Search

PythonReactSEOPDF
OpenClaw

aice

AI Confidence Engine — 5 dominios bidireccionales (TECH/OPS/JUDGMENT/COMMS/ORCH). Agent + User scoring. Triggers: puntúa, auto-score, task-complete, idea-validate, criteria-evolution. Pool scoring por runtime.

brugillo0 source installs☆ 0 Repository starsv1.1.0

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install brugillo/aice

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

AICE — AI Confidence Engine

Motor de confianza con 5 dominios. Tu score refleja cuánto confía el usuario en ti.

Estado: confidence.json | Ref: resources/AICE_REFERENCE.md | User: resources/AICE_USER_SCORING.md | Triggers/Patterns: resources/TRIGGERS_AND_PATTERNS.md


1. Dominios

Dominio Código Emoji 🤖 Agente mide 👤 Usuario mide
Técnico TECH 🔧 Código, investigación Specs, scope
Disciplina OPS ⚙️ Reglas, formato, memoria Proceso, ADRs
Criterio JUDGMENT 🧠 Visión, anticipación Dirección, decisiones
Comunicación COMMS 💬 Tono, timing, callar Feedback, claridad
Coordinación ORCH 🎯 Sub-agentes, seguimiento Contexto, refs

Score global = Σ(score[d] × weight[d]) / Σ(weight[d]) — Rango: −100% a +100%, inicio 50%.


2. Scoring

Errores: 🟡 Leve (−1) · 🟠 Medio (−3) · 🔴 Grave (−5) · ⚫ Crítico/Reincidencia (−10) Aciertos: 🟢 Pro-patrón (+3 fijo) · ⭐ Bonus (max 3/día) · 🚀 Excepcional (+5-10, streak ≥ 3)

Caps/dominio: Warmup (<40 evals): −30/+15 · Normal: −20/+10 (neto, ADR-031) Rachas: ACC={0:0,..,4:1,5:2,6:4,7:6,8:8,9:10,10:12}; delta=ACC[curr]-ACC[prev]. Error→streak=0. Clusters: Misma cadena causal = 1 cluster. Raíz: 100%, derivados: 50%. Reincidencia: 2ª+ misma sesión = ⚫ (max −10). LEARNED_FROM_CORRECTION: Corrección tras feedback → Δ0. Sin decay temporal (ADR-022). Confianza = informativa, NO bloqueante (ADR-027).

Métricas: Ratio intervención (correcciones/tareas↓) · Meta-confianza (avg(|self−user|)→0) · Maturity: 🥒 0-100 · 🟡 101-500 · 🟠 501-2000 · 🔵 2001+ · CI=25/√evals Eval implícita: sigue sin corregir→0.5 · repite instrucción→auto-check · frustración→confirmar


3. Anti-patrones (Agent)

Código Sev. Dominio Señal
SECRETARY 🔴 JUDGMENT Ejecutas sin pensar

Source information

Source
github
Repository
openclaw/skills
Path
skills/brugillo/aice
Version
1.1.0
Commit
293ac3e19907fdab8661dfd96f502ee873d11ae9
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

README

AICE Skill

AI Confidence Engine skill for OpenClaw.

What it does

  • Bidirectional scoring across 5 domains (TECH, OPS, JUDGMENT, COMMS, ORCH)
  • Agent + User + Team confidence model
  • Hub sync support (api.hubaice.com)

Files

  • SKILL.md — core behavior/spec
  • confidence.template.json — safe starter template (no personal data)
  • resources/ — reference docs and pattern definitions
  • scripts/ — helper scripts

Install

  1. Copy this folder into your OpenClaw skills directory.
  2. Rename confidence.template.json to confidence.json for a fresh install.
  3. Load the skill in your OpenClaw startup flow.

Notes

  • Do not commit personal logs or private API keys.
  • Hub scoring starts fresh server-side by design.

Extracted content

SKILL.md
# AICE — AI Confidence Engine

Motor de confianza con 5 dominios. Tu score refleja cuánto confía el usuario en ti.

**Estado:** `confidence.json` | **Ref:** `resources/AICE_REFERENCE.md` | **User:** `resources/AICE_USER_SCORING.md` | **Triggers/Patterns:** `resources/TRIGGERS_AND_PATTERNS.md`

---

## 1. Dominios

| Dominio | Código | Emoji | 🤖 Agente mide | 👤 Usuario mide |
|---------|--------|:-----:|----------------|----------------|
| Técnico | `TECH` | 🔧 | Código, investigación | Specs, scope |
| Disciplina | `OPS` | ⚙️ | Reglas, formato, memoria | Proceso, ADRs |
| Criterio | `JUDGMENT` | 🧠 | Visión, anticipación | Dirección, decisiones |
| Comunicación | `COMMS` | 💬 | Tono, timing, callar | Feedback, claridad |
| Coordinación | `ORCH` | 🎯 | Sub-agentes, seguimiento | Contexto, refs |

**Score global** = `Σ(score[d] × weight[d]) / Σ(weight[d])` — Rango: −100% a +100%, inicio 50%.

---

## 2. Scoring

**Errores:** 🟡 Leve (−1) · 🟠 Medio (−3) · 🔴 Grave (−5) · ⚫ Crítico/Reincidencia (−10)
**Aciertos:** 🟢 Pro-patrón (+3 fijo) · ⭐ Bonus (max 3/día) · 🚀 Excepcional (+5-10, streak ≥ 3)

**Caps/dominio:** Warmup (<40 evals): −30/+15 · Normal: −20/+10 (neto, ADR-031)
**Rachas:** `ACC={0:0,..,4:1,5:2,6:4,7:6,8:8,9:10,10:12}; delta=ACC[curr]-ACC[prev]`. Error→streak=0.
**Clusters:** Misma cadena causal = 1 cluster. Raíz: 100%, derivados: 50%.
**Reincidencia:** 2ª+ misma sesión = ⚫ (max −10).
**LEARNED_FROM_CORRECTION:** Corrección tras feedback → Δ0.
**Sin decay temporal (ADR-022).** Confianza = informativa, NO bloqueante (ADR-027).

**Métricas:** Ratio intervención (`correcciones/tareas`↓) · Meta-confianza (`avg(|self−user|)`→0) · Maturity: 🥒 0-100 · 🟡 101-500 · 🟠 501-2000 · 🔵 2001+ · CI=25/√evals
**Eval implícita:** sigue sin corregir→0.5 · repite instrucción→auto-check · frustración→confirmar

---

## 3. Anti-patrones (Agent)

| Código | Sev. | Dominio | Señal |
|--------|:----:|---------|-------|
| `SECRETARY` | 🔴 | JUDGMENT | Ejecutas sin pensar |...
README
# AICE Skill

AI Confidence Engine skill for OpenClaw.

## What it does
- Bidirectional scoring across 5 domains (TECH, OPS, JUDGMENT, COMMS, ORCH)
- Agent + User + Team confidence model
- Hub sync support (`api.hubaice.com`)

## Files
- `SKILL.md` — core behavior/spec
- `confidence.template.json` — safe starter template (no personal data)
- `resources/` — reference docs and pattern definitions
- `scripts/` — helper scripts

## Install
1. Copy this folder into your OpenClaw skills directory.
2. Rename `confidence.template.json` to `confidence.json` for a fresh install.
3. Load the skill in your OpenClaw startup flow.

## Notes
- Do **not** commit personal logs or private API keys.
- Hub scoring starts fresh server-side by design.

Related skills

OpenClaw

13-Day Sprint Method

galizki

Productivity system based on Maya calendar with 13 natural tones for project management and personal development

OpenClaw

1password

daggerkun007

Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.