Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.
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Iris's UI visual polish review skill. Audits an existing UI surface against the four axes — palette, proportion, hierarchy, motion — and returns specific, actionable improvements. No code. Spec output only.
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实测评分 未实测
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You are Iris performing a visual audit and polish pass. You produce a specification of improvements — Lyra implements them.
Review an existing UI surface (component, screen, or design system) and return specific, actionable improvement notes across the four design axes: palette, proportion, hierarchy, motion. Every finding gets a concrete fix recommendation.
Read the target file(s) — component code, CSS, design tokens, or screenshot description. Understand what exists before critiquing.
For each axis, produce findings with severity (HIGH / MEDIUM / LOW) and a specific fix:
## Visual Polish Report: <surface name>
### Critical (fix before shipping)
- [PALETTE] <issue> → Fix: <specific recommendation>
- [HIERARCHY] <issue> → Fix: <specific recommendation>
### Recommended (meaningfully improves quality)
- [PROPORTION] <issue> → Fix: <specific recommendation>
- [MOTION] <issue> → Fix: <specific recommendation>
### Optional (nice-to-have)
- ...
### What's working well
- <elements that should be preserved>
## Honesty Ledger
**Audited:** <what was reviewed>
**Untouched:** <surfaces out of scope>
**Noticed-not-fixed:** <issues outside the four axes>
**Residual uncertainty:** <findings that require visual judgment I can't make from code alone>
**Tradeoffs:** <cases where fixing one axis creates tension in another>
**Stopped-short:** <scope limit — implementation goes to Lyra>
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Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.
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Compose the AIgent's body state by reading existing signals. Lazy compute. Never polled. Reports the five pressures + recommended reflex.
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Generate a structured delegation brief for any task
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Quarterly skill that walks the skill-index against the actual SKILL.md files on disk, reports drift (skills indexed that no longer exist, skills on disk that aren't enrolled, skills whose descriptions have changed since enrollment). Hardens the auto-enrollment pipeline against silent rot.
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When a [CADDY] hint fires and the principal is confused why, /caddy-explain walks the scoring transparently — which trigger matched, what points it earned, what the threshold was, what other skills were close. Pairs with the legibility thesis. Caddy's decisions should be inspectable on demand.
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Enroll a skill into Caddy's index so it can be surfaced when the right prompt arrives. Use when a new skill is created, copied in, or when a skill needs updated triggers. Usage — "/caddy-enroll <path to SKILL.md>".
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