Knowledge Intake

Knowledge Intake

Systematically process external resources into actionable knowledge. When a user links an article, blog post, or paper, this skill guides evaluation, storage decisions, and application routing.

What It Is

A knowledge governance framework that answers three questions for every external resource:

1. Is it worth storing? - Evaluate signal-to-noise and relevance
2. Where does it apply? - Route to local codebase or meta-infrastructure
3. What does it displace? - Identify outdated knowledge to prune

The Intake Signal

When a user links an external resource, it is a signal of importance.

The act of sharing indicates the resource passed the user's own filter. Our job is to:

• Extract the essential patterns and insights
• Determine appropriate storage location and format
• Connect to existing knowledge structures
• Identify application opportunities

Quick Start

When a user shares a link:

1. FETCH    → Retrieve and parse the content
2. EVALUATE → Apply importance criteria
3. DECIDE   → Storage location and application type
4. STORE    → Create structured knowledge entry
5. CONNECT  → Link to existing palace structures
6. APPLY    → Route to codebase or infrastructure updates
7. PRUNE    → Identify displaced/outdated knowledge

Evaluation Framework

Importance Criteria

Scoring Guide

• 80-100: Evergreen knowledge, store prominently, apply immediately
• 60-79: Valuable insight, store in corpus, schedule application
• 40-59: Useful reference, store as seedling, revisit later
• Below 40: Low priority, capture key quote only or skip

Application Routing

Local Codebase Application

Apply when knowledge directly improves current project:

• Bug fix patterns
• Performance optimizations
• Architecture decisions for this codebase
• Tool/library recommendations

Action: Update code, add comments, create ADR

Meta-Infrastructure Application

Apply when knowledge improves our plugin ecosystem:

• Skill design patterns
• Agent behavior improvements
• Workflow optimizations
• Learning/evaluation methods (like Franklin Protocol)

Action: Update skills, create modules, enhance agents

Routing Decision Tree

Is the knowledge...
├── About HOW we build things? → Meta-infrastructure
│   ├── Skill patterns → Update abstract/memory-palace skills
│   ├── Learning methods → Add to knowledge-corpus
│   └── Tool techniques → Create new skill module
│
└── About WHAT we're building? → Local codebase
    ├── Domain knowledge → Store in project docs
    ├── Implementation patterns → Update code/architecture
    └── Bug/issue solutions → Apply fix, document

Storage Locations

The Tidying Imperative (KonMari-Inspired)

"A cluttered palace is a cluttered mind."

New knowledge often displaces old—but time is not the criterion. Relevance and aspirational alignment are.

The Master Curator

The human in the loop defines what stays. Before major tidying:

1. Who are you becoming? - Your aspirations as a developer
2. What excites you now? - Genuine enthusiasm, not "should"
3. What have you outgrown? - Past interests consciously left behind

The Two Questions

For each piece of knowledge, both must be yes:

• Does it spark joy? - Genuine enthusiasm, not obligation
• Does it serve your aspirations? - Aligned with who you're becoming

Tidying Actions

"I might need this someday" is fear, not joy. Release it.

Marginal Value Filtering (Anti-Pollution)

"If it can't teach something the existing corpus can't already teach → skip it."

Before storing ANY knowledge, run the marginal value filter to prevent corpus pollution.

The Three-Step Filter

1. Redundancy Check

• Exact match → REJECT immediately
• 80%+ overlap → REJECT as redundant
• 40-80% overlap → Evaluate delta (Step 2)
• <40% overlap → Likely novel, proceed to store

2. Delta Analysis (for partial overlap only)

• Novel insight/pattern → High value (0.7-0.9)
• Different framing only → Low value (0.2-0.4)
• More examples → Marginal value (0.4-0.6)
• Contradicts existing → Investigate (0.6-0.8)

3. Integration Decision

• Standalone: Novel content, no significant overlap
• Merge: Enhances existing entry with examples/details
• Replace: Supersedes outdated knowledge
• Skip: Insufficient marginal value

Using the Filter

from memory_palace.corpus import MarginalValueFilter

# Initialize filter with corpus and index directories
filter = MarginalValueFilter(
    corpus_dir="docs/knowledge-corpus",
    index_dir="docs/knowledge-corpus/indexes"
)

# Evaluate new content
redundancy, delta, integration = filter.evaluate_content(
    content=article_text,
    title="Structured Concurrency in Python",
    tags=["async", "concurrency", "python"]
)

# Get human-readable explanation
explanation = filter.explain_decision(redundancy, delta, integration)
print(explanation)

