Completion Learn

Completion Learn

Overview

Use this skill for an explicit completion debrief. The goal is to extract what should remain after a finished task across self, collaboration, and tool.

If the user only says /learn-complete, interpret it as "help me see what should remain after this task."

Default mode is three-axis evolution:

• Self
• Collaboration
• Tool

Prefer a short debrief with durable residue over abstract richness. Treat task completed and capability gained as separate judgments.

Rules

• Require explicit invocation. Never slide into this mode during normal execution or mid-task updates.
• Only run when the task is complete enough for an honest retrospective. If completion is unclear, say so and stop.
• If the user gives a focus like debugging, workflow, or collaboration, treat it as the primary lens.
• Prefer the smallest useful output shape from references/output-structure.md.
• Focus on what should remain from the long interaction instead of replaying the session.
• Before writing the axes, privately judge the residue level:
  • mostly assisted performance
  • partial internalization
  • durable capability gain
• Keep that residue judgment internal by default. Do not add a fourth visible section unless the user asks for deeper explanation or the distinction is the main lesson.
• Inside Self, inspect the user's side of the interaction: attention, phrasing, emotion, ownership, and judgment habits.
• Inside Collaboration, inspect what interaction pattern between the user and AI should become default.
• Map mistakes to one or two labels from references/mistake-patterns.md.
• In default mode, keep the priority order Self > Collaboration > Tool.
• End with one durable rule and one next deliberate practice.
• Make next deliberate practice a reduced-support test, not generic advice. It should check whether the claimed residue actually remains when one layer of help is removed.
• If the residue is mostly assisted performance, keep the practice narrow and ask the user to do one small step alone before using the tool.
• If the residue is partial internalization, make the practice user-first and tool-second on the same bottleneck.
• If the residue is durable capability gain, move the practice to a nearby transfer task instead of repeating the exact same one.
• Inside the Tool axis, keep skill sedimentation separate from skill evolution / optimize.
• If the current skill boundary is right but weak, route to skill-optimizer.
• If the boundary itself should change or a second complementary skill is needed, route to skill-creator.
• If confidence is low, stop at recommendation instead of chaining more skill work.

When to Use

Use when:

• the user invokes /learn-complete
• the user asks for a post-task retrospective, completion debrief, or "what should remain from this task?"
• the user wants to know whether the finished workflow should optimize an existing skill or become a new one

Do not use when:

• the task is still underway
• the user wants a checkpoint, status update, or implementation work
• the main need is debugging, editing, or normal summarization without reflection

Typical asks:

• /learn-complete
• /learn-complete debugging
• 顺便看一下，这次该优化已有 skill 还是新建一个

Workflow

1. Confirm completion and pick the primary lens.
2. Choose Light, Standard, or Deep from references/output-structure.md.
3. Judge whether the outcome was mostly assisted, partially internalized, or a durable capability gain.
4. Extract the strongest residue worth keeping, the main mistake pattern, and the missing guardrail.
5. Convert that into Self, Collaboration, and Tool sedimentation without changing the visible three-axis shape.
6. End with one durable rule, one next deliberate practice, and one tool-evolution judgment.
7. Make sure the practice step matches the residue judgment by reducing support or forcing transfer.
8. Route to skill-optimizer, skill-creator, or stop at recommendation.

Resource Map

• references/improvement-axes.md, references/self-improvement.md, references/collaboration-evolution.md -> default three-axis rubric
• references/focus-lenses.md, references/question-bank.md -> choose the learning lens and deepen it
• references/mistake-patterns.md, references/output-structure.md, references/sample-output.md -> shape the output and keep it concrete
• references/skill-sedimentation.md, references/skill-evolution.md, references/tool-evolution-routing.md -> decide whether to optimize, extend, add, or stop
• references/examples.md -> representative user phrasings