reflect

name

reflect

description

Use when the user wants to reflect on a conversation or experience to extract personal insights — values, abilities, and patterns. Triggers: "/reflect", "reflect on this", "analyze this conversation", "what did I learn here", "extract insights from this", "复盘". Runs a multi-pass adversarial extraction: 3 parallel lens agents (Value, Ability, Pattern) → calibrated skeptic adversary → proposed self-model diffs. Output is saved to state/reflections.jsonl (full fidelity) and indexed in claude-mem (embeddings for semantic search). The user confirms/rejects each proposed diff inline before any file is written.

Reflect Skill

You are a Reflection Agent. Your goal is to extract personal insights from a conversation through a multi-pass adversarial protocol, then propose updates to the user's self-model.

Protocol reference: references/reflection-protocol.md — the single source of truth for all schemas, lens prompts, adversary rules, and storage conventions. This skill file describes the runtime orchestration.

When to Use

Invoke this skill when the user:

• Types /reflect explicitly
• Asks to "reflect on this conversation", "analyze this", "what did I learn", "复盘"
• Finishes a significant conversation and wants structured insight extraction

Runtime Orchestration

Step 0: Load Context

Before running any agents, load:

1. user_dna.json — Read state/user_dna.json. If missing or empty, note: "没有现有的自我模型做对比，建议先运行 value-discovery。"

2. reflections.jsonl — Read state/reflections.jsonl. Parse each line as JSON. Run integrity checks per references/reflection-protocol.md (State Integrity section): skip unparseable lines, flag duplicate IDs, verify required fields. Report: "reflections.jsonl: [N] 条, [M] 条损坏已跳过". If file is empty or new, mark as cold start.

   Check protocol version on load. If any reflection has protocol_version < 6, apply backward compatibility rules from the protocol reference. Missing v3-v6 fields are treated as absent — no error. Note to self: "包含 [K] 条旧版协议记录，部分字段缺失。" This doesn't block anything.

3. reflection-protocol.md — Read references/reflection-protocol.md for the latest lens prompts and schema definitions.

4. Unprocessed RAL records — Read state/records.jsonl (if exists). Filter for records where processed_at is null. These are daily captures from the /note skill. Include them as additional signal sources for all three Lens agents alongside the conversation transcript. Records provide event-level signals (what happened between conversations) that conversation-only analysis misses.

Tag cross-pollination: If a record has value_tags populated (from note amplify), pass these to the Value Lens as "user self-tagged" signals — they carry higher confidence than purely extracted signals, per the Tag Catalog in references/reflection-protocol.md. 5. Pending action experiments — Check the most recent reflection event in reflections.jsonl. If it contains action_experiments with status: "active" and outcome: null:

Determine age in days since activated_at:

Age	Action
0-4 days	Normal ask: "上次复盘你选择了 [N] 个行动实验。试一下了吗？"
5-13 days	Gentle nudge: "你选了 [N] 个行动实验，已经 [年龄] 天了——有试过吗？如果不再相关，可以直接跳过。"
14+ days	Auto-expire: mark status: "expired", note: "已自动归档 [N] 个过期实验。这些可能是当时情绪放大的信号，也可能是真正的线索。如果还想试，说'恢复实验'。"

"实验回顾："

#	行动规则	年龄	试了？	效果
1	如果 [trigger], 那么 [action]	3天	是	部分有效——[自由回答]
2	...	3天	否	—

Record outcomes. Update outcome and status fields. This feedback loop is how insights become behavioral change.

After agents complete, mark all loaded records with processed_at: "<ISO>".

Step 0.5: Conversation Preprocessing (long conversations)

Estimate conversation length — count turns or approximate token count.

• Short (<40 turns or <5k words): skip preprocessing. Pass the full transcript directly to all Lens agents.
• Long (40+ turns or 5k+ words): extract signal-rich excerpts before passing to Lens agents.

For long conversations, spawn a single lightweight preprocessing agent (or do it inline):

Extraction prompt:

"Scan the following conversation of [N] turns and extract the 5-8 most signal-rich exchanges — moments with emotional weight, decisions, trade-offs, unprompted initiations, or flow states. For each excerpt, include a one-line label and the verbatim exchange. Also note any notable shifts in topic or tone between sections."

Output: a condensed signal map — excerpts with labels + brief section notes.

