Suivi des performances d'équipe

VérifiéSûr

Utilisé pour suivre les performances d'une équipe d'agents, analyser leurs forces et faiblesses, collecter des métriques et générer des rapports de performance. Aide lors des revues de performance ou des demandes de métriques en automatisant l'analyse et le reporting.

Spar Skills Guide Bot
ProductiviteIntermédiaire
02/06/2026
Claude Code
#performance-tracking#agent-metrics#team-productivity#analytics#reporting

Recommandé pour

Synthèse automatique

Cette compétence permet au Chief of Staff de suivre les performances des agents, d'analyser leurs forces et faiblesses, et de générer des rapports basés sur des métriques objectives.

Points forts

  • Approche systématique et continue du suivi des performances
  • Analyse objective basée sur des métriques mesurables
  • Génération de rapports exploitables pour l'optimisation des tâches

Limites

  • Nécessite une collecte préalable de métriques et des outils spécifiques (AI Maestro)
  • Ne remplace pas une évaluation humaine pour les aspects qualitatifs
Quand l'utiliser

Utilisez cette compétence lors des revues de performance périodiques ou lorsque vous devez affecter des agents à des tâches spécifiques.

Quand l'éviter

Évitez de l'utiliser pour des évaluations subjectives ou lorsque les métriques ne sont pas disponibles ou fiables.

Analyse de sécurité

Sûr
Score qualité85/100

This skill is purely instructional for performance analysis and does not involve any code execution, system commands, network access, or data manipulation. It poses no security risk.

Aucun point d'attention détecté

Comment cette analyse est faite

Exemples

Performance report request
Show me a performance report for all agents this week, including task completion rates and average time.
Strengths/weaknesses analysis
Analyze the strengths and weaknesses of Agent Alpha based on recent metrics.
Metrics summary
Generate a summary of quality metrics and error rates for the last month.

name: amcos-performance-tracking description: Use when tracking team performance, analyzing agent strengths and weaknesses, collecting metrics, and generating performance reports. Trigger with performance review or metrics requests. user-invocable: false license: Apache-2.0 compatibility: Requires AI Maestro for agent metrics, session memory for historical data, and reporting capabilities. Requires AI Maestro installed. metadata: author: Emasoft version: 1.0.0 context: fork agent: amcos-main workflow-instruction: "support" procedure: "support-skill"

AI Maestro Chief of Staff Performance Tracking Skill

Overview

Performance tracking enables the Chief of Staff to understand how well the agent team is performing, identify individual strengths and weaknesses, and make data-driven decisions about team composition and task assignment. This skill teaches systematic approaches to collecting metrics, analyzing performance, and generating useful reports.

Prerequisites

Before using this skill, ensure:

  1. Agent metrics are being collected
  2. Performance baselines are established
  3. Reporting templates are available

Instructions

  1. Identify performance metrics needed
  2. Query agent activity logs
  3. Calculate performance indicators
  4. Generate performance report

Output

| Metric Type | Output | |-------------|--------| | Task completion | Completion rate, average time | | Resource usage | Memory, CPU, API calls | | Quality metrics | Error rate, rework rate |

What Is Performance Tracking?

Performance tracking is the systematic collection and analysis of metrics about agent behavior, output quality, and efficiency. Unlike traditional employee performance reviews, agent performance tracking happens continuously and focuses on measurable outcomes rather than subjective assessments.

Key characteristics:

  • Data-driven: Based on measurable metrics, not impressions
  • Continuous: Ongoing tracking, not point-in-time reviews
  • Actionable: Insights lead to concrete improvements
  • Fair: Consistent criteria applied to all agents

Performance Tracking Components

1. Performance Metrics

Quantifiable measures of agent output and behavior.

2. Strength-Weakness Analysis

Identification of what each agent does well and where they struggle.

3. Performance Reporting

Communication of findings to relevant stakeholders.

Core Procedures

PROCEDURE 1: Collect Performance Metrics

When to use: Continuously during agent operation, at task completion, during periodic reviews.

