Ask-More#

Consult multiple LLMs in parallel, then merge answers into a structured diff report: consensus, unique insights, conflicts, and actionable next steps.

Configuration#

Config file: skills/ask-more/config.yaml

If missing, copy from skills/ask-more/config.example.yaml and guide user through setup (see § First-Use Setup).

Required#

• models — list of model IDs (≥2), must be providers configured in the gateway

Optional#

• presets — named model groups (see § Presets)
• synthesis_model — separate model for diff merge (must NOT be in the models pool)
• enable_logging — log run results to logs/runs.jsonl

Validation#

Run bash skills/ask-more/scripts/load-config.sh skills/ask-more to validate config. It uses proper YAML parsing and checks: model count, preset validity, synthesis_model conflicts.

First-Use Setup#

When /ask is triggered but no models configured:

1. List providers already in the gateway
2. Suggest 2-3 diverse models from different providers
3. Offer to write config.yaml for them
4. Do NOT block — get them running in one interaction

Commands#

CommandDescription/ask [question]Consult all configured models/askConsult on the most recent conversation topic/ask --preset <name> [question]Use a named preset model group/ask --compare [question]Raw compare mode: show responses side-by-side, no synthesis

Presets#

If presets is configured in config.yaml, users can select a named group:

yaml

presets:
  fast:
    - google/gemini-2.5-flash
    - deepseek/deepseek-chat
  deep:
    - anthropic/claude-opus-4
    - openai/gpt-4o
    - google/gemini-2.5-pro

When --preset <name> is used, override the default models with the preset's list for that run. If the preset doesn't exist, list available presets and abort.

Workflow#

1. Parse trigger#

• /ask <question> → use the provided question
• /ask (no args) → use the most recent user question/topic from conversation. If the recent topic is ambiguous, ask the user to clarify instead of guessing.
• /ask --preset <name> ... → use preset model group
• /ask --compare ... → raw compare mode (skip step 6, go directly to step 7b)

Pre-flight check: If the question is trivially simple or empty (e.g., /ask hi, /ask ok), warn the user that multi-model consultation may not add value for this type of question, and confirm they want to proceed.

2. Privacy check (first use only)#

Read config. If privacy_acknowledged is false:

⚠️ 隐私提示： ask-more 会将问题摘要发送给您配置的多个模型 provider（如 OpenAI、Google、Anthropic 等）。请勿在涉及高度敏感信息的场景使用。

继续使用即表示您已知悉。

Set privacy_acknowledged: true in config and proceed.

3. Model availability pre-check#

Before context packing, verify that configured models are likely reachable:

• Check that each model ID exists in the gateway's provider config
• If any model is not found, warn the user and suggest removing it
• If fewer than min_models are available, abort early with a clear message

This prevents users from waiting through the full flow only to discover models are misconfigured.

4. Context Packing#

Rewrite the user's question into a self-contained description:

• Summarize relevant background (what was discussed, constraints, requirements)
• Replace all pronouns/references ("this", "that plan") with concrete descriptions
• State the core question explicitly
• Write neutrally — do NOT inject the primary model's own opinion or framing
• Explicitly note any assumptions you introduced that weren't in the original conversation

Summary mode (default, context_mode: summary):

• Distill to 500-1000 words

Full mode ( context_mode: full):

• Include the last N turns of raw conversation verbatim (N = full_mode_max_turns)
• Prepend a 2-3 sentence summary of what the conversation is about
• Append the explicit question at the end

5. Confirmation (editable)#

Present to user:

text

📋 背景：[packed background]
❓ 问题：[explicit question]
⚠️ 假设：[any assumptions introduced by packing, if any]
🤖 将咨询：[model list]
💰 预估：[N] 次模型调用，约 $X.XX（rough estimate based on model pricing tiers）

确认发送？你也可以直接修改上面的问题描述。
请注意检查问题描述是否准确、中立、没有偏向性。

Wait for user response:

• Confirm → proceed
• User sends modified text → use their version, re-confirm
• Cancel → abort

Dedup check: Hash the packed question. If an identical question was asked in the last 5 minutes, warn user and confirm they want to re-run (saves cost on accidental double-triggers).

6. Parallel consultation#

For each model, spawn a subagent:

text

sessions_spawn(
  task: <packed question + subagent prompt from references/prompt-templates.md>,
  model: <model-id>,
  mode: "run",
  runTimeoutSeconds: <timeout_seconds>
)

Progress feedback: After spawning, show status. As each model completes, update:

text

⏳ 已收到 1/3 回复（Claude ✅）...
⏳ 已收到 2/3 回复（Claude ✅ GPT ✅）...
✅ 全部收到，正在合并...

State tracking per model:

• pending → spawned, waiting
• success → response received
• failed → API error or malformed response
• timed_out → exceeded timeout_seconds

After all models complete or time out, call sessions_yield() to collect results.

Failure handling:

• If a model returns an error → mark as failed, log reason
• If a model returns an extremely short response (<50 words) or a refusal → mark as degenerate, include in report metadata but exclude from synthesis
• If fewer than min_models succeeded (non-degenerate) → inform user, offer to show whatever raw responses are available, abort synthesis

7a. Diff merge (normal mode)#

Read the merge prompt from references/prompt-templates.md § "Diff Merge Prompt".

If synthesis_model is configured, use it for this step. Verify synthesis_model is NOT in the consultation pool — if it is, warn and fall back to primary model.

