返回技能列表

test-team-coordination

pjt222
更新于 2 days ago
8 次查看
17
2
17
在 GitHub 上查看
aitestingdesign

关于

This skill runs predefined test scenarios against AI teams to evaluate their coordination patterns and performance. It observes team behaviors, checks acceptance criteria, and generates structured RESULT.md reports. Use it to validate team coordination, compare patterns on equivalent workloads, or establish performance baselines for team compositions.

快速安装

Claude Code

推荐
主要方式
npx skills add pjt222/agent-almanac -a claude-code
插件命令备选方式
/plugin add https://github.com/pjt222/agent-almanac
Git 克隆备选方式
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/test-team-coordination

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

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>-NNN auto-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 test
  • coordination-pattern — the expected pattern
  • team-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 testing
  • review-skill-format — validates individual skill format (this skill validates team coordination)
  • create-team — creates team definitions that this skill tests
  • evolve-team — evolves team definitions based on test findings
  • test-a2a-interop — similar testing pattern for A2A protocol conformance
  • assess-form — morphic assessment that opaque team lead uses internal

GitHub 仓库

pjt222/agent-almanac
路径: i18n/caveman/skills/test-team-coordination
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

相关推荐技能

content-collections

Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。

查看技能

polymarket

这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。

查看技能

creating-opencode-plugins

该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。

查看技能

sglang

SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。

查看技能