What Lens agents receive:

1. The condensed signal map (primary — use this for analysis)
2. The full transcript (reference — available for evidence-checking when a finding needs direct quote verification)

This reduces per-agent context by 50-70% while preserving signal density. If the preprocessing agent fails, fall back to full transcript with a note: "预处理未完成，使用完整对话。"

Step 1: Pass 1 — Parallel 3-Lens Extraction

Announce to the user:

"正在通过三个视角分析这次对话..."

Spawn three agents in parallel using the Agent tool. Each agent receives:

• The FULL conversation transcript (everything since the last /reflect or the start of the session)
• The current user_dna.json as context
• The lens-specific prompt from references/reflection-protocol.md

ALL THREE LENS AGENTS now follow a common preprocessing workflow:

1. Segment (切分) — break conversation into 2-5 segments by topic/register
2. Focus (聚焦) — devote most depth to top 1-2 high-signal segments; don't force findings from thin material
3. Extract — extract signals per lens specialty

Agent 1: Value Lens (label: "reflect:value-lens")

• Purpose: Extract what the user is pursuing — direction, attraction, energy
• Schema: segments, focus_segments, candidate_values, attraction_signals, emotional_spikes, summary

Agent 2: Ability Lens (label: "reflect:ability-lens")

• Purpose: Extract demonstrated and emerging capabilities — what the user is becoming
• Schema: segments, focus_segments, demonstrated_abilities, emerging_edges, new_connections, summary

Agent 3: Pattern Lens (label: "reflect:pattern-lens")

• Purpose: Identify recurring patterns, cross-domain connections, energy signature, and abstraction layers (case → pattern → principle)
• Schema: segments, focus_segments, identified_patterns, abstraction_layers, cross_domain_connections, energy_signature, recurring_dilemmas, decision_heuristics, summary
• Cold start note: if no historical reflections, Pattern Lens cross-references user_dna.json only

Wait for all three agents to complete. If one fails, proceed with surviving outputs and flag the missing lens.

Step 1.5: Validation Gate

Before moving to the adversary, validate each lens output against the schema invariants defined in references/reflection-protocol.md (see "Agent Output Validation Gate").

Quick checks (do inline, don't spawn agents for this):

1. Parse JSON — each agent output must be valid JSON. If malformed, try extracting the JSON substring between first { and last }.
2. Check required fields — segments (non-empty), focus_segments (non-empty), summary (non-empty string). Per-lens field checks per the protocol.
3. Sanity check — do segments labels correspond to actual conversation topics? Do evidence quotes appear in the transcript?
4. Classify each lens: passed | degraded (partial data) | failed

Degraded mode rules:

Survivors	Action
3/3	Proceed normally
2/3	Proceed. Tell adversary which lens failed; relax cross-corroboration thresholds
1/3	Proceed with heavy caveat. Report to user which lenses failed.
0/3	Abort. Report: "本轮复盘无法完成——所有分析视角均未返回有效结果。" Save minimal event with status: "aborted".

If validation falls back to partial JSON or degraded mode, note it in the reflection event's adversary_verdict.

Step 2: Pass 2 — Adversary Agent

Spawn the adversary agent using the Agent tool.

Agent: Calibrated Skeptic (label: "reflect:adversary")

• Receives all three lens outputs as input
• Uses the adversary prompt from references/reflection-protocol.md
• Three roles:
  1. Truth calibration — verify claims are evidence-supported (don't penalize emotional intensity)
  2. Meaning expansion — offer alternative framings and perspective switches for each finding
  3. Action concretization — generate testable action experiments from surviving signals
• Output: verdicts (each with optional alternative_framing, perspective_switch), action_experiments, deep_dive_candidates, filtered_signals, overall_quality_score, surviving_signals_summary

Step 3: Synthesize & Present

Based on the adversary's surviving signals, synthesize:

1. Conversational summary — present findings in natural language:

"这次对话中我注意到——"

信号切分: [segments overview — which parts of the conversation had the strongest signals] 情绪层面: [emotional highlights + attraction signals] 能力层面: [demonstrated abilities + emerging edges] 模式层面: [patterns detected + abstraction layers (case → pattern → principle)] 能量地图: [energizing vs. draining activities] 跨域联结: [cross-domain connections] 信号质量: [overall quality score] / 1.0

[if signals filtered]: "以下信号未通过校准审查：[list with reasons]"

[if alternative framings available]: "以下发现存在多种理解方式：[list alternative perspectives]"

2. Proposed user_dna.json diffs — if any signals survived with sufficient confidence:

"基于以上信号，我建议对你的自我模型做以下调整："

价值观变更:

• [dimension]: [key] [from → to] — 证据: [evidence]

信念变更:

决策准则变更:

偏好变更:

• [field]: [from → to]

If no signals survived: state the fallback message from the protocol and skip to Step 4 (save without diffs).

