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Use when building your own cognee processing — writing custom tasks, chaining them into a pipeline with run_custom_pipeline or the lightweight run_pipeline (from cognee.pipelines import run_pipeline), storing custom DataPoints with add_data_points, running custom extraction/enrichment over the existing graph with memify, checking pipeline run status, or debugging how data flows between tasks (batch_size, data_per_batch, ctx, Drop, enriches).

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Gets payroll ready to run without anyone getting shorted: pulls timesheets from Gusto, QuickBooks Payroll, or an uploaded spreadsheet, totals regular, overtime, and PTO hours by person, flags every anomaly it finds — missed punches, overtime spikes, rate changes, mid-period hires and terminations, hours off the schedule — and stages the run for the owner to approve line by line before a dollar moves. Anomalies are always raised, never quietly corrected. Once the owner runs it, the journal posts to the ledger or goes to the bookkeeper. Reach for this whenever payroll comes up at all — "run payroll," "payroll is due," "check the timecards," "did everyone clock out," "how many hours did the crew put in," "why is payroll so high this week," "I need to pay the guys Friday" — and use it after cash-flow-snapshot when the owner is worried about whether payroll clears.

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Work through a live objection or competitive threat - what's really being said, the response that has worked before, and the proof points to use, grounded in your own win/loss history and customer quotes. Use when the user says "they said we're too expensive", "how do I respond to [objection]", "[competitor] is in the deal", "help me handle this pushback", or pastes an objection from an email or call.

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Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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Imports FBX meshes into CARLA on UE 5.8 as spawnable props — drives the editor headlessly to import the mesh, derives the prop size from its measured bounds, and registers it in PropParameters.json so it appears in the blueprint library as static.prop.<name>. Handles the FBX unit-scale problem, verifies the result against a running server, and can revert cleanly. Use when the user asks to "import a prop", "add a custom mesh/object to CARLA", "make my model spawnable", or a prop imports but never shows up in the blueprint library.

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Inspects a recorded CARLA .log without replaying it — show_recorder_file_info for the header/actors/frames, collision queries between actor categories, and blocked-actor queries for vehicles that got stuck. Use when the user asks to "what's in this recording", "show recorder info", "which vehicles collided", "find collisions in the log", or "which actors were blocked/stuck".

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**MANDATORY prerequisite** — you MUST invoke this skill BEFORE calling the `get_design_context` Figma MCP tool. You MUST trigger this skill whenever the user wants to implement, build, port, or code up a Figma design as code. Example prompts (not exhaustive) are 'implement this Figma design', 'build this screen from Figma', 'turn this Figma into code', 'design to code'. This skill provides critical instructions and steps to the agent on how to correctly implement Figma designs in code and must NOT be skipped.

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Build a cursor trail whose spacing stays constant at any hand speed, by emitting motes per unit of distance travelled rather than on a timer, so a flick draws the same continuous ribbon as a crawl instead of breaking into scattered dots. Covers sub-segment placement, the ring-buffer ordering trap, the idle breath a distance emitter needs, anchoring the trail to the screen in a 3-D scene, scaling scatter against the plane it hangs on, coasting instead of stopping dead, touch and reduced-motion fallbacks, and why moving the emitter to a DOM overlay to raise its z-index costs more than it buys. Use for cursor wisps, pointer sparks, embers, magic trails, comet tails, plankton, dust, or any mote trail that must stay legible however fast the hand moves.

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Interview the user for the raw material their posts are made of. Builds a lasting Story Bank of roles, numbers, turning points, scars and positions, or runs a focused interview that turns one topic into a post spine. Use when a draft has nothing concrete to draw on, or the user says interview me. Not for learning how they write (use linkedin-humanizer --mode profile).

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Recommend the right TRES Finance report for any user question. Trigger this skill whenever a user asks which report to use, what report contains certain data, how to get specific information out of TRES, or compares two reports. Also trigger when the user describes a goal (e.g. "month-end close", "auditor needs proof", "tax filing", "reconciliation") without naming a specific report. Trigger phrases include "which report", "what report", "how do I export", "where can I find", "I need a report for", "what's the difference between", "best report for", "auditor asked for", or any question about getting data out of TRES Finance.