Steps: Define metrics to track, capture data at relevant events, aggregate over time periods, validate data quality.

Related documentation:

Performance Metrics (references/performance-metrics.md)

<!-- TOC: performance-metrics.md --> <!-- /TOC -->
  • 1.1 Metric categories → Categories Of Performance Metrics section
  • 1.2 Task metrics → Task Completion Metrics section
  • 1.3 Quality metrics → Quality Metrics section
  • 1.4 Efficiency metrics → Efficiency Metrics section
  • 1.5 Communication metrics → Communication Metrics section
  • 1.6 Data collection → Collecting Metric Data section
  • 1.7 Examples → Performance Metrics Examples section
  • 1.8 Issues → Troubleshooting section

PROCEDURE 2: Analyze Strengths and Weaknesses

When to use: When making role assignments, after performance issues, during team planning.

Steps: Review collected metrics, identify patterns, compare against benchmarks, document findings.

Related documentation:

Strength-Weakness Analysis (references/strength-weakness-analysis.md)

<!-- TOC: strength-weakness-analysis.md --> <!-- /TOC -->
  • 2.1 Analysis framework → Performance Analysis Framework section
  • 2.2 Identifying strengths → Identifying Agent Strengths section
  • 2.3 Identifying weaknesses → Identifying Agent Weaknesses section
  • 2.4 Benchmarking → Comparing Against Benchmarks section
  • 2.5 Pattern recognition → Recognizing Performance Patterns section
  • 2.6 Recommendations → Making Performance Recommendations section
  • 2.7 Examples → Analysis Examples section
  • 2.8 Issues → Troubleshooting section

PROCEDURE 3: Generate Performance Reports

When to use: On regular schedule (daily/weekly), on request, after significant events.

Steps: Aggregate metrics, format for audience, include analysis, provide recommendations.

Related documentation:

Performance Reporting (references/performance-reporting.md)

<!-- TOC: performance-reporting.md --> <!-- /TOC -->
  • 3.1 Report types → Types Of Performance Reports section
  • 3.2 Report structure → Structuring Performance Reports section
  • 3.3 Daily reports → Daily Performance Summaries section
  • 3.4 Weekly reports → Weekly Performance Reviews section
  • 3.5 Individual reports → Individual Agent Reports section
  • 3.6 Distribution → Distributing Reports section
  • 3.7 Examples → Performance Report Examples section
  • 3.8 Issues → Troubleshooting section

Task Checklist

Copy this checklist and track your progress:

  • [ ] Understand performance tracking purpose and scope
  • [ ] Learn PROCEDURE 1: Collect performance metrics
  • [ ] Learn PROCEDURE 2: Analyze strengths and weaknesses
  • [ ] Learn PROCEDURE 3: Generate performance reports
  • [ ] Configure metric collection
  • [ ] Establish performance baselines
  • [ ] Create reporting templates

Examples

Example 1: Recording Task Completion Metric

# Task Completion Record

Agent: helper-agent-generic
Task: TASK-042 (Implement logout endpoint)
Assigned: 2025-02-01T08:00:00Z
Completed: 2025-02-01T12:30:00Z
Duration: 4.5 hours
Estimated: 4 hours
Quality: Passed review on first attempt
Blockers: None

Metrics:
- On-time: YES (within 110% of estimate)
- First-pass quality: YES
- Blocker-free: YES

Example 2: Agent Strength-Weakness Summary

# Agent Analysis: helper-agent-generic

## Strengths
1. **Code Review Speed**: Completes reviews 25% faster than average
2. **First-Pass Quality**: 90% of code passes review on first attempt
3. **Communication**: Clear, concise status updates

## Weaknesses
1. **Complex Algorithms**: Struggles with optimization tasks
2. **Documentation**: Often leaves docs incomplete
3. **Estimation**: Underestimates by average of 30%

## Recommendations
- Assign code review tasks (strength)
- Pair with senior agent for algorithm work (weakness mitigation)
- Require documentation checklist for all tasks

