「pact」の検索結果

Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.

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Shared conventions for every SDLC development-phase architect agent: hard rules, code quality bar, workflow steps (superpowers invocation, spec reading, codebase exploration, verification), and the report/compact-summary contract. Architects load this skill first, then apply their stack-specific instructions on top.

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。

Operator-side inventory of experiment PRs for auditing a Senpai research track. It outputs merged winners, a compact results table, and full details for deep dives.

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。

Fetch upcoming economic events and data releases using FMP API. Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases, and other market-moving economic indicators for specified date ranges (default: next 7 days). Output chronological markdown reports with impact assessment.

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This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.

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Use when an answer is wanted from one named seat only: breaking, business, career, codebase, human, impact, innovation, moat, rent-seeking, skeptic, or stability. Not for multi-lens synthesis: use prism. Not for rebuilding from primitives: use from-first-principle.

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Use when asked to audit typography across a codebase. Produces a file:line report with concrete fixes ordered by impact and flags unverifiable rules. Not for building a type system or token scale.

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。

Assess and route one bounded Proto UI dependency drift, advisory, or update question. Use to trace manifest and lockfile identity, consumer impact, compatibility evidence, provenance, update risk, and the exact next update or no-action transition. This leaf remains read-only.

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。

Distill books, documents, folders, or URLs into compact, practical Agent Skills. Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and examples.

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Recover from stale RFDB nodes that survive `commit_batch` cleanup despite having their `file` field listed in `changed_files`. Use when: (1) re-running an enricher/orchestrator step but old data with the same file field stays in the graph, (2) `commit_batch(changed_files=[X], ...)` reports success but a query later still returns nodes with `file = X`, (3) you re-pointed structural nodes (e.g., DIRECTORY/FILE) from a synthetic file path to real file paths and the old synthetic-path nodes won't go away, (4) graph-stream / find_by_type returns a mix of "stale" and "new" nodes for the same logical entity. Root cause: `handle_commit_batch` calls `engine.find_by_attr({file: X})` to enumerate nodes-to-delete, but that query path returns only a PARTIAL set after compaction (L1 segments and the file→nodes index can desync). Workaround: enumerate stale nodes via the TS rfdb client `queryNodes({file: X})` (different code path that finds all of them) and call `deleteNode(id)` per node, then re-commit the intended state. Related: rfdb-manifest-l1-carryforward (same L1 family).

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Use for ANY bug bounty, penetration test, or web/API/mobile security assessment: recon, testing endpoints, analyzing Burp traffic or any bug-bounty platform's MCP (HackerOne, Intigriti, Bugcrowd, YesWeHack, Immunefi), reviewing APKs, bypassing a WAF, enforcing scope, hunting a specific vuln class, validating findings, writing reports, or when you are stuck on a target and about to give up. Activates automatically for all bug-bounty and pentest sessions. Runs an autonomous, never-surrender predator methodology: refuses to conclude "secure", exhausts each vuln class to depth, proves a real attacker-path with impact and no false positives, tracks coverage so nothing is re-tested or skipped, diagnoses failed tooling from full logs, and hunts at maximum effort with intelligent token use until the goal is found. Activates and follows instructions in ANY language the operator writes in (the skill itself is English). Bundles a per-class web-exploitation KB, a writeup library, and PayloadsAllTheThings.

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。

Distill books, documents, folders, or URLs into compact, practical Agent Skills. Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and examples.

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 or evolve an implementation-agnostic website design system from existing-site evidence, verified brand inputs, and an optional Visual Reference Brief. Use to establish visual and restrained-motion baselines, extract an existing site's rendered system for style-preserving extensions, validate one selected baseline with a compact Design Board, or update shared rules before page/module design. Do not conduct cross-industry reference research, map Elementor settings, create page layouts, design modules, or implement production code.

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Use this skill whenever the user wants to create, improve, rewrite, review, tailor, or ATS-optimize a PRODUCT MANAGER resume or CV. Triggers include any mention of 'PM resume', 'product manager resume', 'product resume', 'PM CV', 'product management CV', plain-language asks like 'create my resume', 'build my CV', 'optimize my resume', 'review my resume', 'make it ATS-friendly', 'tailor my resume to this job description', 'help me get hired', the Spanish terms 'hoja de vida' or 'curriculum vitae', or a request to turn product/PM experience into a resume, quantify product impact, write PM bullet points, or prepare a resume for an APM, PM, Senior, Lead, Group, Director of Product, Head of Product, or CPO application. Also use for AI Product Manager, Growth PM, Platform/Technical PM, and Product Owner resumes. Do NOT use for engineering-only resumes, LinkedIn rewrites, portfolios, or cover letters unless the resume is the main deliverable.

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。

Built by DynG AI for PlatformPilot.ai-style Go agent runtimes. Use when the user wants to scaffold HandoffKit runtime primitives into a Go project: channel-backed mailboxes, select, router, broker, join/quorum barriers, trace, budget, corpus/compactor, supervisor/nursery, dead letters, and sketch interfaces. Trigger words include PlatformPilot scaffold, scaffold handoffkit, add the HandoffKit runtime, set up message-passing agents, or drop in CSP agent primitives.

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Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"

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 the hyalo CLI instead of read/edit/grep/write when working with markdown (.md) files that have YAML frontmatter. This skill MUST be consulted whenever pi is working with markdown documentation directories, knowledgebases, wikis, notes, Obsidian-compatible collections, Zettelkasten systems, iteration plans, or any collection of .md files with frontmatter. Trigger this skill when: searching or filtering markdown files by content, tags, or properties; reading or modifying YAML frontmatter; managing tags or metadata across documents; toggling task checkboxes in markdown; getting an overview of a documentation directory; querying document properties or status fields; bulk-updating metadata across many markdown files; or when you find yourself repeatedly using read/edit/grep/write on .md files. Even if the user does not mention "hyalo" by name, use this skill whenever the task involves structured markdown documents with frontmatter. For pi sessions, ALWAYS use `--format text` for compact, LLM-friendly output.

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 surfacing the assumptions underlying an analytical judgment, the user asks "what are we assuming?" / "are these assumptions still valid?", or before publishing a high-impact assessment. Standard SAT applied during major assessments.

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。

Compact the current conversation into a handoff document for another agent to pick up.

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。

Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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Without identity infrastructure, every Claude Code session starts from zero. The agent has no name, no remembered preferences, no sense of who it works with or what it has learned. Context compaction erases everything mid-session. This skill fixes that — no external tools required.

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。

Expert error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.

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Use Codex (CLI + AppServer) as the full agent provider — planning, tool orchestration, native compaction, MCP tools, session resume — in place of the Claude Agent SDK. ChatGPT subscription or OPENAI_API_KEY. Per-group via agent_provider. Distinct from using OpenAI as an MCP tool (where Claude remains the planner).

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。