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Analyze a blockchain transaction by its hash using TRES Finance MCP and generate a visual flow diagram showing all asset movements, followed by a plain-language explanation of what happened. Use this skill whenever the user provides a transaction hash and asks to "analyze", "explain", "visualize", "diagram", "show me what happened in", or "tell the story of" a transaction. Also trigger when the user pastes a tx hash (long hex string starting with 0x) and asks anything about it. Always use this skill for transaction hash analysis — do not attempt to answer from memory alone.

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Read when the user asks what OpenKnowledge is, wants to install it on a repository, wants to open or preview a single markdown file that is not part of an OpenKnowledge project, wants to share an OpenKnowledge project with collaborators, asks whether OpenKnowledge supports a particular capability, or asks how `ok init` / `ok cowork` / OK Desktop set up a project. Do NOT load to perform OpenKnowledge reads/writes — the runtime guidance for editing markdown inside an initialized OK project ships as a separate project-local skill installed into each detected agent's skills dir (for example `.claude/skills/open-knowledge/`) whenever `ok init` runs.

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International / multilingual SEO audit focused on hreflang correctness: missing or broken return tags, wrong language/region codes, missing x-default, self-referencing errors, hreflang vs canonical conflicts, and inconsistent signals across HTML head, HTTP headers and XML sitemap. Also reviews URL structure (ccTLD vs subdirectory vs subdomain), language targeting and geo signals. Use whenever the user runs a multi-language or multi-region site and asks about hreflang, international or multilingual SEO, the wrong-language page showing in search, or duplicate content across country versions. Trigger on: "hreflang", "international SEO", "multilingual SEO", "multi-region", "wrong language in Google", "x-default", "ccTLD vs subdirectory", "geo targeting", "language targeting", "my English page shows for Thai users", "th/en page ranking in the wrong country", or any cross-language/cross-country ranking question. For full-site audits use /seo-analysis; for a single URL use /seo-page.

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Review how the lean-ctx ctx_* MCP tools performed in the current session and file upstream issues for confirmed problems. Use when the user asks to review/audit lean-ctx tool performance, says "lean-ctx review", asks why ctx_* tools failed or fell back to native tools, or wants to report a lean-ctx bug.

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Verify that a watermark-removal or "humanizer" step actually removed a statistical text watermark, by measuring a sample whose key the user holds. Use after any tool, script or service claims to have cleaned a text, or when the user asks whether such a tool works, which one to trust, or how to test one. Reports a detector score against fixed thresholds plus what the run cost in meaning, facts, verbatim overlap and length. This skill only measures and never removes a mark.

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Use this skill for hands-on DOCA GPI programming — wiring a GPU-Packet-Initiator context so a CUDA kernel drives RDMA queues directly from GPU memory without host CPU mediation. Covers picking GPI vs doca-gpunetio, the doca_gpi / domain / channel object model, the GPU-side handle handoff (doca_gpu_gpi_channel*), attaching GPU memory to a GPI domain, the domain and channel attribute objects, and debugging DOCA_ERROR_* from doca_gpi_* calls. Trigger even when the user does not explicitly mention "DOCA GPI" — implicit phrasings include "my CUDA kernel needs to post RDMA directly from GPU memory", "DOCA_ERROR_* from doca_gpi_gpu_channel_get", "how do I hand a GPU handle to my CUDA kernel", "how many channels can a GPI domain hold", or "GPU kernel driving RDMA without the host CPU on the path". Refuse and route elsewhere for the doca-gpunetio Send/Receive surface, the doca-rdma queue lifecycle, DPA-side initiation (doca-rdmi), or the CUDA programming model — those belong to other skills.

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Use this skill when the user is invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path — picking the active / passive device split, choosing workload-shape axes (burst, queue, completion threshold, workers, hash pipe algo, PSL tables), or reading Kops/sec iteration stats and the optional self-test. Trigger even when the user does not explicitly mention "doca_flow_dpa_perf" or "DPA Provider" — typical implicit phrasings include "how fast can the DPA program path-selector entries", "baseline rule-update rate on ConnectX-8", "tool reports zero ops on my BlueField", "self-test sentinel never shows on tcpdump", or "is my BlueField-2 DPA-capable". Refuse and route elsewhere for the host / DPU-CPU Flow path (doca-flow-perf), Flow pipeline tuning (doca-flow-tune), writing doca-flow / doca-dpa applications, or DOCA install — those belong to other skills.

