756 skills · sorted by relevance
Collect and organize victim postings from ransomware data leak sites through tracking feeds. Analyze group activity over time, compare sector and country patterns, and produce threat reports with risk assessments and defensive recommendations.
Capture and analyze iOS memory snapshots to find objects that remain allocated unexpectedly. Trace the references keeping them alive, identify a targeted fix, and compare the same app flow before and after to document evidence.
gitleaks + pre-commit を使ってローカルコミット時のクレデンシャル混入を機械的にブロックする。「クレデンシャル漏洩防止」「シークレット混入対策」「gitleaks」「pre-commit シークレット」「credential leak prevention」に言及した場合に使用する。
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Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".
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Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
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- Find leaked or mentioned selectors circulating in pastes, leak forums, Telegram channels and dump markets, and judge whether a claimed leak is genuine or a recycled combolist. Use when checking whether a name, email, domain or credential is circulating, verifying a breach claim made against your organisation, or setting up ongoing leak monitoring
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Create cinematic opening frames and chapter cards with warm light leaks, film grain, and large serif titles. The template produces a single HTML file with widescreen framing and optional subtle motion.
Review page source, browser JavaScript, and Git history for exposed API keys, passwords, and tokens. Identify potential leaks and guide fixes, including moving secrets to the server, replacing compromised credentials, and adding checks before commits.
Find and fix JavaScript memory leaks that accumulate as people use a web app. Review event listeners, timers, retained page elements, and outdated asynchronous tasks, then explain how to verify cleanup in the browser.
Audit environment files and repository contents for possible credential leaks, then prioritize findings by severity. Plan credential replacement, improve configuration examples, and establish checks in development and delivery pipelines to reduce accidental exposure.
Write and review Apache SeaTunnel unit tests and E2E tests that check complete jobs using Testcontainers. It provides concrete rules for container setup, waiting, resource cleanup, and assertions to address intermittent failures and resource leaks.
Find leaked secrets in source code, Git history, build artifacts, and infrastructure — and audit the secrets-management posture preventing future leaks. Use when the user mentions 'secrets audit,' 'secret scanning,' 'leaked credentials,' 'API key in code,' 'gitleaks,' 'trufflehog,' 'git history scan,' 'secrets management,' 'vault audit,' 'rotation policy,' 'AWS Secrets Manager,' 'HashiCorp Vault,' 'Doppler,' '1Password Secrets Automation,' 'sealed-secrets,' 'External Secrets Operator,' or needs to find or prevent credential exposure.
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A fast, configurable secrets scanner built by the creator of Gitleaks and backed by Aikido Security. Betterleaks detects leaked passwords, API keys, and tokens in git repositories, directories, and stdin with CEL-based validation and parallelized scanning.
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Monitors dark web forums, marketplaces, paste sites, and ransomware leak sites for mentions of organizational assets, leaked credentials, threatened attacks, and threat actor communications to provide early warning intelligence. Use when establishing dark web monitoring coverage, investigating specific data breach claims, or enriching incident investigations with dark web context. Activates for requests involving dark web OSINT, leak site monitoring, credential exposure, Recorded Future dark web, or Tor hidden service intelligence.
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Capture and inspect iOS leaks and memgraphs. Use when debugging leaked objects, retain cycles, memory growth, or before/after leak evidence.
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Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
The English summary is being prepared. Showing the original description.
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
The English summary is being prepared. Showing the original description.
Investigate memory growth and out-of-memory failures in OpenClaw tests. Compare memory measurements and heap snapshots, records of objects held in memory, to distinguish cleanup leaks from retained test modules and verify targeted fixes.
Inspect a site's login flow for token leaks, missing protections against forged requests, exposed session cookies, and OAuth configuration issues. Produce a structured findings report with severity ratings and a pass, warn, or fail verdict.
Review AI skills and bundles without running their code. Checks for data leaks, suspicious permissions, hidden code, and system changes across desktop and mobile platforms, then reports flagged actions, risk scores, and suggested mitigations.
Assess Bluetooth Low Energy devices by capturing wireless traffic, inspecting available data services, and checking pairing security. Identify signs of interception, reused commands, and data access that lacks authentication.
Create a film-style opening frame or chapter card as a single HTML file. Combine warm light leaks, grain texture, large serif titles, and optional scratches or film perforations for documentaries and personal short films.
Test whether an AI agent reveals sensitive information through progressively stronger dialogue prompts. Check API keys, personal data, system instructions, and internal settings, then report findings only when sensitive content is actually accessible.
Film light leaks, grain, 16:9 letterbox, and large serif type for cinematic openings or chapter cards.
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