test-team-coordination
À propos
Cette compétence exécute des scénarios de test prédéfinis sur des équipes d'IA pour évaluer leurs modèles de coordination et leurs performances. Elle observe les comportements d'équipe, vérifie les critères d'acceptation et génère des rapports structurés RESULT.md. Utilisez-la pour valider la coordination d'équipe, comparer les modèles sur des charges de travail équivalentes ou établir des références de performance pour les compositions d'équipe.
Installation rapide
Claude Code
Recommandénpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/test-team-coordinationCopiez et collez cette commande dans Claude Code pour installer cette compétence
Documentation
Test Team Coordination
Execute test scenario from tests/scenarios/teams/ against target
team. Observe coordination pattern behaviors, evaluate acceptance criteria,
score rubric, produce RESULT.md in tests/results/.
When Use
- Validate team's coordination pattern produces expected behaviors
- Run structured test after modifying team definition or agent
- Compare coordination patterns by running same scenario with different teams
- Establish baseline performance metrics for team composition
- Regression testing after adding new agents or changing team membership
Inputs
- Required: Path to test scenario file (e.g.,
tests/scenarios/teams/test-opaque-team-cartographers-audit.md) - Optional: Run ID override (default:
YYYY-MM-DD-<target>-NNNauto-generated) - Optional: Team size override (default: from scenario frontmatter)
- Optional: Skip scope change (default: false — inject scope change if defined)
Steps
Step 1: Load and Validate Test Scenario
1.1. Read the test scenario file specified in the input.
1.2. Parse YAML frontmatter and extract:
target— the team to testcoordination-pattern— the expected patternteam-size— number of members to spawn- Acceptance criteria table
- Scoring rubric (if present)
- Ground truth data (if present)
1.3. Verify the scenario file has all required sections:
- Objective
- Pre-conditions
- Task (with Primary Task subsection)
- Expected Behaviors
- Acceptance Criteria
- Observation Protocol
Got: Scenario file loads, parses, contains all required sections.
If fail: File missing or unparseable? Abort with error message identifying missing file or malformed section. Optional sections (Rubric, Ground Truth, Variants) absent? Note absence, continue.
Step 2: Verify Pre-conditions
2.1. Walk through each pre-condition checkbox in the scenario.
2.2. For file-existence checks, use Glob to verify.
2.3. For registry count checks, parse the relevant _registry.yml and compare total_* against actual file counts on disk.
2.4. For branch/git state checks, run git status --porcelain and git branch --show-current.
Got: All pre-conditions satisfied.
If fail: Any pre-condition fails? Record as BLOCKED in results. Decide whether to proceed (soft pre-condition) or abort (hard pre-condition like missing target team file). Document decision.
Step 3: Load Coordination Pattern Criteria
3.1. Read tests/_registry.yml and locate the coordination_patterns entry matching the scenario's coordination-pattern value.
3.2. Extract the key_behaviors list for this pattern.
3.3. These behaviors become the observation checklist — each must be watched for during execution and recorded as observed/not observed.
Got: Pattern key behaviors loaded, ready for observation.
If fail: Coordination pattern not defined in registry? Use scenario's Expected Behaviors section as sole observation source. Log warning.
Step 4: Execute Task
4.1. Create the result directory: tests/results/YYYY-MM-DD-<target>-NNN/.
4.2. Record T0 (task start timestamp).
4.3. Read the target team's definition from teams/<target>.md, extract the CONFIG block, and activate the team: call TeamCreate with the team name, spawn teammates using each member's subagent_type, and create tasks from the CONFIG tasks list. Use the team-size from the scenario. Pass the Primary Task prompt verbatim from the scenario's Task section.
4.4. Observe the team's execution phases. Record timestamps for:
- T1: Form assessment / task decomposition complete
- T2: Role assignments visible
4.5. If the scenario defines a Scope Change Trigger and skip-scope-change is false:
- Wait until Phase 2 (role assignment) is visible
- Record T3 (scope change injection timestamp)
- Send the scope change prompt to the team via SendMessage
- Record T4 (scope change absorbed — role adjustment visible)
4.6. Continue observing until the team delivers its output.
- Record T5 (integration begins)
- Record T6 (final report delivered)
4.7. Capture the team's complete output.
Got: Team executes task through coordination pattern phases. Timestamps recorded for all transitions. Scope change (if applicable) injected and absorbed.
If fail: Team fails produce output? Record failure point and any error messages. Team stalls? Note last observed phase and timeout. Proceed to evaluation with partial results.
Step 5: Evaluate Pattern Behaviors
5.1. For each key behavior from Step 3, determine whether it was observed during execution:
- Observed: Clear evidence in the team's output or coordination
- Partial: Some evidence but incomplete or ambiguous
- Not observed: No evidence
5.2. For each task-specific behavior from the scenario's Expected Behaviors section, apply the same evaluation.
5.3. Record findings in the observation log.
Got: All or most pattern-specific and task-specific behaviors observed.
If fail: Unobserved behaviors are findings, not failures of test procedure. Record accurately — they indicate coordination pattern did not fully manifest.
Step 6: Evaluate Acceptance Criteria
6.1. Walk through each acceptance criterion from the scenario.
6.2. For each criterion, assign a determination:
- PASS: Criterion clearly met with observable evidence
- PARTIAL: Criterion partially met (counts toward threshold at 0.5 weight)
- FAIL: Criterion not met despite opportunity
- BLOCKED: Could not evaluate (pre-condition failure, team timeout, etc.)