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Analyze how liquid a stock is using Yahoo Finance data (yfinance): bid-ask spreads, volume and dollar volume (ADTV), top-of-book and options depth, square-root market impact and slippage estimates, turnover ratio, and Amihud illiquidity, rolled into a liquidity grade. Use this skill whenever the user asks about liquidity or trading costs — how easily a position can be entered or exited, what a large order would do to the price, spread or execution-cost estimates, order book depth, volume patterns, or liquidity comparisons — especially for small caps, penny stocks, and thinly traded names.

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Introspect, explain, and improve Ralph Orchestrator using its published llms.txt doc map. Use this skill whenever the user asks questions about Ralph's behavior, wants to understand how a Ralph internal works (event loop, hats, memories, tasks, backends, presets), debug an unfamiliar failure mode, or propose a code change to the ralph-orchestrator repo. The skill teaches the agent to discover authoritative answers from the live docs via llms.txt before guessing, and to scope improvements through the published architecture rather than the local checkout alone.

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List, find, and show Azure resources across subscriptions or resource groups. Handles prompts like "list the websites in my subscription", "list my web apps", "show my app services", "list virtual machines", "list my VMs", "show storage accounts", "find container apps", and "what resources do I have". USE FOR: list websites, list web apps, list app services, show websites in subscription, resource inventory, find resources by tag, tag analysis, orphaned resource discovery (not for cost analysis), unattached disks, count resources by type, cross-subscription lookup, and Azure Resource Graph queries. DO NOT USE FOR: deploying/changing resources (use azure-deploy), cost optimization (use cost-optimization from the optional azure-cost plugin), or non-Azure clouds.

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Use when the user asks to "codify how our founder sounds", "build a founder voice dossier", or "set our content pillars"; runs an 80%-extraction interview over the user's OWN posts, emails, and decks (never competitor scraping, never an invented persona) and produces the versioned voice record — a per-platform register map (incl. 小红书/微信公众号), banned phrases, per-context disclosure lines (the ECHO C2 upstream), a few-shot bank built exclusively from own posts, and 3-5 content pillars with Estimated %-allocations — submitted via memory/events/channels.ndjson for channel-registry to store as voice-dossier.md, the record every Craft-phase skill reads first. Not for the brand-level voice canon — use brand-language-codifier; not for audience/persona research — use audience-mapper. 声音档案/品牌语气/创始人语气/内容支柱/披露声明

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Use when the user asks to "plan my launch tier", "how big should this launch be", or "build a launch risk register with kill criteria"; produces a tier decision (Tier 1 flagship all-channel / Tier 2 targeted / Tier 3 changelog-level), a launch-type declaration (new-product / feature / relaunch / partnership with co-marketing split), an effort calibration matrix (tier to channel intensity and asset scope), D0/W1/M1 KPI targets (labeled Estimated), a risk register (likelihood x blast-radius, owners, mitigations, kill criteria / rollback thresholds), and a T-8w to T+4w timeline skeleton. Not for picking the launch date or window — use launch-window-planner; not for creator-channel launch campaigns — use campaign-planner. 发布分级/发布类型/风险登记册/kill criteria

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Guides the end-to-end AWS resilience lifecycle integrating Resilience Hub v2, Fault Injection Service, and Application Recovery Controller. Covers the Define → Test → Operate workflow: from policy creation through failure mode assessment, to FIS experiment validation, to ARC operational controls. Applicable when the user wants a complete resilience strategy, needs to connect findings to experiments to controls, or is planning a resilience program. Also applicable for the meta question of whether marking NGRH findings as resolved is enough, whether they are "done" after resolving findings, or how to validate findings before resolving them. Not applicable for resolving or remediating a specific individual finding (see resilience-hub-failure-mode-assessment), or when a single service is explicitly named (e.g. "what FIS experiment should I run").

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Safely change and test untested codebases using Feathers' "Working Effectively with Legacy Code". Use when the user mentions "legacy code", "no tests", "untested codebase", "how do I test this", "seams", "characterization tests", "golden master", "sprout method", "afraid to change this code", "monster method", "dependency breaking", or "inherited a messy codebase". Also trigger when changing code without tests safely, getting a class under test when constructors, statics, or singletons block it, adding features to tangled modules, or planning incremental test coverage for an old codebase. Covers the legacy-code change algorithm, seams, characterization tests, sprout/wrap, and dependency-breaking techniques. For refactoring code that already has tests, see refactoring-patterns. For day-to-day code quality, see clean-code.

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