Results for “duplicate-detection”

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Deduplicating Training Data Makes Language Models Better

Analyzes multiple provided pages to identify keyword overlap and potential cannibalization issues. Suggests differentiation strategies. Use PROACTIVELY when reviewing similar content.

Refactors duplicated code by extracting helper methods, shared abstractions, or reusable structures, with automated testing and commits after each extraction.

Analyzes .NET test suites for duplicated boilerplate, copy-paste tests, and structural maintainability issues, producing a report with concrete before/after refactoring suggestions.

Runs a three-layer verification pipeline on AI-generated output: extracts verifiable claims, finds supporting or contradicting sources via web search, and produces a structured verification report with source links for human review.

`analysis-agent`/`task-agent`/`review-agent`: use when repeated failure needs a new hypothesis or proof path; skip an initial failure with verified cause and a different action.

Searches local, marketplace, GitHub, and web sources for existing skills before creating a new one to avoid duplication.

Systematic audit and review by Referee 2. Two modes — "deck" reviews slide presentations for rhetoric, visual quality, and compile cleanliness; "code" performs cross-language replication and econometric audit of empirical pipelines. Use when reviewing slides, auditing code, or verifying replication.

Rereads code, specs, plans, reviews, or skill edits with fresh eyes to catch blunders, oversights, and omissions before closure, reporting concrete evidence-backed findings.

Discover, compare, audit, repair, adapt, craft, run, debrief, and publish repeatable AI-agent loops for engineering workflows.

Diagnose bugs, failing tests, or unexpected behavior by reproducing, isolating, hypothesizing with evidence, and confirming fixes before making changes.

Systematic debugging — reproduce, read the error, isolate, hypothesize, fix, verify, document. Use when tracking down a bug, unexpected behavior, or test failure — including phrases like "this doesn't work", "why is this failing", or "help me debug".

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

COCO: Microsoft COCO: Common Objects in Context

Detect and resolve naming collisions, overlapping intent triggers, and insufficient intent diversity across the skill library. Load when a new skill is created (called by universal-skill-creator), during improvement passes (called by improve-skills), or when the user asks to deconflict skills, check for naming collisions, audit trigger overlap, review intent coverage, find duplicate triggers, check skill names, or says "are any skills too similar", "which skills overlap", "deconflict the library", "check for confusing skill names", "intent audit".

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new problems.

Automatically organizes files in a directory into a clean structure based on configurable rules — by file type, date, project, or priority — with support for duplicate detection, naming conventions, and archival strategies. Use when the user requests file organization or provides relevant inputs for this workflow.

Find adjacent vendors and subs (not top market primes) who fill a capability gap against a displacement target using USASpending flows and SAM entity signals. Use when user defines a teaming gap and wants vault-ready partner shortlist with citations.

Intelligently organize files across your computer by understanding context, detecting duplicates, suggesting folder structures, and automating cleanup. Maintain a clean digital workspace without manual effort.

Executes DAG workflows with parallel wave processing, agent spawning, context isolation, permission enforcement, and full execution tracing. Use when running a planned DAG, managing concurrent agent execution, enforcing isolation boundaries, or tracing execution for debugging. Activate on "execute DAG", "run workflow", "spawn agents", "parallel execution", "execution trace", "agent isolation". NOT for planning DAGs (use dag-planner), validating outputs (use dag-quality), or matching skills (use dag-skills-matcher).

Maps a codebase into feature-grouped flowcharts, identifies duplicated concerns across features, and proposes a unified architecture with handoff prompts for refactoring.

Audit a codebase, UI, or copy for machine-generated tells across prose, visual/UI, code, and structure/IA, then produce a phased de-slop burndown. Use when the user says "feels AI-generated", "looks like AI slop", "reads like ChatGPT", "feels generic/soulless", or wants an authenticity/voice pass before launch.

Systematically fuzz web applications for hidden content and input validation vulnerabilities across directories, files, parameters, and authentication bypasses.

Detect domain fronting C2 traffic by analyzing SNI vs HTTP Host header mismatches in proxy logs and TLS certificate discrepancies using pyOpenSSL for certificate inspection.

Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

Spawns multiple parallel AI agents that compete on the same task using isolated git worktrees, evaluates results, and merges the best solution.

Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality using scapy for packet capture analysis.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC sc

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Reviews a PR or diff through three independent lenses—correctness, security, and test coverage—then deduplicates and validates findings before persisting them, reducing noise while catching more real bugs.

Wire ETL, ingestion, cron, edge-function, and queue jobs correctly. Use for "build a pipeline", "sync X into Y", "nightly aggregation", "cron double-counts", "dedupe", "backfill", "the numbers are wrong after a retry". Bakes in idempotency, atomic writes, data contracts, dead-letter, and observability.