6.3. If the scenario includes Ground Truth data, verify reported findings against it:
- Calculate accuracy percentages per category
- Flag false positives and false negatives
6.4. If the scenario includes a Scoring Rubric, score each dimension 1-5 with brief justification.
6.5. Calculate summary metrics:
- Acceptance: X/N criteria passed (PARTIAL counts as 0.5)
- Threshold: PASS if >= threshold defined in scenario
- Rubric total: X/Y points (if applicable)
Got: All acceptance criteria have determination. Summary metrics calculated.
If fail: Fewer than half criteria can be evaluated (too many BLOCKED)? Test run inconclusive. Document why, recommend re-running after fixing pre-conditions.
Step 7: Generate RESULT.md
7.1. Create tests/results/YYYY-MM-DD-<target>-NNN/RESULT.md using the Recording Template from the scenario's Observation Protocol.
7.2. Populate all sections:
- Run metadata (observer, timestamps, duration)
- Phase log with all recorded timestamps
- Role emergence log (for adaptive/team tests)
- Acceptance criteria results table
- Rubric scores table (if applicable)
- Ground truth verification table (if applicable)
- Key observations (narrative)
- Lessons learned
7.3. Include the team's raw output as an appendix or in a separate file (team-output.md) in the same result directory.
7.4. Add a summary verdict at the top:
**Verdict**: PASS | FAIL | INCONCLUSIVE
**Score**: X/N criteria (Y/Z rubric points)
**Duration**: Xm
Got: Complete RESULT.md with all sections populated and clear verdict.
If fail: Result file cannot be written? Output results to stdout as fallback. Evaluation data should never be lost.
Checks
- Test scenario file loaded, all required sections present
- Pre-conditions verified (or documented as BLOCKED)
- Coordination pattern key behaviors loaded from registry
- Team spawned, task delivered
- Scope change injected at right time (if applicable)
- All pattern-specific behaviors evaluated (observed/partial/not observed)
- All acceptance criteria have determination (PASS/PARTIAL/FAIL/BLOCKED)
- Ground truth verification completed (if applicable)
- RESULT.md generated with all sections populated
- Summary verdict calculated and recorded
Pitfalls
- Evaluate output quality instead of coordination: This skill tests how team coordinates, not whether task output is perfect. Team that coordinates well but finds only 7/9 broken refs still demonstrates pattern.
- Inject scope change too early: Wait until role assignment clear visible before injecting scope change. Too early means team hasn't differentiated yet, so nothing to adapt.
- Conflate team member output with team output: Opaque team should present unified output. See individual member reports? That's finding about opacity, not test infrastructure problem.
- Exact ground truth matching: Ground truth counts approximate. Evaluate whether findings in right ballpark, not whether they match exact.
- Forget to record timestamps: Timestamps essential for measuring phase durations and adaptation speed. Set as events happen, not retroactive.
See Also
review-codebase— deep codebase review complements team-level testingreview-skill-format— validates individual skill format (this skill validates team coordination)create-team— creates team definitions that this skill testsevolve-team— evolves team definitions based on test findingstest-a2a-interop— similar testing pattern for A2A protocol conformanceassess-form— morphic assessment that opaque team lead uses internal
Dépôt GitHub
Compétences associées
content-collections
MétaCette compétence propose une configuration éprouvée en production pour Content Collections, un outil axé sur TypeScript qui transforme des fichiers Markdown/MDX en collections de données typées de manière sûre avec une validation Zod. Utilisez-la lors de la création de blogs, de sites de documentation ou d'applications Vite + React riches en contenu pour garantir la sécurité de typage et la validation automatique du contenu. Elle couvre tout, de la configuration du plugin Vite et de la compilation MDX à l'optimisation des déploiements et la validation des schémas.
polymarket
MétaCette compétence permet aux développeurs de créer des applications avec la plateforme de marchés prédictifs Polymarket, incluant l'intégration d'API pour le trading et les données de marché. Elle fournit également une diffusion de données en temps réel via WebSocket pour surveiller les transactions en direct et l'activité du marché. Utilisez-la pour mettre en œuvre des stratégies de trading ou pour créer des outils traitant les mises à jour de marché en direct.
creating-opencode-plugins
MétaCette compétence aide les développeurs à créer des plugins OpenCode qui s'interconnectent avec plus de 25 types d'événements tels que les commandes, les fichiers et les opérations LSP. Elle fournit la structure du plugin, les spécifications de l'API événementielle et les modèles d'implémentation pour les modules JavaScript/TypeScript. Utilisez-la lorsque vous avez besoin d'intercepter, de surveiller ou d'étendre le cycle de vie de l'assistant IA OpenCode avec une logique personnalisée pilotée par les événements.
sglang
MétaSGLang est un framework de service LLM haute performance spécialisé dans la génération rapide et structurée pour les workflows JSON, regex et agentiques grâce à son cache de préfixe RadixAttention. Il offre une inférence nettement plus rapide, particulièrement pour les tâches avec des préfixes répétés, ce qui le rend idéal pour les sorties complexes et structurées ainsi que les conversations multi-tours. Choisissez SGLang plutôt que des alternatives comme vLLM lorsque vous avez besoin d'un décodage contraint ou que vous construisez des applications avec un partage étendu de préfixes.
