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Found 62 Skills
Operate notifications as one ECC-native workflow across GitHub, Linear, desktop alerts, hooks, and connected communication surfaces. Use when the real problem is alert routing, deduplication, escalation, or inbox collapse.
Integrate multiple plot point analysis results into a comprehensive report, and generate high-quality analysis through deduplication, classification, sorting, and summarization. Suitable for integrating multiple analysis sources and generating unified reports
Search Zotero library using code execution for efficient multi-strategy searches without crash risks. Use this skill when the user needs comprehensive Zotero searches with automatic deduplication and ranking.
Integrate Didit Face Search standalone API to perform 1:N facial search against all previously verified sessions. Use when the user wants to detect duplicate accounts, search for matching faces, check if a face already exists in the system, prevent duplicate registrations, search against blocklist, or implement facial deduplication using Didit. Returns ranked matches with similarity percentages.
Reviews, curates, and maintains the Forge library of agents, skills, and templates. Performs deduplication analysis, staleness detection, quality promotion, and orphan reference checking. Produces structured review reports with actionable recommendations for merging, archiving, or promoting library items. Use this skill when the user wants to review the library, clean up agents or skills, check what's available, find duplicates, trim unused items, see library statistics, or says "what's in my library?" Also triggers on scheduled review intervals or when the library grows beyond 20 items. Do NOT use for creating new agents (use Agent Creator), creating skills (use Skill Creator), or planning teams (use Mission Planner).
Secures webhook receivers with signature verification, retry handling, deduplication, idempotency keys, and error responses. Provides verification code, dedupe storage strategy, runbook for incidents. Use when implementing "webhooks", "webhook security", "event receivers", or "third-party integrations".
Implement Nostr client architecture including relay pool management, subscription lifecycle with EOSE/CLOSED handling, event deduplication, optimistic UI for publishing, and reconnection strategies. Use when building Nostr clients, managing WebSocket relay connections, handling subscription state machines, implementing event caches, or debugging relay communication issues like missed events or broken reconnections.
For users needing to conduct systematic literature reviews, literature reviews, related work, or literature research: AI automatically generates search terms, performs multi-source retrieval → deduplication → AI reads and scores each paper one by one (1–10 points for semantic relevance and sub-topic grouping) → selects papers based on high-score priority ratio → automatically generates word budget for the review (70% cited sections + 30% non-cited sections, average of three samplings) → free writing in the style of senior domain experts (fixed sections: abstract, introduction, sub-topics, discussion, future outlook, conclusion), with strict verification of main text word count and number of references, and mandatory export to PDF and Word. Supports multilingual translation and intelligent compilation (en/zh/ja/de/fr/es).
Next.js performance optimization and best practices. Use when writing Next.js code (App Router or Pages Router); implementing Server Components, Server Actions, or API routes; optimizing RSC serialization, data fetching, or server-side rendering; reviewing Next.js code for performance issues; fixing authentication in Server Actions; or implementing Suspense boundaries, parallel data fetching, or request deduplication.
Implementation workflows for Frappe scheduled tasks and background jobs (v14/v15/v16). Covers hooks.py scheduler_events, frappe.enqueue, queue selection, job deduplication, and error handling. Triggers: how to schedule task, background job, cron job, async processing, queue selection, job deduplication, scheduler implementation.
Generate and curate evaluation datasets — structured generation via dimensions-tuples-NL, quick from description, expansion from existing data, plus dataset maintenance through deduplication, rebalancing, and gap-filling. Use when creating eval data, expanding test coverage, or cleaning datasets. Do NOT use when sufficient real production data exists (use analyze-trace-failures instead). Do NOT use for evaluator creation (use build-evaluator).
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.