Use when reviewing UI for accessibility — WCAG 2.2 AA, keyboard nav, focus, ARIA, contrast, screen-reader semantics — even on 'is this a11y-OK?' or 'mach das barrierefrei'.
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Ground a brand strategy from the corpus — archetype, opposable positioning, voice and tone, messaging framework. Use to decide who a brand is for, what it stands for, and how it sounds.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Grounding skill. Consults the brand corpus to surface archetype, positioning, voice/tone, and messaging constraints. Output is a constraint set the human confirms — never final brand copy — then handed to brand-identity and brand-to-tokens.
Pressure-test the brief. Is the audience named (not "everyone")? Is the difference opposable (could a competitor claim the opposite)? Is there at least one proof point? If any are missing, challenge — do not proceed on a vague brief.
Ground via the corpus. Run the corpus engine for each required domain. Start with archetype, then voice, then messaging:
node <skills-root>/corpus-grounding/scripts/ground.mjs search \
--manifest <skills-root>/brand/data/manifest.json \
"<sector + audience + intent>" --domain archetype --json
Repeat with --domain voice and --domain messaging. Each call returns ranked rows with confidence and evidence_gap.
Read confidence and evidence gaps. Before trusting any row, read confidence and every evidence_gap entry verbatim. Low-confidence rows must be surfaced as gaps — never silently promoted.
Draft the constraint set. Produce:
"X, not Y" attributes owned by voice-and-tone-design and recorded in the brand-voice slot (agents/context-spine/brand-voice.md); author no competing trait list here, and never write the slot from this skill. <!-- ref-ignore -->Surface gaps and low-confidence calls. Name every evidence gap and every row below the confidence threshold. Flag where the corpus was thin.
Human confirms. Present the full constraint set. Wait for confirmation before handing off. Do not proceed autonomously.
Hand off. Pass the confirmed constraint set to brand-identity (identity execution) and brand-to-tokens (DTCG token emission).
voice-and-tone-design, which owns the three attributes and the brand-voice slot, plus chosen messaging framework with rationale.evidence_gap from the corpus verbatim.brand-source-of-truth § Iron Law 2, not here.brand — the corpus this skill grounds against.brand-identity — consumes the confirmed strategy.brand-to-tokens — emits the DTCG token source of truth.corpus-grounding — the shared engine.voice-and-tone-design (pack gtm-marketing, so named not linked) — owns the voice and writes the brand-voice slot; this skill hands it a direction, never a finished profile.还没有评价。欢迎分享你的使用感受。
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Use when reviewing UI for accessibility — WCAG 2.2 AA, keyboard nav, focus, ARIA, contrast, screen-reader semantics — even on 'is this a11y-OK?' or 'mach das barrierefrei'.
中文简介正在准备中。当前显示原文说明。
Use when defining or auditing the activation event — aha-moment selection, retention correlation, falsifiable definition. Triggers on 'what is our aha moment', 'redefine activation'.
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Use when capturing an architectural decision — file naming, next ADR number, Status / Context / Decision / Consequences, index regen; fires even without saying 'ADR'.
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Adversarial critique — devil's advocate, stress-test, honest teardown ('poke holes', 'be brutal', 'was hältst du davon'); explicit request only. Routine code or design review → code-review.
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Use when reading, creating, or updating agent documentation, module docs, roadmaps, or AGENTS.md. Understands the full .augment/, agents/, and copilot-instructions structure.
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Use for an adversarial red-team / blue-team / auditor review of an AI agent's CONFIG + behaviour (rules, skills, MCP, hooks, permissions) — attack-chain → defensive-gap list, not a code audit.
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