359건 · 관련도순
Check AI agent plugins and tools for changed, missing, or untracked files using SHA-256 file fingerprints. Generate integrity records, review dependency version settings, and check requirements before promoting a plugin to production.
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Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.
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Set up host-based intrusion detection to monitor changes on Windows and Linux endpoints. Configure Wazuh agents, file integrity checks, registry monitoring, alert rules, and automated responses, with guidance on exclusions and centralized security monitoring.
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Audit a paper for numerical inconsistencies and other research integrity concerns. Record findings tied to source passages, track corrections and human waivers, and translate the audit report into workflow blocking or warning decisions.
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Coordinate a research project from topic exploration to a final manuscript. The skill connects research, writing, integrity checks, review, and revision workflows, tracks deliverables, and records progress with user confirmation at stage boundaries.
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End-to-end substantive-integrity forensic sweep of a research paper (especially autoresearch / AI-Scientist-style output). Orchestrates the whole pipeline: ingest (arxiv-id | pdf | dir → working dir + pdftotext for L0) → /evidence-ledger (artifact manifest + observability level L0/L1/L2 + span-anchored claims.json) → fan out the integrity auditor skills (consistency, citation, baseline, experiment, presentation, proof-derivation, eval-design — each reads the ledger, emits span-anchored findings) + the zero-verdict-weight AIS writing-style track → advisory memos (/adversarial-case-builder + /novelty-duplication-advisory, no verdict weight) → deterministic tools/adjudicate_findings.py (--ledger REQUIRED) → reviewer-ready Integrity Forensics Report. Cross-model (fresh codex per dimension) and reviewer≠adjudicator: the model proposes findings, the deterministic adjudicator decides the verdict. Observability-aware, detect-only, never an opaque AI-text classifier (a separate zero-weight AIS section lists AI writing-style impressions, never moving the verdict). Triggers: "anti-autoresearch", "integrity audit this paper", "forensic review", "audit a submission", "审一篇投稿的诚信".
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Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.
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Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
의료 앱 공개 전에 진료 판단 지원 로직, 환자 정보 유출, 데이터 무결성 등을 검증하는 스킬입니다. 중대한 테스트가 실패하면 배포를 중단하는 구성을 제시합니다.
Evaluate an Agent Skill bundle before release by checking structure, activation conditions, output quality, scripts, safety, file integrity, and host compatibility. Receive a detailed JSON report that distinguishes local evidence checks from production readiness backed by external attestation.
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Assess a company’s management through public statements, delivered commitments, crisis decisions, and use of company funds. Produce evidence-based scores for integrity, strategy, capital allocation, and governance, alongside a verdict on long-term trust.
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Set up decoy files on endpoints and shared folders to detect possible ransomware activity. Monitor modifications, deletions, and renaming with file monitoring tools, connect events to alerts, and test whether the detection and response process works.
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Check a paper's numerical consistency using fixed rules, then record blocking or warning decisions and outstanding findings. The workflow preserves a traceable correction history, while explicitly leaving broader semantic review unavailable in its Codex mode.
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Coordinate meta-analysis, which combines results across studies, from data extraction to reporting. Validate effect sizes, check for duplicates and implausible values, and assess how individual studies or publication bias affect the combined result.
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Coordinate research, drafting, citation and data verification, AI review, revision, and final formatting through connected skills. Start from a research topic, an existing manuscript, or reviewer comments, with progress tracking and user checkpoints.
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Review dependency handling, package downloads, credentials, and deployed file cleanup in APM. The skill defines checks for safe paths, centralized credential access, and refusing content when its integrity or signature cannot be verified.
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Via modeling: pads, antipads, ground return vias, GRV placement, and signal integrity for high-speed layer transitions. TRIGGER: user asks to model a via, design a via transition, place ground return vias, analyze via performance, or check signal integrity through layer transitions. Invoke BEFORE writing code — only viaSingleEnded exists (no viaDifferential), and the location format is non-obvious. SKIP: general signal integrity without vias (use matlab-analyze-em), transmission line design (use matlab-design-pcb-transmission-line), PDN analysis (use matlab-analyze-pcb-pdn), material/stackup setup only (use matlab-manage-pcb-material).
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Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/check_ai_style.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS-* style tells (broken narrative arc, LLM phrase tics, jargon-stuffing, invented codenames, clause/formula walls, gratuitous pseudocode, bullet overuse, bold-module spam, restatement loops, focus drift, single-style figures, appendix dumping). Every finding is named, LOCATED, span-anchored to the evidence ledger (claims.json), carries not_integrity_finding:true + false_positive_risk:high + an fp_case, and gets ZERO verdict weight: the adjudicator forces it to info, excludes it from overall_verdict, and renders it in a SEPARATE report section. NOT an AI-text classifier — no scores, no "this is AI-written", no authorship probability; a paper can be CLEAN_GIVEN_EVIDENCE and still list many. Emits ai-style-impressions.findings.json; computes NO verdict; if a tell is actually substantive it routes to the integrity auditor. Triggers: "AI style", "vibe check", "writing fingerprint", "AI 文风", "vibe paper".
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Use when picklist data quality has degraded: unrestricted picklists accepting garbage API values, dependent picklist relationships bypassed by integrations, record type picklist value mappings drifting out of sync, or orphaned values appearing in reports. NOT for the value-deactivation runbook or the API-name-vs-label trap — use admin/picklist-data-integrity. NOT for initial picklist creation or Global Value Set design — use admin/picklist-and-value-sets. NOT for record type configuration itself — use admin/record-types-and-page-layouts.
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Picklist governance for Salesforce admins — Restricted vs Unrestricted, Global Value Sets vs local picklists, value deactivation that breaks reports, dependent picklists with deactivated controllers, and the 'value not in picklist' phantom-data problem. Covers the per-record-type value selection model, the API-vs-label mismatch trap, and the mass-replace-values runbook. NOT for auditing an org where invalid values already exist or record-type mappings have drifted — use admin/picklist-field-integrity-issues. NOT for creating a picklist field or wiring up a dependent picklist — use admin/picklist-and-value-sets.
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Verify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"
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Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.
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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
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