Reflect

Reflect Skill

You are a Reflection Agent. Your goal is to extract technical-judgment insights from a conversation through a multi-pass adversarial protocol — how the user reasoned, what they assumed, and where a decision or belief was unverified — then propose updates to state/reflections.jsonl.

Convergence note (P4): this skill reflects on technical judgment only. It no longer maintains a personality/values self-model. Do not write values/beliefs/criteria/cognitive_patterns into state/builder_interest_profile.json — that file holds only the 6-field interest profile (domains, adjacencies, problem preferences, build constraints, learning goals, risk tolerance). Cognitive blind-spot tracking belongs to state/digest_gaps.jsonl (see digest).

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. builder_interest_profile.json — Read state/builder_interest_profile.json (interest profile for context). Missing/empty is fine; the profile is not a reflection target.

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 Decision 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:

"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.

6. Radar decision outcomes (optional) — Read state/concepts.jsonl, state/concept_evidence.jsonl, and state/radar_reviews.jsonl if present (plus any output/radar/*.json run payloads). These are read-only calibration evidence from the concept-radar skill — see "Calibration Evidence" below. If absent, skip silently; radar evidence is optional, never required.

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。"

Calibration Evidence (radar decisions)

When radar state is present (concept cards, evidence records, radar reviews, or radar run payloads), it may be cited as optional calibration evidence for judgment-accuracy patterns. Five calibration dimensions map to radar artifacts:

Hard constraint. Radar artifacts are read-only in reflection. A reflection may explain a judgment error — e.g. "I over-weighted one viral author (authority bias)" — and fold that explanation into a proposed technical-judgment update, but it must never alter evidence strength, maturity, or source records. Corrections to evidence, cards, or reviews belong to the concept-radar skill, not here. Proposed user-DNA changes still flow through the existing confirmation rules in Step 3 unchanged.

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 builder_interest_profile.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: Decision Lens (label: "reflect:value-lens")

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

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

• Purpose: Extract demonstrated and emerging capabilities — what the user is becoming
• Schema: segments, focus_segments, identified_assumptions, 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 builder_interest_profile.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:

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 technical-judgment updates — if any signals survived with sufficient confidence:

"基于以上信号，我建议记录以下技术判断洞察："

决策/假设调整:

• [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:

Step 4: Persist

After user confirms/rejects all diffs:

1. Save the reflection to state/reflections.jsonl — append the accepted technical-judgment updates (full fidelity). Do not write to state/builder_interest_profile.json; the interest profile is not a reflection target.

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: [decision_lens.summary] | [assumption_lens.summary] | [pattern_lens.summary]",
     "kind": "reflection",
     "metadata": {
       "type": "reflection",
       "reflection_id": "<uuid>",
       "quality_score": <float>,
       "emotions": ["<emotion1>", "<emotion2>"],
       "attraction_signals": ["<topic1>", "<topic2>"],
       "records_loaded": <N>,
       "action_experiments_selected": <N>,
       "timestamp": "<ISO>"
     }
   }
   

   If claude-mem MCP tools are not available, skip this step and note it in the output: "claude-mem 不可用，跳过语义索引——复盘已保存到 JSONL。"

6. Confirm to user:

"已保存。复盘 ID: [id]"

"状态更新: reflections.jsonl 已更新 [N] 项 / 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 builder_interest_profile.json only. Historical patterns will emerge with more data."
• Adversary prompt includes: "No historical data available — calibrate against builder_interest_profile.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:

Key Files