# Act on decision
if integration.decision == IntegrationDecision.SKIP:
    print(f"Skipping: {integration.rationale}")
elif integration.decision == IntegrationDecision.STANDALONE:
    # Store as new entry
    store_knowledge(content, title)
elif integration.decision == IntegrationDecision.MERGE:
    # Enhance existing entry
    enhance_entry(integration.target_entries[0], content)
elif integration.decision == IntegrationDecision.REPLACE:
    # Replace outdated entry
    replace_entry(integration.target_entries[0], content)

Filter Output Example

=== Marginal Value Assessment ===

Redundancy: partial
Overlap: 65%
Matches: async-patterns, python-concurrency
  - Partial overlap (65%) with 2 entries

Delta Type: novel_insight
Value Score: 75%
Teaching Delta: Introduces 8 new concepts
Novel aspects:
  + New concepts: structured, taskgroup, context-manager
  + New topics: Error Propagation, Resource Cleanup

Decision: STANDALONE
Confidence: 80%
Rationale: Novel insights justify standalone: Introduces 8 new concepts

Progressive Autonomy Integration

The marginal value filter respects autonomy levels (see plan Phase 4):

• Level 0: ALL decisions require human approval
• Level 1: Auto-approve 85+ scores in known domains
• Level 2: Auto-approve 70+ scores in known domains
• Level 3: Auto-approve 60+, auto-reject obvious noise

Current implementation: Level 0 (all human-in-the-loop).

RL-Based Quality Scoring

The knowledge corpus uses reinforcement learning signals to dynamically score entry quality based on actual usage patterns.

Usage Signals

Quality Decay Model

Knowledge entries decay over time unless validated:

Entries are classified by decay status:

• Fresh: >70% quality retained
• Stale: 40-70% quality retained
• Critical: 20-40% quality retained
• Archived: <20% quality retained

Source Lineage Tracking

Hybrid lineage tracking based on source importance:

Full Lineage (for important sources):

• Primary source with complete metadata
• Derivation chain (what entries it was derived from)
• Transformation history (summarization, extraction, etc.)
• Validation chain (who validated and when)

Simple Lineage (for standard sources):

• Source type and URL
• Retrieval timestamp

Full lineage is used for:

• Research papers
• Documentation
• Entries with importance score >= 0.7

Knowledge Orchestrator

The KnowledgeOrchestrator coordinates all quality systems:

from memory_palace.corpus import KnowledgeOrchestrator, UsageSignal

# Initialize orchestrator
orchestrator = KnowledgeOrchestrator(
    corpus_dir="docs/knowledge-corpus",
    index_dir="docs/knowledge-corpus/indexes"
)

# Record usage events
orchestrator.record_usage("entry-1", UsageSignal.ACCESS)
orchestrator.record_usage("entry-1", UsageSignal.POSITIVE_FEEDBACK)

# Assess entry quality
entry = {"id": "entry-1", "maturity": "growing"}
assessment = orchestrator.assess_entry(entry)
print(f"Quality: {assessment.overall_score:.0%}")
print(f"Status: {assessment.status}")
print(f"Recommendations: {assessment.recommendations}")

# Get maintenance queue
entries = [...]  # Your entry list
queue = orchestrator.get_maintenance_queue(entries)
for item in queue:
    print(f"{item.entry_id}: {item.status} - {item.recommendations}")

# Ingest new content with lineage
from memory_palace.corpus import SourceReference, SourceType

source = SourceReference(
    source_id="src-1",
    source_type=SourceType.DOCUMENTATION,
    url="https://docs.example.com/api",
    title="API Documentation"
)
entry_id, decision = orchestrator.ingest_with_lineage(
    content="# API Reference\n...",
    title="API Documentation",
    source=source
)

RL Integration with Marginal Value Filter

The marginal value filter emits RL signals on integration decisions:

from memory_palace.corpus import MarginalValueFilter

filter = MarginalValueFilter(corpus_dir, index_dir)

# Evaluate with RL signal emission
redundancy, delta, integration, rl_signal = filter.evaluate_with_rl(
    content=article_text,
    title="New Article",
    tags=["python", "async"]
)

# RL signal contains:
# - signal_type: UsageSignal to emit
# - weight: Signal weight for scoring
# - action: What happened (new_entry_created, entry_enhanced, etc.)
# - decision: Integration decision made
# - confidence: Decision confidence
print(f"RL Signal: {rl_signal['action']} (weight: {rl_signal['weight']})")

Workflow Example

User shares: "Check out this article on structured concurrency"

intake:
  source: "https://example.com/structured-concurrency"

