dissertation-learning-loop

name	dissertation-learning-loop
description	Use when dissertation work should convert new reading, source searches, LMS/module materials, supervisor resources, or discussion outcomes into durable project knowledge, literature-map updates, proposal implications, and next-reading questions without enabling unsupervised background automation.

Dissertation Learning Loop

Use this skill when the agent needs to learn from newly read sources, websites, LMS content, supervisor or public resource material, supervisor feedback, proposal discussions, or user-confirmed decisions and carry that learning into later dissertation work.

Purpose

Make the agent's learning task-triggered, source-grounded, and reusable. This is a local learning workflow, not autonomous background browsing.

Core Rule

Every learned item must keep its evidence boundary:

• CONFIRMED: directly supported by a local file, LMS/module source, user-confirmed fact, or verified source.
• LITERATURE-SUPPORTED: supported by a screened academic source.
• CONTEXTUAL: useful for thinking, but not formal dissertation evidence.
• INFERENCE: the agent's synthesis or implication.
• TO CONFIRM: needs user, supervisor, LMS, or source verification.

When To Use

Use for:

• literature search follow-up
• reading-list or source-note ingestion
• proposal/gap/methodology discussion where new decisions emerge
• supervisor feedback digestion
• updating Obsidian, source registers, or research-wiki after new evidence
• preparing the next research-search questions

Do not use for:

• raw participant data storage
• inventing citation details from memory
• automatic weekly searches unless a separate automation is explicitly created
• writing formal text before source-first and document-quality gates are applied

Workflow

1. Identify the learning input:
   • source file, URL, LMS page, user-confirmed statement, supervisor note, or discussion result.
2. Classify the learning type:
   • literature concept
   • project decision
   • methodology implication
   • ethics/design boundary
   • writing-style preference
   • next-reading need
3. Extract only reusable knowledge:
   • key concept or claim
   • why it matters for this dissertation
   • what it can support
   • what it cannot support
   • next question it raises
4. Update the right layer:
   • knowledge-base/SOURCE_REGISTER.md for source status
   • knowledge-base/sources/ for source notes
   • research-wiki/LITERATURE_MAP.md for clusters and gap logic
   • research-wiki/METHOD_DECISIONS.md for methodology decisions
   • research-wiki/OPEN_QUESTIONS.md for unresolved questions
   • Obsidian for navigation and thinking links
5. Record major learning events in research-wiki/TASK_STATE.md when they affect future work.

Output Shape

Learning captured:
- Source/input:
- Evidence status:
- Reusable point:
- Dissertation implication:
- Boundary:
- Files updated:
- Next reading/question:

Guardrails

• Do not treat a skimmed or metadata-only source as full evidence.
• Do not store full copyrighted text or raw participant material in project knowledge layers.
• Do not let a useful idea become a formal claim without citation readiness.
• If the learning changes the dissertation route, mark the affected proposal/methodology decision clearly.