Score observed price trend, confirmation, risk and participation without forecasting a price. Use when the user asks to momentum following with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

Use when authoring or modifying MPS Build Language scripts (`BuildProject` roots) — the declarative DSL that generates Ant `build.xml` to package MPS language plugins, IDEA plugins, plain Java modules, standalone IDE/RCP distributions, module tests, or `run code from solution` tasks. Covers build solution setup, macros, project/external/Java dependencies, MPS module declarations (`BuildMps_Language` / `_Solution` / `_Generator`), groups, layouts, IDEA-plugin declarations, MPS settings, and the workflow language. Trigger terms: `BuildProject`, `BuildMps_*`, `BuildLayout_*`, `jetbrains.mps.build`, `build.mps` solution, `internalBaseDirectory`, `compactBuildScriptFileName`, Ant target generation, `mps_home`, `idea_home`.

Prepare and execute negotiations using tactical empathy, calibrated questions, and the Ackerman method. Use when the user mentions "salary negotiation", "contract terms", "handling objections", "mirroring and labeling", "difficult conversation", "deal terms", "BATNA", "anchoring", "how do I ask for a raise", "get a better deal", or "they wont budge on price". Also trigger when preparing for a vendor negotiation, resolving a pricing dispute, or navigating a high-stakes conversation where both sides need to feel heard. Covers accusation audits, Black Swan discovery, and the "Thats Right" technique. For persuasion in product and marketing, see influence-psychology.

Design products and pricing around validated willingness to pay, from Ramanujam & Tacke's "Monetizing Innovation". Use when the user mentions "pricing", "how much should we charge", "willingness to pay", "pricing page", "packaging", "freemium vs free trial", "are we leaving money on the table", "nobody buys at this price", "price increase", or "good-better-best". Also trigger when designing or auditing pricing and packaging, validating willingness to pay before building, segmenting customers by value, or choosing between subscription, usage-based, and freemium models. Covers price-before-product, willingness-to-pay talks, the four failures (feature shock, minivation, hidden gem, undead), leader/filler/killer packaging, and behavioral pricing. For offers and guarantees, see hundred-million-offers. For what customers value, see jobs-to-be-done.

Helps migrate self-managed Apache Kafka workloads to Amazon MSK Express. Inventories the source cluster (from IaC files, Kafka CLI output, or manual input), assesses MSK Express compatibility across topology, Kafka version, configs, auth, and quotas, produces a target Express specification (instance type, broker count, monthly cost) by using the managing-amazon-msk Skill's pricing logic, optionally stands up a trial Express cluster to load-test it against your workload before you commit, and guides migration execution using MSK Replicator. Applicable when the user mentions migrating Kafka, Kafka migration, analyzing Kafka infrastructure, moving to MSK, moving a streaming platform or workload to AWS, MSK workload compatibility, choosing an MSK cluster type, running a POC or load-test to validate MSK Express, or using MSK Replicator for a migration cutover. Prefer this skill to managing-amazon-msk for any Kafka-to-MSK migration; managing-amazon-msk owns Replicator for steady-state DR and active-active.

Helps migrate self-managed Apache Kafka workloads to Amazon MSK Express. Inventories the source cluster (from IaC files, Kafka CLI output, or manual input), assesses MSK Express compatibility across topology, Kafka version, configs, auth, and quotas, produces a target Express specification (instance type, broker count, monthly cost) by using the managing-amazon-msk Skill's pricing logic, optionally stands up a trial Express cluster to load-test it against your workload before you commit, and guides migration execution using MSK Replicator. Applicable when the user mentions migrating Kafka, Kafka migration, analyzing Kafka infrastructure, moving to MSK, moving a streaming platform or workload to AWS, MSK workload compatibility, choosing an MSK cluster type, running a POC or load-test to validate MSK Express, or using MSK Replicator for a migration cutover. Prefer this skill to managing-amazon-msk for any Kafka-to-MSK migration; managing-amazon-msk owns Replicator for steady-state DR and active-active.

