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Found 87 Skills
Subscribe to AI and tech RSS feeds and persist normalized metadata into SQLite using mature Python tooling (feedparser + sqlite3). Use when adding feed URLs/OPML sources, running incremental sync with deduplication, and storing entry metadata without full-text extraction or summarization.
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.
Fetch journal articles from Crossref published after a user-specified date and insert them into PostgreSQL `journals` with DOI deduplication. Use when incrementally ingesting journal metadata from `journals_issn` into `journals`.
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.
Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.
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".
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).
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
Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.
Use truffler to find similar or pre-existing JavaScript/TypeScript symbols before implementing new code, especially helpers, utilities, parsers, formatters, scanners, fuzzy matchers, and other reusable functions. Agents should use this skill whenever they are about to add or refactor functionality in a JS/TS repository and need to avoid duplicating existing code, even if the user does not explicitly mention deduplication.
Build an entity-relationship link-analysis graph of an investigation — nodes, typed edges with source and confidence, aliases, and temporal validity — to expose shared infrastructure, bridging nodes, and the real principal behind a frontman. Use for link analysis, network mapping, Maltego graphs, Neo4j/Cypher or Gephi work, centrality and community detection, entity resolution and deduplication, or visualising how selectors and pivots connect.
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).