demand-shaping-designer

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Mis à jour18 juillet 2026 à 15:25

Design carbon-responsive experience tiers — gracefully reducing quality or deferring non-essential features when grid carbon intensity is high (eco modes, adaptive quality, deferred background work), the demand-shaping half of carbon-aware design. Use this skill whenever the user wants an eco mode, asks how the product itself can respond to dirty-grid hours, mentions demand shaping or carbon-aware UX, or wants the product experience itself to respond to grid conditions. Part of Lean Agentic AI Skills; emits a shaping design plus lean-findings.json.

Installation

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navveenb

navveenb/lean-agentic-ai-skills

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Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

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SKILL.md

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name

demand-shaping-designer

description

Demand Shaping Designer

Advisor skill — the other half of carbon-awareness. carbon-aware-scheduler moves when work runs; this skill designs how the product does less when the grid is dirty, moving I and E together. The canonical mental model: video calls dropping resolution under low bandwidth — same idea, carbon signal instead of network signal.

Subject type: emit subject.type: "architecture" in findings.

Design procedure

Inventory shapeable experience dimensions. For each product surface: quality tiers (media resolution/bitrate, generation quality, sync frequency, prefetch depth, animation richness, model tier for AI features), and deferrable work (non-urgent notifications, feed pre-computation, speculative prefetch, background enrichment).
Classify by user impact: invisible (prefetch depth, background enrichment — shape freely), degraded-but-acceptable (media quality step-down, less frequent refresh — shape with disclosure), and core value (never shape; list explicitly so the design has a floor).
Pick the signal and thresholds — same sources as carbon-aware-scheduler (references/grid-data-sources.md there): regional intensity with two or three bands (normal / elevated / high), hysteresis so the experience doesn't flap at band edges, and a user's region resolved coarsely (privacy note: country/grid-zone granularity suffices; never precise location for this).
Design the transparency layer. Silent shaping of visible dimensions erodes trust; the design includes user-facing framing (an eco indicator, a setting to opt out or always-on) — eco mode as a feature users can be proud of, not a stealth downgrade. Default-on is defensible only for the invisible tier.
Emit the shaping matrix (dimension × band × behavior), the findings file (unshaped-but-shapeable dimensions as findings), and the measurement hook: shaped-vs-unshaped served ratios are countable and should be dashboarded.

Trade-offs to always name

User experience is the product — shaping visible dimensions trades satisfaction, and the classification step exists to protect that. Engineering complexity of tiered behavior is real (test matrix grows). Rebound honesty: shaping reduces intensity-weighted consumption; it is not license to grow baseline usage and call it green.

Not this skill's job

Time-shifting backend jobs (carbon-aware-scheduler), the media encoding ladder itself (video-streaming-optimizer), choosing regions (region-selector).

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name

demand-shaping-designer

description

Demand Shaping Designer

Subject type: emit subject.type: "architecture" in findings.

Design procedure

Inventory shapeable experience dimensions. For each product surface: quality tiers (media resolution/bitrate, generation quality, sync frequency, prefetch depth, animation richness, model tier for AI features), and deferrable work (non-urgent notifications, feed pre-computation, speculative prefetch, background enrichment).
Classify by user impact: invisible (prefetch depth, background enrichment — shape freely), degraded-but-acceptable (media quality step-down, less frequent refresh — shape with disclosure), and core value (never shape; list explicitly so the design has a floor).
Pick the signal and thresholds — same sources as carbon-aware-scheduler (references/grid-data-sources.md there): regional intensity with two or three bands (normal / elevated / high), hysteresis so the experience doesn't flap at band edges, and a user's region resolved coarsely (privacy note: country/grid-zone granularity suffices; never precise location for this).
Design the transparency layer. Silent shaping of visible dimensions erodes trust; the design includes user-facing framing (an eco indicator, a setting to opt out or always-on) — eco mode as a feature users can be proud of, not a stealth downgrade. Default-on is defensible only for the invisible tier.
Emit the shaping matrix (dimension × band × behavior), the findings file (unshaped-but-shapeable dimensions as findings), and the measurement hook: shaped-vs-unshaped served ratios are countable and should be dashboarded.

Trade-offs to always name

Not this skill's job

Time-shifting backend jobs (carbon-aware-scheduler), the media encoding ladder itself (video-streaming-optimizer), choosing regions (region-selector).