Knowledge Base Workflow

What This Skill Does

This skill teaches you how to use the knowledge-base-server MCP tools efficiently. It does NOT replace the MCP server — it complements it by giving you the retrieval strategy that saves 90%+ tokens.

Think of it this way:

• MCP server = the engine (search, read, write, capture)
• This skill = the driving instructions (when to search, what to read, how to capture)

Before Starting Any Task

Search the KB for relevant context BEFORE writing code or making decisions:

1. kb_context("topic") — get summaries only (~100 tokens per doc, 90% savings)
2. Review titles and summaries — decide which docs matter
3. kb_read(id) — read full content ONLY for docs you actually need

Never skip this. The KB has accumulated lessons, fixes, decisions, and architecture docs. Searching first prevents:

• Repeating solved problems
• Contradicting past decisions
• Missing known pitfalls
• Wasting tokens on re-discovery

The Three-Tier Retrieval Pattern

The KB has three tiers of information. Query the right tier for your need:

Need	Tool	Tokens	When
Quick context	kb_context	~100/doc	Always start here
Specific search	kb_search	~200/result	Looking for something specific
Conceptual match	kb_search_smart	~200/result	Fuzzy/semantic queries
Full document	kb_read	~500-5000/doc	Only after context confirms relevance

Rule: Never kb_read without kb_context first. You wouldn't read an entire book to check if it's relevant — you'd read the summary.

After Completing Work

Capture what you learned so the next session starts smarter:

After debugging sessions:

kb_capture_session:
  goal: "What you were trying to do"
  commands_worked: "What worked"
  commands_failed: "What failed and why"
  root_causes: "The actual problem"
  fixes: "What fixed it"
  lessons: "What to do differently next time"

After bug fixes:

kb_capture_fix:
  title: "Short fix title"
  symptom: "What was broken"
  cause: "Root cause"
  resolution: "How it was fixed"

After research or decisions:

kb_write:
  title: "Decision or finding title"
  type: "decision" or "research" or "lesson"
  content: "What was decided and why"

The Self-Learning Loop

This is how the system compounds intelligence:

Session N:
  1. Search KB for context (maybe find nothing)
  2. Do the work (hit problems, make decisions)
  3. Capture findings to KB

Session N+1:
  1. Search KB for context (find Session N's captures!)
  2. Skip the problems Session N already solved
  3. Capture NEW findings

Session N+100:
  1. Search KB for context (find 100 sessions of accumulated knowledge)
  2. One-shot clean implementation because context covers everything
  3. Capture only genuinely new learnings

This is NOT fine-tuning. The model doesn't change. The context it receives improves. And context is everything.

When to Use Each Tool

Situation	Tool	Why
Starting a new task	kb_context	Get the lay of the land
"How did we do X?"	kb_search	Find specific past work
"What do we know about X?"	kb_search_smart	Conceptual/fuzzy match
Need full implementation details	kb_read	After context identified the doc
Finished debugging	kb_capture_session	Record what happened
Fixed a bug	kb_capture_fix	Record symptom/cause/fix
Made a decision	kb_write type=decision	Record the decision and why
Found useful research	kb_write type=research	Save for future reference
Want cross-cutting insights	kb_synthesize	Connect dots across sources
New content needs tagging	kb_classify	Auto-classify unprocessed notes

What NOT to Do

• Don't kb_read every document that matches a search — read summaries first
• Don't skip searching because "I probably know this" — the KB knows more than you remember
• Don't forget to capture after significant work — a lesson not captured is a lesson repeated
• Don't index raw code into the KB — use CODEMAP.md structural maps instead
• Don't treat the KB as a dump — classified, typed, tagged notes are 10x more useful than raw text