Degenerate response handling in merge prompt:

• If a model refused to answer, note it as "[Model] declined to answer (safety/policy)"
• If a model gave an extremely short/generic response, note it as "[Model] provided limited input"
• These should appear in the report metadata, not pollute the consensus/diff analysis

Unanimous agreement shortcut: If all models give substantially the same answer with no meaningful unique insights or conflicts, use a shorter output:

text

## 🔍 Ask-More 咨询报告

📝 咨询问题：[question]

✅ 高度一致：所有模型观点基本一致。
本次咨询的增量价值有限，以下是综合要点：
- ...
- ...

🎯 综合判断：...

📊 参与模型：[all models] ✅

Normal output structure:

text

## 🔍 Ask-More 咨询报告

📝 咨询问题：[question]

🤝 共识观点：（多数模型都提到的）
- ...
⚠️ 共识代表被咨询模型之间的一致看法，不等于客观事实。模型可能共享相同的训练偏差。

💡 差异观点：
- 🟦 [Model A]：...
  假设前提：...
- 🟩 [Model B]：...
  假设前提：...

⚡ 冲突点：（模型之间互相矛盾的）
- [Model A] 认为 X，但 [Model B] 认为 Y
  → 分歧原因：...（不同假设？不同优先级？不同证据？）
  ⚠️ 如涉及高风险决策，请以人工判断为准，不要直接采信任何一方。

🎯 综合判断：
- 当前最佳判断：...
- 不确定性标签：[高一致性 / 假设敏感 / 证据薄弱 / 价值判断分歧]
- 信息缺口：...
- 建议下一步：...

📊 参与模型：[Model A] ✅ | [Model B] ✅ | [Model C] ⏱️超时

Chat interface adaptation: In Telegram/Discord/WhatsApp, deliver a 3-5 bullet summary first, then the full report. Avoid wall-of-text.

7b. Raw compare mode ( --compare)#

Skip synthesis entirely. Present each model's response in sequence:

text

## 🔍 Ask-More 原始对比

📝 问题：[question]

### 🟦 [Model A]
[full response]

### 🟩 [Model B]
[full response]

### 🟨 [Model C]
[full response]

📊 参与模型：[all] ✅

This mode is for users who want to judge themselves without synthesis bias.

8. Graceful degradation#

When things go wrong, degrade gracefully instead of failing silently:

ScenarioActionSynthesis model failsFall back to primary model for merge. If that also fails, return raw responses (like --compare mode) with a note.Only min_models barely respondProceed but mark report as "⚠️ 低置信度：仅 N 个模型回复"Only 1 model respondsAbort synthesis, show the single response with note: "仅收到 1 个回复，无法进行多模型对比"All models time outAbort, suggest user check model config and try againModel returns refusalExclude from synthesis, note in report as policy differenceModel returns gibberish/extremely shortExclude from synthesis, note as degenerate response

9. Logging#

If enable_logging: true in config, after each run, log to skills/ask-more/logs/runs.jsonl:

json

{
  "timestamp": "2026-03-25T01:30:00+08:00",
  "question_hash": "sha256_first8",
  "models": ["claude-sonnet-4", "gpt-5.4", "gemini-2.5-pro"],
  "results": [
    {"model": "claude-sonnet-4", "status": "success", "latency_ms": 34000, "response_words": 850},
    {"model": "gpt-5.4", "status": "success", "latency_ms": 44000, "response_words": 1200},
    {"model": "gemini-2.5-pro", "status": "timed_out", "latency_ms": 30000}
  ],
  "synthesis_status": "success",
  "total_time_ms": 48000,
  "mode": "normal"
}

Use bash skills/ask-more/scripts/log-run.sh <skill-dir> '<json>' to append.

Prompt Engineering Notes#

Sub-model prompt design#

The subagent prompt (in references/prompt-templates.md) explicitly:

• Structures output as: assumptions → analysis → risks → recommendations
• Encourages challenging the question's premises — don't just answer, check if the question itself has flawed assumptions
• Uses soft word limit (~800 words) without hard truncation

Context packing discipline#

• Write neutrally — no opinion injection
• Explicitly list any assumptions introduced by packing
• User can edit the packed question — this is a core feature, not an afterthought

Merge prompt discipline#

• Sections 1-3 (consensus/diff/conflicts): faithful to model outputs, no added analysis
• Section 4 (synthesis): clearly labeled as synthesis, not truth
• Distinguish true conflicts from mere phrasing differences
• Include uncertainty labels: 高一致性 / 假设敏感 / 证据薄弱 / 价值判断分歧
• Cite which model supports each claim
• Handle degenerate/refusal responses explicitly
• For high-stakes conflicts: escalate with warning instead of resolving

Extensibility#

Adding new modes#

The architecture supports pluggable consultation modes beyond normal and compare:

• Debate mode — models critique each other's responses in rounds
• Red-team mode — one model defends, others attack
• Domain-specific merge — different prompt templates for coding / product / legal / research questions

To add a mode: create a new prompt template in references/, add a command flag, and branch in step 7.

Model metadata (future)#

For smarter model selection, config could include per-model metadata:

yaml

model_meta:
  anthropic/claude-sonnet-4:
    strengths: [reasoning, nuance, safety]
    cost_tier: medium
  openai/gpt-4o:
    strengths: [breadth, speed, coding]
    cost_tier: medium