「dedup」の検索結果

Bridge Claude Code auto-memory into AgentDB with ONNX embeddings, deduplicate, and enable unified cross-project search

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。

Health-check the user's Claude Code setup and fix issues: diagnose installation health — what the `claude doctor` terminal diagnostics cover — from local data (duplicate or leftover installs, PATH, unparseable settings files, broken or colliding agent definitions, skills whose frontmatter fails to parse); find unused skills, MCP servers, and plugins versus their context cost and disable dead weight; deduplicate local CLAUDE.md files against checked-in ones; trim checked-in CLAUDE.md files by cutting content a session could derive from the codebase (directory layouts, tech-stack lists, architecture overviews) while keeping gotchas, rationale, and non-standard conventions; migrate always-loaded CLAUDE.md guidance into lazy skills and nested CLAUDE.md files; flag slow hooks and context-heavy extensions; check the installed version is current; make auto mode the default permission mode; and pre-approve frequently denied read-only commands. Use when the user asks for a doctor run, checkup, audit, tune-up, or cleanup of their Claude Code setup or configuration.

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。

Finds new job postings matching your profile via installed portal-search CLIs (LinkedIn, local job boards, and any skills added with /add-portal). Deduplicates across runs. Triggers on: job scrape, find jobs, search jobs, new jobs, job search, scrape jobs, /scrape

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Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.

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。

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", "this data is messy".

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 an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers, MISP, or email reports. Use when preparing indicators of compromise for safe analyst sharing or automating threat intel distribution to TAXII/MISP feeds.

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。

Daily digest of AI releases from Anthropic, Meta, Vercel Labs, OpenAI and Google GitHub repos, the Claude Cowork changelog, the claude.dev blog and Hacker News, deduplicated against a local state file and sent by email. Use when the user asks for a daily AI news digest, to scan AI labs for new releases, or to set up a scheduled release-hunter job.

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。

Deduplicate and synthesize raw concept stubs into a tiered intellectual map (T1 Canon to T4 Riff), tracing idea evolution across sources over time. Transforms thousands of raw concept pages into a curated intellectual fingerprint. Includes a reversible curation cull pass (Phase 5) with hard keep/delete/merge verdicts, substance gates, grounding labels, cluster budgets, and merge-with-backlinks salience promotion.

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。

Feed and whole-publication ingestion: turn an entire blog, newsletter, or RSS/Atom archive into brain source pages. Covers feed discovery, pagination walking, normalization to a common article shape, canonical-URL dedup, idempotent re-runs, 429 pacing, and empty-husk repair. This is the PUBLICATION-scope skill — a single article URL routes to idea-ingest instead. Per-article enrichment hands off to the brain-ingest-gate skill; public posts only (gated content is skipped, never worked around).

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。

Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.

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。

Find leads matching criteria and bulk-add them to an Apollo outreach sequence. Handles enrichment, contact creation, deduplication, and enrollment in one 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。

Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.

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。

Log genuine, recurring repository friction to .agents/PAPERCUTS.md — confusing setup, a flaky repo command or script, a misleading in-repo error, stale generated files, or a non-obvious gotcha that will cost the next contributor time. Also use to review, deduplicate, and resolve existing entries. Gate hard before logging: only friction the repository itself can fix counts. Never log the agent's own sandbox/permission errors, shell-scripting mistakes, transient flakiness, or third-party tool quirks the repo can't change.

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。

Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

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。

Daily outbound-sourcing sweep for {{greenhouse_role}}. Searches LinkedIn for candidates matching {{sourcing_criteria}}, dedupes every match against the role's Greenhouse pipeline, and drafts a personalized outreach email per new candidate to {{recruiter_review_inbox}} — capped at {{daily_cap}} per run. Never sends outreach and never writes to Greenhouse; the recruiter reviews, sends, and owns the ATS.

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。

Turns feedback from Plain, public reviews, and a Slack channel into deduplicated, quantified themes, and reconciles each theme against one Linear issue in {{linear_team}} — never prioritizing, assigning, or closing.

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。

Daily brand-mention monitoring loop for {{brand_terms}}. Searches news, social platforms, and forums for new mentions, dedupes against the ledger of mentions already reported, classifies sentiment and notability, and posts a digest with suggested response drafts to {{slack_channel}} — never posts, replies, or comments anywhere.

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。

Hourly error-backlog grooming sweep for {{sentry_project}}. Groups new and spiking errors, dedupes against existing GitHub issues in {{triage_repo}} by Sentry issue ID, drafts a GitHub issue with the stack trace and impact for the top {{max_issues_per_run}}, and posts a sweep summary to {{alert_channel}}. Never resolves, ignores, mutes, or assigns an error.

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。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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 knowledge for AI lead generation — web research, enrichment, scoring, deduplication, and report generation

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。

Triage Linear or Jira backlogs and run bug sweeps via the Composio CLI. Bulk-fetch issues, dedupe, relabel, reassign, and post summaries — all from the shell without clicking through the UI.

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。

Scan open issues and discussions, deduplicate, prioritize, and propose labels. Provides a clean actionable queue.

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。

Scan open issues and discussions. Dedupe, prioritize, and propose labels. Updates issue-triage-state.md. L1 propose-only — never auto-label or close.

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。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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。