'adversarial' 검색 결과

Adversarially probe this monorepo for security flaws at the right depth. Steers the red-team subagent — picks quick/standard/deep from the request, spawns it (fanning out for a wide deep sweep), collates findings into a dated report, and files security/sec:* issues for confirmed high/critical findings. Use when asked to "red-team", "try to hack this", "find security holes", "pentest this package/app", or as the brain behind the CI + daily security runs.

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Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

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Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

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Identify poisoned training data and backdoored models across the ML pipeline.

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Hostile, WordPress-specialized QA audit: severity-ranked findings, never a fix. Use for an adversarial audit.

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Load when working on Tier 0 Sovereign (payments, money, orders), Tier 1 Security (auth, tenancy, migrations), or Tier 2 Privacy (PII, erasure) tasks to enforce advanced model tier, Invariant-Breaker adversarial tests, and zero-PII telemetry validation.

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Load when orchestrating Multi-Agent Debate (MAD), adversarial code reviews, or pull-request deliberation to enforce Response Anonymization, expert persona assignment, and weighted post-hoc voting.

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Roleplay a hostile user to find and triage UX pain points.

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Dual-layer PR review tool (Raven's Verdict). Runs adversarial cynical review and edge case hunter in parallel, merges and deduplicates findings into professional engineering output. Use when user asks to 'review a PR' and provides a PR url or id.

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Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review,

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変更が将来インシデントや技術的負債を引き起こすと仮定し、その原因と経路を逆算して設計の盲点を可視化する

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攻撃者・競合・悪意あるユーザーの視点で変更を分析し、防御の盲点と悪用シナリオを可視化する

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コミット/PR の「全部置換した」「-N%削減」等の完了主張を grep で反証できる残骸や best/typical/worst 試算で検証し、抽出・集約リファクタでは並列度(Promise.all)/fast-path/遅延評価の性能特性退行と、Map/Set 集約キーの cross-kind 衝突による検出漏れも監査する

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変更に含まれる設計判断・実装選択の論理的整合性を徹底的に検証し、確証バイアスを排除して判断精度を高める

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同一ファイル/隣接ファイル内で「規則Xを守れ」と宣言した直後にXを破っている、宣言と実装の乖離を検出する

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モジュール/スキルの構造変更で当該ファイルは更新したが、caller 側が古い構造（旧参照・旧シグネチャ・旧採番）を参照したまま残るパターンを検出する

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Execute per-layer adversarial audit/remediation loop, persist fixes metadata, and manage fresh-run advancement

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Layer-by-layer remediation loop using adversarial audits, targeted fixes, and fresh-run verification enforcement

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Ship the day's work in one repo: triage new issues, review every open PR (description, visual recap, adversarial reviewer, shipping pre-flight and quality gate), merge only after GO, then post a status recap. Use for "ship it", "ship day", "merge the ready PRs", "end of work block".

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Devil's-advocate attack on AI workflow designs — threat model, assumption challenges, code critiques, and hypothetical redesigns with no file edits. Use for "attack this design", devil's advocate, or hostile challenge on agents/tools/summarization/tokens. Not for PR peer review (code-review) or self-review that applies fixes (ai-systems-review). Prefer: this skill → decide → ai-systems-review.

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Use when designing, running, or judging an experiment on a Syntropic137 workflow - comparing prompt variants, fan-out vs single agent, adversarial cross-model review, model selection (can Haiku do this job), phase splits, or tool grants. Trigger phrases include "run a workflow experiment", "A/B the workflow", "does fan-out help", "which model for this phase", "is this prompt better", "measure review quality", "why did the workflow do that". Do NOT use for a one-off workflow run that is just doing work, for authoring a workflow that is not being compared against anything, or for debugging a single failed execution (that is ordinary debugging).

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Comprehensive LLM security testing: bias detection, data leakage, prompt injection, jailbreak, alignment testing, adversarial prompts

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Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapt

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Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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