'reconstruction' 검색 결과

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for custom binary or text protocol recovery, handshake reconstruction, framing, sequence control, checksums, stateful replay, and accepted-session reproduction. Use when the user asks to decode an unknown protocol, recover custom framing, build a replay harness, satisfy sequence or checksum rules, replay a captured session, or prove the smallest message order that reaches an accepted branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Clone un site web à l'identique à partir d'une URL : capture Playwright complète (screens full-page + fold par fold sur desktop/tablet/mobile, crops par section, états hover), extraction de tout ce que le navigateur expose (fonts, variables CSS, palette, typo, layout, breakpoints, animations, assets, console/network), reconstruction section par section, puis boucle de vérification par diff pixel et console propre. Utilise ce skill DÈS QUE l'utilisateur veut copier, cloner, répliquer, reproduire, « refaire pareil », s'inspirer fortement d'un site, d'une landing page, d'un template ou d'une page précise (« fais-moi la même chose que X », « copie ce site », « reproduis cette landing », « clone neverboring.ai », « je veux exactement ce design »), même s'il ne dit pas « clone ». Aussi pour auditer visuellement un site existant (screens complets, design tokens, fonts utilisées).

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Cross-session shared memory for agents. Use at session start to load the persistent bias-log via `mcp__memex__recent_observations`, mid-session to search prior context with `mcp__memex__search`, and immediately when a bias surfaces to log a new `observation` via `mcp__memex__add`. The six-layer reconstruction trail (raw → embedded → indexed → graphed → curated → reflexive) means memex stores facts AND the agent's own reasoning patterns across sessions. Prefer querying memex before re-deriving prior architectural decisions; re-derivation is itself evidence that the trail is incomplete.

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Run the memex session-init ritual at the start of a fresh session: load the bias-log, read the persistent-self trail in order, run the verification scoreboard, and surface the next slice from the docs. Use at the very start of any session that operates on the memex repo, when reconstructing context before doing work, or when an interrupted session needs to re-orient. NOT the `memex init` CLI command (which initializes the store/db) — this is the reconstruct-context ritual.

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Conduct a blameless post-mortem analysis after an incident. Build timeline reconstruction, identify contributing factors, and generate actionable improvements. Focus on systemic issues rather than individual blame. Use after any production incident or service degradation, following a near-miss, when investigating recurring issues, or to share systemic learnings across teams.

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Consistent agent behavior after restart — progressive identity loading, working context reconstruction from persistent artifacts, fresh-vs-continuation detection, calibration through centering and attunement, and identity verification for coherence. Addresses the cold-start problem where an agent must reconstruct who it is and what it was doing from evidence rather than memory. Use at the start of every new session, after a session interruption or crash, when agent behavior feels inconsistent with prior sessions, or when persistent memory and current context appear contradictory.

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Workflow recovery protocol for resuming workflows after context loss, session interruption, or errors. Handles state reconstruction, artifact recovery, and seamless workflow continuation.

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Use when an authorized reference surface needs a clean-room frontend reconstruction across static, motion, and responsive fidelity. Not for styling or component work without a reference surface.

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Digital forensics and incident response triage skill for disk, memory, network, cloud, and endpoint artifacts. Covers Volatility 3, Velociraptor, KAPE, Plaso, Autopsy, Chainsaw, Hayabusa, Zeek, Arkime, EVTX, AWS/Azure/GCP audit logs, and timeline reconstruction. Use to extract IOCs, build incident timelines, and produce defensible analyst reports.

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Analyzing contact tracing data — network reconstruction, transmission chains, and evaluating tracing performance.

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Write blameless incident postmortems following Google SRE methodology. Covers timeline reconstruction, root cause analysis (5 Whys), impact assessment, and action items with owners. Use after production incidents, outages, or significant bugs to prevent recurrence.

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Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.

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Memory forensics playbook using Volatility 2/3. Use when analyzing memory dumps for malware analysis, credential extraction, process investigation, code injection detection, and incident response timeline reconstruction.

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Rebuilds a usable PE file from a memory dump by fixing the section alignment (virtual-to-raw), correcting the entry point, and preparing for import table reconstruction. Activates for requests to rebuild a dumped PE, fix a memory-dumped executable, or convert a process dump into an analyzable file.

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Audit an existing Lamport-style hierarchical mathematical proof forward for validity and whether its hierarchy, scope, dependencies, side conditions, and proof constructs discharge the stated theorem. Use when the central question concerns a top-down proof with numbered levels, subproofs, ASSUME/PROVE, SUFFICES, PICK, CASE, DEFINE, Q.E.D., or prose equivalents. For an ordinary proof that first needs source-preserving hierarchy, use convert-lamport; for conclusion-first AND/OR obligation reconstruction, use reverse-lamport. Do not use to generate or replace a proof, or to audit Lean, Coq, Isabelle, or other proof-assistant source.

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基于已有项目做重构/新建（搬家不重写）的完整流程 Skill。当用户需要从现有混乱项目迁移到干净新项目时激活，引导完成：新项目文件夹 → PM功能清单 → 技术架构+红蓝推演（回写闭环）→ Cursor规范配置 → 分Phase构建。区别于 role-技术架构师（后者设计新技术方案）：本 Skill 是迁移型，先盘点已有资产，再设计去哪里放。触发词：「基于已有项目重构」「重构方案」「新建项目并迁移」「搬家不重写」「从零开始但复用现有代码」「项目架构整改」。

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Author, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensic event reconstruction on infected hosts (use secops-investigate), or case management operations (use secops-cases).

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Reconstruct editable LaTeX from PDF content using page-aware extraction and visual comparison. Preserve source evidence, flag uncertain math/tables/citations, and distinguish text extraction, OCR, reconstruction, and verified compilation.

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Professional network graph analyst identifying Gladwellian superconnectors, mavens, and influence brokers using betweenness centrality, structural holes theory, and multi-source network reconstruction. Activate on 'superconnectors', 'network analysis', 'who knows who', 'professional network', 'influence mapping', 'betweenness centrality'. NOT for surveillance, discrimination, stalking, privacy violation, or speculation without data.

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Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

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Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.

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Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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Plan and run safe reverse engineering, decompilation, disassembly, instrumentation, de-obfuscation, and anti-analysis countermeasures for binaries, firmware, mobile apps, WebAssembly, JavaScript bundles, and .NET assemblies. Use for behavior reconstruction, cross-layer Electron tracing, REA, reversing strategy, decompiler selection, binary analysis, malware-style triage, unpacking, deobfuscation, defeating anti-debug or anti-VM checks, Wakaru, Ghidra, IDA, RetDec, radare2, Rizin, Frida, angr, YARA, or command scripts that capture analysis stats.

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Evaluate, introduce, or refactor persistence abstractions when direct Eloquent is insufficient. Use for repository requests, complex aggregate reconstruction, or multiple persistence implementations; reject CRUD wrappers without a concrete benefit.

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