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Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.

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Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.

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Apply GPGP/TAEMS coordination as local-scheduler constraints under explicit task, view, commitment, and delivery assumptions. NOT for assuming global task visibility, reliable delivery, or modern execution authority.

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Classify every destructive/gated action an agent body can attempt (git, filesystem, network, shell, github) into block/approve/allow tiers, and audit whether the pre-tool and post-tool governance backing that classification is real: blocked actions proven zero-side-effect by a negative fixture, every denial backed by a receipt and a transcript event, every block-tier denial paired with a safe alternative, and no unmanaged or same-UID body ever marked "contained." Use when building or reviewing Agent Harbor's C5 governance gate (destructive git blocker, approval request/result, denial receipts), gating a new tool surface before it can run destructive commands, or auditing whether an existing "destructive-action blocked" claim actually has evidence behind it. NOT for proving an existing sandbox boundary actually contains an adversary once an action is isolated (use sandboxed-adversarial-test-harness), deciding where in a DAG to place a human review node or designing the approval UX itself (use human-gate-designer), or designing the general work-receipt schema across an entire agent task (use agent-work-receipt-designer).

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Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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Find and identify the CCSPlayerPawn_SnapEyeAngles function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the non-virtual SnapEyeAngles method. It is the one caller of the shared apply helper (CCSPlayerPawn_ApplyEyeAngleNetworkChange) that is NOT CCSPlayerPawn_SnapViewAngles. Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSPlayerPawn_SnapEyeAngles.py preprocessor (xref_funcs apply-helper, exclude_funcs SnapViewAngles). Trigger: CCSPlayerPawn_SnapEyeAngles, SnapEyeAngles

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Locate CCSPlayer_MovementServices::Pawn in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSPlayer_MovementServices::Pawn" (symbol CCSPlayer_MovementServices_Pawn). This is a STRUCT MEMBER OFFSET (schema netvar), not a function. Resolve the CCSPlayer_MovementServices class layout via the schema/network system; the member must satisfy natural alignment. Cross-check with functions that dereference the member. Do not switch output schema: emit the structmember fields below even if the class turns out to be polymorphic - a func_*/vfunc_* payload for this symbol is rejected by canonical_symbol_yaml_bytes. Trigger: CCSPlayer_MovementServices_Pawn, CCSPlayer_MovementServices::Pawn

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Locate CCSPlayerController_InventoryServices::m_pInventory in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerController_InventoryServices::m_pInventory" (symbol CCSPlayerController_InventoryServices_m_pInventory). This is a STRUCT MEMBER OFFSET (schema netvar), not a function. Resolve the CCSPlayerController_InventoryServices class layout via the schema/network system; m_pInventory is an 8-byte pointer member early in the class (must stay 8-aligned). Cross-check: functions of the class dereference this->m_pInventory shortly after entry. Trigger: CCSPlayerController_InventoryServices_m_pInventory, CCSPlayerController_InventoryServices::m_pInventory

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Find and identify the CCSPlayerController_Respawn virtual function in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the Respawn vfunc slot by RTTI-walking the CCSPlayerController vtable and confirming the candidate's decompiled body performs a full controller-state reset (networked flags, per-round fields, comeback/damage-info bookkeeping) rather than any other lifecycle vfunc. Trigger: CCSPlayerController_Respawn

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Locate CCSBot::Profile in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSBot::Profile" (symbol CCSBot_Profile). This is a STRUCT MEMBER OFFSET (schema netvar), not a function. Resolve the CCSBot class layout via the schema/network system; the member must satisfy natural alignment. Cross-check with functions that dereference the member. Do not switch output schema: emit the structmember fields below even if the class turns out to be polymorphic - a func_*/vfunc_* payload for this symbol is rejected by canonical_symbol_yaml_bytes. Trigger: CCSBot_Profile, CCSBot::Profile

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Locate CBaseEntity::m_iTeamNum in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CBaseEntity::m_iTeamNum" (symbol CBaseEntity_m_iTeamNum). This is a STRUCT MEMBER OFFSET (schema netvar), not a function. Resolve the CBaseEntity class layout via the schema/network system; the member must satisfy natural alignment. Cross-check with functions that dereference the member. Trigger: CBaseEntity_m_iTeamNum, CBaseEntity::m_iTeamNum

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Converts Stitch designs into modular Vite and React components with system-level networking and AST-based validation. Use when translating design exports into production-ready React component architecture.

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Runs Hyper-V VMs and Windows Sandbox. Trigger to create, start, stop, check or delete a VM (Gen2, secure boot, virtual TPM, settings proved); to take, list or restore a checkpoint; to test a plugin install on a clean machine from a golden checkpoint; to open a suspicious file or zip in a disposable sandbox with networking off; when Hyper-V says access denied; or say hypervrunner (audit, setup, vm, checkpoint, teardown, cleanroom, sandbox, status). Runs as a Hyper-V Administrators member, never self-elevated. Containers and WSL are dockerrunner's; whether code is trustworthy trustwarden's; secrets in returned files shieldwarden's.

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Complete Three.js building system with spatial indexing, structural physics, and multiplayer networking. Use for survival/crafting games, sandbox games, multiplayer construction, or any 3D building mechanics.

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Guide for adding new blockchain chains to OneKey. Use when implementing new chain support, adding blockchain protocols, or understanding chain architecture. Triggers on chain, blockchain, protocol, network, coin, token, add chain, new chain.

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Protocol-based dependency injection for testable Swift code — mock file system, network, and external APIs using focused protocols and Swift Testing.

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Docker and Docker Compose patterns for local development, container security, networking, volume strategies, and multi-service orchestration.

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Describes the NemoClaw plugin and blueprint architecture and how they orchestrate the OpenClaw sandbox. Use when looking up architecture, plugin structure, or blueprint design. Trigger keywords - nemoclaw architecture, nemoclaw plugin blueprint structure, nemoclaw cli commands, nemoclaw command reference, nemoclaw network policy, sandbox egress control operator approval, nemoclaw troubleshooting, nemoclaw debug sandbox issues.

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Creates interactive educational MicroSims using the best-matched JavaScript library (p5.js, Chart.js, Plotly, Mermaid, vis-network, vis-timeline, Leaflet, Venn.js). Analyzes user requirements to route to the appropriate visualization type and generates complete MicroSim packages with HTML, JavaScript, CSS, documentation, and metadata.

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Indestructible agent memory — permanently stored, never lost. Save decisions, identity, and context as a memory chain on the Autonomys Network. Rebuild your full history from a single CID, even after total state loss.

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Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".

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LinkedIn automation via browser relay or cookies for messaging, profile viewing, and network actions.

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Multi-Agent group chat collaboration system inspired by DingTalk/Lark. Enables AI agents to chat in groups, @mention each other, assign tasks, make decisions via voting, and collaborate. Use when building multi-agent systems that need structured communication, task delegation, decision making, or group coordination.

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