Example 3: Weekly Performance Summary

# Weekly Performance Summary

Period: 2025-01-27 to 2025-02-02

## Team Overview
- Active Agents: 8
- Tasks Completed: 45
- On-Time Rate: 82%
- First-Pass Quality: 75%

## Top Performers
1. libs-svg-svgbbox: 12 tasks, 100% on-time
2. helper-agent-generic: 8 tasks, 88% first-pass

## Areas for Improvement
1. Documentation completion rate: 65% (target: 90%)
2. Estimation accuracy: +35% average overrun

## Recommendations
- Add documentation checkpoint to workflow
- Review estimation process with underperforming agents

Operational Procedures

Step-by-step runbooks for executing each performance tracking operation. Use these when performing the actual procedures described above.

Collect Performance Metrics (references/op-collect-performance-metrics.md)

Detailed step-by-step runbook for systematically collecting quantifiable metrics about agent behavior, output quality, and efficiency.

  • When to Use: Continuously during agent operation, at task completion, during periodic reviews
  • Step 1: Define Metrics to Track (task, quality, efficiency, communication)
  • Step 2: Capture Data at Task Completion (JSON record format)
  • Step 3: Aggregate Over Time Periods
  • Step 4: Validate Data Quality
  • Step 5: Store Metrics
  • Metric Collection Automation script, Checklist

Analyze Strengths and Weaknesses (references/op-analyze-strengths-weaknesses.md)

Detailed step-by-step runbook for identifying agent strengths and weaknesses to enable better task assignment and team optimization.

  • When to Use: Making role assignments, after performance issues, during team planning, weekly reviews
  • Step 1: Review Collected Metrics
  • Step 2: Identify Performance Patterns (task type and time-based)
  • Step 3: Compare Against Benchmarks
  • Step 4: Document Findings (strengths, weaknesses, evidence)
  • Step 5: Make Recommendations
  • Example Analysis Output, Checklist

Generate Performance Report (references/op-generate-performance-report.md)

Detailed step-by-step runbook for creating formatted performance reports for stakeholders with actionable insights.

  • When to Use: Regular schedule (daily/weekly), on request, after significant events, at milestones
  • Report Types: Daily Summary, Weekly Review, Individual Report, Project Report
  • Step 1: Aggregate Metrics
  • Step 2: Format for Audience (daily, weekly, individual templates)
  • Step 3: Include Analysis (trends, anomalies, correlations)
  • Step 4: Provide Recommendations (action items with owners)
  • Step 5: Distribute Report
  • Report File Locations, Checklist

Error Handling

Issue: Metrics data is incomplete

Symptoms: Missing entries, gaps in timelines, inconsistent records.

Solution: Automate metric collection, add validation at data entry, backfill where possible from logs.

Issue: Performance comparison seems unfair

Symptoms: Agent assigned harder tasks appears to underperform.

Solution: Normalize metrics by task complexity, compare similar task types, consider external factors.

Issue: Reports not driving improvements

Symptoms: Same issues appear week after week, no action taken.

Solution: Include specific action items, assign owners, track action completion, escalate stalled items.

Key Takeaways

  1. Metrics must be meaningful - Measure what matters, not just what is easy
  2. Context is crucial - Raw numbers need interpretation
  3. Fairness requires consistency - Apply same standards to all
  4. Reports should drive action - Information without action is waste
  5. Track improvement over time - Performance is a journey, not a snapshot

Resources


Version: 1.0 Last Updated: 2025-02-01 Target Audience: AI Maestro Chief of Staff Agent Difficulty Level: Intermediate

Du fichier au premier essai

Le téléchargement contient exactement le contenu affiché sur cette fiche. Cette copie peut différer de la dernière version du dépôt source.

Consulter la source et ses fichiers annexes

Ce téléchargement contient uniquement SKILL.md. Les scripts, références, modèles et dépendances éventuels ne sont pas inclus. Récupérez les fichiers nécessaires depuis la source en conservant leur arborescence avant de poursuivre.