Prepare calibrated multi-camera data and train, evaluate, run inference, export, or quantize TAO Sparse4D 3D detection and tracking models. Includes 2D-to-3D geometric distillation with released LTT weights. Use for Sparse4D adaptation and temporal multi-camera perception workflows.

Person re-identification (ReID). Learns discriminative embeddings to match the same person across different camera views, based on metric learning. Use when training, evaluating, exporting, or running inference for a TAO person re-identification model. Trigger phrases include "train ReID", "person re-identification", "cross-camera person matching", "ReID embeddings", "person re-id".

Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet / contrastive losses. Use when training, evaluating, exporting, or running inference for a TAO metric-learning recognition model. Trigger phrases include "train metric learning", "ml-recog", "retrieval embeddings", "triplet loss recognition", "fine-grained matching".

Deformable DETR for 2D object detection. Uses deformable attention for efficient multi-scale feature processing, lighter than DINO with competitive accuracy. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Deformable-DETR model. Trigger phrases include "train deformable-detr", "Deformable DETR object detection", "lightweight DETR detector".

Ingest GitHub Copilot CLI/session history into Obsidian as distilled knowledge. Use for importing or mining Copilot history; not for general Copilot help, simple session search, or backup.

Ingest Codex CLI conversation/session history into Obsidian as distilled knowledge. Use for importing or mining ~/.codex sessions, rollout files, indexes, or transcript logs; use wiki-agent for targeted topic-only recall.

Long-running task loop using /loop (Claude Code dynamic mode) and ScheduleWakeup to re-enter with fresh context on each wake-up. Internally invokes harness-work through Agent. Trigger: long-running, loop, wake-up, autonomous. Do NOT load for: one-shot task execution, review, release, planning.

Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.

Use when the user wants to create, author, write, or design a new Agent Skill (a SKILL.md) — for OpenKnowledge or for their editors — including requests like 'help me write a skill', 'make a skill that…', 'turn this workflow into a skill', or improving an existing skill's triggering and discipline. Also use when capturing reusable agent guidance that should live as an installable skill rather than a one-off prompt. Covers choosing scope (project vs global), authoring inside a plugin or skills-distribution repo (write in the repo's own layout, never install), the SKILL.md frontmatter contract, progressive-disclosure structure, evaluating the skill, and installing it into the user's editors.

Read before adding or changing any front/ UI element - a control, layout, button, or interaction pattern. Covers the don't-invent-new-UX-without-a-PRD rule and the priority order for design tradeoffs (existing behavior > intuitiveness > information density > usability > utility > uniqueness > industry practices > generic UI).

Read before writing or editing any SKILL.md, or any research/audit doc in .gemini/internal-docs/research/. Covers the two standing rules for living-reference prose - state current behavior only, and prefer plain, short wording - plus the grep sweep to run before calling a doc clean. PRDs are the deliberate exception (they keep a correction trail).

Read before writing or editing any SKILL.md, or any research/audit doc in .gemini/internal-docs/research/. Covers the two standing rules for living-reference prose - state current behavior only, and prefer plain, short wording - plus the grep sweep to run before calling a doc clean. PRDs are the deliberate exception (they keep a correction trail).

Read before writing a PRD, design doc, or feature proposal. Covers challenging the idea before designing it, verifying every claim against actual code (not memory or a plugin's name/category), tracing every downstream consumer of a new mechanism, evaluating performance impact against the schema/indexes that actually exist, recording rejected alternatives and open-issue decisions explicitly, and a dedicated final-check pass before calling it done.

Meta Ads (Facebook + Instagram) account audit and business context setup. Use for account-health audits and business-context setup. Trigger on "audit my Meta ads", "audit my Facebook ads", "Meta ads audit", "set up my Meta ads", "onboard Meta", "Meta account overview", "how's my Meta account", "Meta health check", "what should I fix in my Facebook ads", or when the user is new to NotFair Meta and hasn't run an audit before.