# PHASE 3: Marginal Value Filter
marginal_value:
  redundancy:
    level: partial_overlap
    overlap_score: 0.65
    matching_entries: [async-patterns, python-concurrency]
  delta:
    type: novel_insight
    value_score: 0.75
    novel_aspects: [structured, taskgroup, context-manager]
    teaching_delta: "Introduces structured concurrency pattern"
  integration:
    decision: standalone
    confidence: 0.80
    rationale: "Novel insights justify standalone entry"

# Continue with evaluation if filter passes
evaluation:
  novelty: 75        # New pattern for error handling
  applicability: 90  # Directly relevant to async code
  durability: 85     # Core concept, won't age quickly
  connectivity: 70   # Links to error handling, async patterns
  authority: 80      # Well-known author, cited sources
  total: 82          # Evergreen, store and apply

routing:
  type: both
  local_application:
    - Refactor async error handling in current project
    - Add structured concurrency pattern to codebase
  meta_application:
    - Create module in relevant skill
    - Add to knowledge-corpus as reference

storage:
  location: docs/knowledge-corpus/structured-concurrency.md
  format: memory_palace_entry
  maturity: growing

pruning:
  displaces:
    - Old async error patterns (mark deprecated)
  complements:
    - Existing error handling module
    - Async patterns documentation

Queue Processing

Research sessions and external content are automatically queued for review in docs/knowledge-corpus/queue/.

Processing Queue Entries

# List pending queue entries
ls -1t docs/knowledge-corpus/queue/*.yaml

# Review specific entry
cat docs/knowledge-corpus/queue/2025-12-31_topic.yaml

# Process approved entry
# 1. Create memory palace entry in docs/knowledge-corpus/
# 2. Update queue entry status to 'processed'
# 3. Archive or delete queue entry

Queue Integration

The research-queue-integration hook automatically queues:

• Brainstorming sessions with 3+ WebSearch calls
• Research-focused sessions with substantial findings
• Manual additions via queue entry creation

Queue entry format: See docs/knowledge-corpus/queue/README.md

Queue Status Workflow

pending_review → [Review] → approved/rejected
approved → [Create Entry] → processed
processed → [Archive] → queue/archive/

Automation

• Run uv run python skills/knowledge-intake/scripts/intake_cli.py --candidate path/to/intake_candidate.json --auto-accept
• The CLI runs marginal value filter, creates palace entries (docs/knowledge-corpus/*.md), developer drafts (docs/developer-drafts/), and appends audit rows to docs/curation-log.md.
• Use --output-root in tests or sandboxes to avoid mutating the main corpus.
• Queue Processing: Use --process-queue flag to review and process queued entries interactively.

Detailed Resources

• Evaluation Rubric: See modules/evaluation-rubric.md
• Storage Patterns: See modules/storage-patterns.md
• KonMari Tidying Philosophy: See modules/konmari-tidying.md
• Tidying Workflows: See modules/pruning-workflows.md

Hook Integration

Memory-palace hooks automatically detect content that may need knowledge intake processing:

Automatic Triggers

Hook Signals

When hooks detect potential knowledge content, they add context messages:

Memory Palace: New web content fetched from {url}.
Consider running knowledge-intake to evaluate and store if valuable.

Memory Palace: Reading local knowledge doc '{path}'.
This path is configured for knowledge tracking.
Consider running knowledge-intake if this contains valuable reference material.

Deduplication

Hooks check the memory-palace-index.yaml to avoid redundant processing:

• Known URLs: "Content already indexed" - skip re-evaluation
• Changed content: "Content has changed" - suggest update
• New content: Full evaluation recommended

Safety Checks

Before signaling intake, hooks validate content:

• Size limits (default 500KB)
• Secret detection (API keys, credentials)
• Data bomb prevention (repetition, unicode bombs)
• Prompt injection sanitization

Index Schema Alignment

The deduplication index stores fields aligned with this skill's evaluation:

entries:
  "https://example.com/article":
    content_hash: "xxh:abc123..."
    stored_at: "docs/knowledge-corpus/article.md"
    importance_score: 82           # From evaluation framework
    maturity: "growing"            # seedling, growing, evergreen
    routing_type: "both"           # local, meta, both
    last_updated: "2025-12-06T..."

Integration

• memory-palace-architect - Structures stored knowledge spatially
• digital-garden-cultivator - Manages knowledge lifecycle
• knowledge-locator - Finds and retrieves stored knowledge
• skills-eval (abstract) - Evaluates meta-infrastructure updates