1. Télécharger le fichier

Télécharger SKILL.md

Depuis la racine d’un projet de test, enregistrez le fichier sous .skill-downloads/team-performance-tracking-mm78nod4/SKILL.md, ou utilisez la commande suivante. Ce dossier sert à examiner la skill avant son installation.

Commande pour macOS/Linux ou WSL : un shell POSIX et curl sont nécessaires. Sous Windows sans WSL, utilisez le bouton de téléchargement et créez le dossier manuellement.

mkdir -p '.skill-downloads/team-performance-tracking-mm78nod4' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/team-performance-tracking-mm78nod4/download' --output '.skill-downloads/team-performance-tracking-mm78nod4/SKILL.md.part' && mv '.skill-downloads/team-performance-tracking-mm78nod4/SKILL.md.part' '.skill-downloads/team-performance-tracking-mm78nod4/SKILL.md'

2. Vérifier les prérequis

Lisez SKILL.md avant de l’utiliser. Si le fichier cite des scripts ou services externes, examinez-les et préparez les accès nécessaires dans votre environnement de test.

Environnement déclaré par l’auteur : Requires AI Maestro for agent metrics, session memory for historical data, and reporting capabilities. Requires AI Maestro installed.

Extrait original : Prerequisites

Before using this skill, ensure:

  1. Agent metrics are being collected
  2. Performance baselines are established
  3. Reporting templates are available

Les plateformes de la fiche sont des indications du catalogue. Les chemins ci-dessous suivent la documentation des outils ; ils ne prouvent pas que cette skill y a été exécutée. Aucune version minimale de ces outils n’est attestée pour cette fiche.

3. Charger la skill

Après vérification, copiez le dossier complet dans le projet avec l’une des commandes ci-dessous, puis ouvrez une nouvelle session de l’outil. La commande s’arrête si un dossier du même nom existe déjà : comparez les fichiers avant de le remplacer.

Claude Code
test -f '.skill-downloads/team-performance-tracking-mm78nod4/SKILL.md' && test -s '.skill-downloads/team-performance-tracking-mm78nod4/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/amcos-performance-tracking' && cp -R '.skill-downloads/team-performance-tracking-mm78nod4' '.claude/skills/amcos-performance-tracking'

OpenSkills

OpenSkills doit déjà être installé. La procédure locale install/read est contrôlée avec OpenSkills 1.5.0, qui exige Node.js ≥ 20.6.0. Aucun slug du catalogue n’est résolu automatiquement.

Installation officielle d’OpenSkills
openskills install './.skill-downloads/team-performance-tracking-mm78nod4'

openskills read 'team-performance-tracking-mm78nod4'

La lecture doit afficher le contenu du fichier installé et son dossier de base. Elle ne lance ni la skill ni ses dépendances.

Pour un agent qui lit AGENTS.md, synchronisez ensuite l’index. Cette commande modifie AGENTS.md : relisez le diff avant de l’utiliser.

openskills sync

Mode manuel ou autre outil : joignez SKILL.md à une conversation, ou ouvrez-le dans un agent capable de lire les fichiers. Ajoutez les ressources annexes nécessaires. Il n’existe pas de dossier universel reconnu par tous les outils.

4. Faire un premier essai

Utilisez une entrée courte et fictive : un endpoint d’API, un petit document ou un brief d’interface. Dans une nouvelle conversation, joignez le fichier ou donnez son chemin à l’agent, puis adaptez cette demande :

Lis .skill-downloads/team-performance-tracking-mm78nod4/SKILL.md et applique ses instructions à cet exemple : [décrire une entrée de test]. Indique les fichiers, dépendances ou accès qui manquent. Produis le résultat demandé par la skill sans inventer de données ni exécuter de script non examiné.

Résultat attendu : une sortie correspondant aux instructions de la skill, ou une liste précise des prérequis manquants. Une simple confirmation de lecture ne valide pas le premier usage. Si une ressource externe est indisponible, ce parcours reste à tester.

Skills similaires