「ida」の検索結果

This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.

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Workflow for upgrading/integrating cache-dit in SGLang diffusion (multimodal_gen): DBCache, DMD calibrator, TaylorSeer, SVDQuant DQ; porting upstream PRs and resolving conflicts against the per-request knob system; adding new cache knobs; building the sglang generate CLI test matrix; precision validation (PSNR / log evidence); troubleshooting environment issues (wheel ABI, svdq extension, flashinfer conflicts). Use when upgrading or integrating cache-dit in sglang diffusion, porting cache-dit PRs with conflicts, adding cache knobs, running the sglang generate CLI test matrix, or validating precision (PSNR) for DBCache/DMD/SVDQuant(DQ) paths.

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Earnings estimate revisions, guidance analysis, and post-earnings drift (PEAD) — track analyst consensus changes, earnings surprise patterns, and management guidance shifts for US/HK equities.

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Install and run Stratus Red Team to detonate granular, MITRE ATT&CK-mapped AWS, Azure, GCP, and Kubernetes attack techniques through their warmup-detonate-revert-cleanup lifecycle, then verify the telemetry they generate. Use to validate that cloud detections (CloudTrail, GuardDuty, Microsoft Sentinel, GCP SCC, Falco) actually fire, or to run a repeatable purple-team exercise generating realistic attacker telemetry.

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Detects prompt injection using regex signature matching, heuristic scoring for structural anomalies, and DeBERTa-based transformer classification, flagging direct injections (system-prompt overrides, role-play escapes) and indirect injections (encoded payloads, obfuscation) per OWASP LLM Top 10 (LLM01:2025). Use for input validation layers in chatbots/agents/RAG pipelines, or for retrospectively classifying injection attempts in logs or incident investigations.

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Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-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。

WorkorAI talent marketplace skill: candidate job search and employer hiring with white-box match explanations via the WorkorAI MCP server (https://workorai.com/mcp). Use when the user asks to find a job, apply to jobs, respond to employer invitations, or when an employer wants to post jobs, search and evaluate candidates, invite them, and review applicants.

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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Add clidash — a zero-dependency, read-only web dashboard that derives its tabs and tables at runtime from any CLI that lists resources as JSON. Ships pre-wired for NanoClaw's ncl CLI (agent groups, sessions, channels, users, roles), plus message-activity charts, a log tail, and a read-only file viewer for group skills/CLAUDE.md/profiles.

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Skill validation framework PLUS daily test-suite health and regression intelligence. Validates skill conformance (frontmatter, manifest coverage, resolver coverage). Runs the project test suite in tiered phases (unit / evals / integration / system health), classifies failures, and produces a regression-aware report.

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Token-hygiene audit of the always-loaded context stack — CLAUDE.md, AGENTS.md, auto-memory MEMORY.md, and the bootstrap-rendered identity files (SOUL.md, USER.md, ACCESS_POLICY.md, HEARTBEAT.md) or their harness equivalents. Finds redundancy, contradictions, stale content, compression candidates, and skill-extraction candidates; produces a ranked action list sorted by token savings with a risk class per finding. REPORT-ONLY: this skill never edits any audited file. Recommendations for bootstrap-rendered files target the interview answer bank / templates, never the rendered output. Judging routes through `gbrain eval cross-modal` (single cheap model by default; full multi-model panel is explicit opt-in).

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Ghostwrite content in a specific person's voice from a VALIDATED voice profile — tweets, replies, short posts, launch copy, recruiting blurbs, emails. Loads the subject's voice profile (people/<slug>-voice) plus first-party context from the brain, drafts 2-3 options in-register, then runs a hard voice-fidelity self-check before showing anything. Includes the profile BUILDER: if no validated profile exists, drafting hard-stops and this skill walks the corpus-to-fingerprint build instead. Never auto-posts.

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。

Prepare a local Python SDK release candidate in a dedicated worktree. Use only when explicitly invoked with a version.

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。

Build or adapt a bounded computer-use loop where Cua Driver observes and acts, TypeSafe Jev selects only from application-owned candidate IDs, and the caller validates and verifies every action. Use for the jev-use recipe or similar Jev integrations; do not use it to add model logic or credentials to Cua Driver.

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Runs the payroll-confidence chain end to end — forecasts cash across the payroll window with cash-flow-snapshot, ranks and drafts overdue-invoice reminders with invoice-chase to close any gap, then assembles timesheets, flags every anomaly, and stages the run with payroll-prep. On Gusto the run is staged for the owner to submit; on QuickBooks Payroll, or with no payroll system, the last step is a validated run sheet the owner keys in. This chain never presses the payroll button. Builds on the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), with Gmail or M365, PayPal, Square, Stripe, and either Gusto or QuickBooks Payroll adding detail, and falls back to CSV and timesheet uploads. Use it whenever payroll is the worry, including "can I make payroll," "payroll is due Friday," "plan payroll," "will I have enough to pay the crew," or "I need to run payroll but money is tight." Accepts optional horizon and payroll-date arguments.

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。

Takes a pile of applications and turns it into a ranked shortlist, scored only against the job's stated rubric, with drafted replies to every candidate, scheduled interviews, and an onboarding checklist for whoever gets hired. Picks up where job-post-builder leaves off and carries the funnel through to first day, including the payroll setup handoff. Runs on Gmail and Google Calendar, deepens with Gusto, DocuSign, and Drive or M365, and works entirely from uploaded resumes with drafted replies the owner sends by hand. Use this whenever the owner has applicants to deal with — including phrasings like "I've got 40 resumes and no time," "who should I interview," "screen these applications," "help me narrow this down," "set up interviews for the top three," or "I need to tell the rest no." Reach for it when the owner mentions applicants, resumes, candidates, or being buried in a hiring inbox.

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 an offer letter with comp details and terms. Use when a candidate is ready for an offer, assembling a total comp package (base, equity, signing bonus), writing the offer letter text itself, or prepping negotiation guidance for the hiring manager.

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。

Track and manage recruiting pipeline stages. Trigger with "recruiting update", "candidate pipeline", "how many candidates", "hiring status", or when the user discusses sourcing, screening, interviewing, or extending offers.

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, report-only documentation review for Mastra docs. Use when auditing changed docs against source, validating contextual code examples or API coverage, checking canonical mastra-docs guidance, or running narrow deterministic checks.

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。

Design guidance for charts, graphs, dashboards, maps, and data visualizations, including a local palette validator.

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。

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:memory-review or asks what has been learned and what should be promoted or pruned.

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。

Validate, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (syntax + imports + runtime + output format), multi-dimensional quality scoring with letter grades and tier classification (BASIC/STANDARD/POWERFUL). Use when authoring a new skill, auditing existing skills for tier promotion, setting up pre-commit hooks for skill quality, or integrating skill QA into CI.

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 to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.

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Phase 2 of building a Claude Managed Agent — turn a validated build sheet into exact API payloads and a resumable BYOK curl launch script, then launch (environment → agent → session → kickoff) using the founder's OWN Anthropic key. Use when the user says "launch it", "deploy the agent", "create the agent now", or when the orchestrator routes phase=stage-launch. payload_generator.py emits the four ordered payloads; launch_script_writer.py writes launch.sh that reads $ANTHROPIC_API_KEY at runtime and never embeds it; payload_validator.py runs a pre-launch check including an API-key-leak scan. No tool in this skill makes network calls — the user runs launch.sh themselves. Distinct from interview (planning) and grade-iterate (the outcome loop).

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。