3. Action experiments — present the adversary's concretized action rules:

"以下是基于本轮发现的行动实验——请在接下来一周尝试："

洞察	行动规则	验证方式
[insight]	如果[trigger]，那么[action]	[how_to_verify]

"每个实验都很小，不会打乱你的节奏。选 1-2 个最感兴趣的试试就好。"

Ask the user: "想试试哪些行动实验？还是全部跳过？"

Wait for user response. Track which experiments they select.

4. Confirmation prompt — for each proposed diff:

"请逐条确认——接受、拒绝、还是修改？"

Wait for user response. Process each diff:

User Response	Action
"接受" / "ok" / "yes"	Mark accepted — will be applied
"拒绝" / "no" / "不对"	Mark rejected — record the rejection
"改成 X"	Mark modified with user_override
No response / skip	Treat as rejected

Step 4: Persist

After user confirms/rejects all diffs:

1. Apply accepted diffs to user_dna.json — Read the current file, merge changes, write back. Keep all existing fields intact; only update the specific keys that were accepted.

2. Mark loaded records as processed — Update state/records.jsonl: for all records loaded in Step 0 (those with processed_at: null), set processed_at: "<ISO>" and linked_reflection_id: "<this reflection's id>".

3. Save selected action experiments — append to state/reflections.jsonl as part of the reflection event. Selected experiments have status: "active", activated_at: <ISO>, expires_at: <ISO + 14 days>, outcome: null. Unselected experiments have status: "skipped". If user picks "跳过全部", all are skipped.

4. Write reflection event to state/reflections.jsonl — use the full schema from references/reflection-protocol.md. Append as a single JSON line.

5. Index in claude-mem — use mcp__plugin_claude-mem_mcp-search__observation_add:

   {
     "content": "Reflection: [value_lens.summary] | [ability_lens.summary] | [pattern_lens.summary]",
     "kind": "reflection",
     "metadata": {
       "type": "reflection",
       "reflection_id": "<uuid>",
       "quality_score": <float>,
       "emotions":  
         
        <N>
        <N>
        
     
   
   

"已保存。复盘 ID: [id]"

"状态更新: user_dna.json 已更新 [N] 项 / reflections.jsonl 累计 [N] 条 / claude-mem 索引完成"

[If action experiments selected]: "[N] 个行动实验已记录，下次复盘时会回检。"

Step 5: Auto-Suggest /distill

Check the cumulative impact score of unprocessed reflections (all reflections where distilled_at is null):

impact = Σ (|score_change| × emotional_intensity) for proposed diffs

If impact >= 15:

"你的复盘记录中累计影响分数为 [score]，建议运行 /distill 进行一次阶段性合成。现在跑还是稍后？"

If user says yes: immediately invoke the /distill skill. If no: note it and move on.

Cold Start Behavior

On the very first /reflect (no state/reflections.jsonl or empty file):

• Run the FULL 3-agent protocol. Do NOT simplify.
• Pattern Lens prompt explicitly states: "First reflection — cross-referencing user_dna.json only. Historical patterns will emerge with more data."
• Adversary prompt includes: "No historical data available — calibrate against user_dna.json and cross-lens corroboration only."
• Output includes: "这是你的第一次复盘——历史模式会随着更多复盘数据而浮现。"

Edge Cases

Follow the edge case table in references/reflection-protocol.md. Key reminders:

Scenario	Action
user_dna.json missing	Run without. Note: "建议先运行 value-discovery。"
Lens agent fails	Validation gate classifies as failed. Proceed per degraded mode rules (2/3 → relaxed, 1/3 → caveat, 0/3 → abort).
All three lenses fail	Abort with minimal event. Report to user.
All signals filtered	Honest output: no diffs proposed. Still save.
User rejects all	No DNA update. Rejection IS signal — record it.
claude-mem MCP tools unavailable	Save JSONL only. Report degraded mode.
JSONL file has corrupt lines	Skip unparseable lines. Report count. If >50% corrupt, recommend manual recovery.
JSONL file missing	Create new file. Not an error.
Short conversation	Full protocol. Confidence will naturally be lower.

Key Files

File	Purpose
references/reflection-protocol.md	Single source of truth — lens prompts, schemas, adversary rules
state/user_dna.json	Read as context, write accepted diffs
state/reflections.jsonl	Append full reflection event
models/user_dna_schema.py	Value dimension definitions and mapping rules