AgentSkillsCN

querying-council-with-agents

执行代理增强型理事会查询,通过孵化并行的 Claude 子代理,每个子代理分别向提供商查询、评估响应质量、提出后续问题,并返回带有置信度评分与盲点分析的结构化见解。在使用 --agents 标志或检测到复杂架构决策时调用。

SKILL.md
--- frontmatter
name: querying-council-with-agents
description: Executes agent-enhanced council queries by spawning parallel Claude subagents that each query a provider, evaluate response quality, ask follow-up questions, and return structured insights with confidence ratings and blind spot analysis. Invoked when the --agents flag is used or when complex architectural decisions are detected.

Agent-Enhanced Council Execution

Use parallel Claude subagents for deeper analysis. Each subagent queries its provider, evaluates response quality, can ask follow-up questions, and returns structured insights.

Step 1: Determine Provider Details

For each selected provider, gather:

  • Provider name and script path: ${CLAUDE_PLUGIN_ROOT}/scripts/providers/{name}.sh
  • Model name: run bash ${CLAUDE_PLUGIN_ROOT}/scripts/query-council.sh --list-available or read the provider script defaults

Step 2: Spawn Provider Agents in Parallel

Launch ALL provider agents in a single message (multiple Agent tool calls) for parallel execution. Use run_in_background: true and subagent_type: "general-purpose" for each.

Agent prompt template: See agent-prompt-template.md for the full template. Read it and fill in {PROVIDER}, {SCRIPT_PATH}, and {QUESTION} for each agent.

CRITICAL: If a role was assigned to a provider (via --roles), prepend the role context to the question before passing it to the agent. Use the same role injection format as the standard flow.

CRITICAL: If file context was gathered (via --file or auto-context), include it in the question passed to each agent.

Step 3: Collect Results

As each background agent completes, you will be automatically notified. Wait for ALL agents to complete before proceeding to display.

If an agent fails or times out, note the failure and continue with available results.

Step 4: Display Results

For each provider, display the agent's structured analysis using this format:

code
## {EMOJI} {PROVIDER} ({MODEL}) — Agent Analysis

**Quality**: {quality} | **Confidence**: {confidence} | **Retried**: {retried}

### Key Recommendations
{recommendations}

### Unique Perspective
{unique_perspective}

### Blind Spots
{blind_spots}

---

<details>
<summary>Full {PROVIDER} Response</summary>

{full_response}

</details>

Provider emojis (ALWAYS use emoji + space):

  • 🟦 Gemini
  • 🔳 OpenAI
  • 🟥 Grok
  • 🟩 Perplexity

Step 5: Enhanced Synthesis

With pre-analyzed responses, generate a richer synthesis than the standard mode:

Confidence-Weighted Consensus

Weight agreement by each provider's confidence level. High-confidence agreement is stronger signal than low-confidence agreement.

Blind Spot Analysis

Cross-reference each provider's blind spots against other providers' recommendations. Flag risks that NO provider considered.

Divergence with Context

Where providers disagree, explain WHY they likely diverge (different assumptions, different optimization targets, different risk tolerance).

Recommendation

Synthesize the strongest approach, noting which providers support it and at what confidence level.

Step 6: Save Output

Save the complete output (all provider analyses + synthesis) to a cache file:

bash
mkdir -p .claude/council-cache

Write the output to .claude/council-cache/council-agents-{TIMESTAMP}.md where TIMESTAMP is the current Unix timestamp.

Tell the user:


Full agent analysis saved to .claude/council-cache/council-agents-{TIMESTAMP}.md

Error Handling

  • If a provider agent fails, show the error and continue with others
  • If ALL agents fail, report clearly and suggest falling back to standard mode
  • If only one provider was selected and its agent fails, suggest retrying without --agents