unavailable. For RUNNING the loop-station scripts, defer to the loop-station skill — this skill is interpretation + the wider evidence toolkit.

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。