Processes external resources into stored knowledge with quality scoring and routing. Use when ingesting articles, papers, or docs into a memory palace.
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Processes external resources into stored knowledge with quality scoring and routing. Use when ingesting articles, papers, or docs into a memory palace.
Process external resources into the knowledge store. When a user links an article, blog post, or paper, this skill guides evaluation, storage decisions, and application routing.
When To Use
Capturing and organizing knowledge from sessions
Ingesting information into structured memory palaces
When NOT To Use
Temporary notes that do not need long-term storage
Code-only changes without knowledge capture needs
What It Is
A knowledge governance framework that answers three questions for every external resource:
Is it worth storing? - Evaluate signal-to-noise and relevance
Where does it apply? - Route to local codebase or meta-infrastructure
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 - Identify outdated knowledge to prune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 → Detect format, retrieve and convert content
2. EVALUATE → Apply importance criteria
3. DECIDE → Storage location and application type
4. STORE → Create structured knowledge entry
5. VALIDATE → Scribe verification (slop scan + doc verify)
6. CONNECT → Link to existing palace structures
7. PROMOTE → Offer Discussion promotion (score 80+)
8. APPLY → Route to codebase or infrastructure updates
9. PRUNE → Identify displaced/outdated knowledge
Step 1: FETCH with Format Detection
Before retrieving content, detect the source format from
the URL or file path to choose the right retrieval method.
Web articles and blog posts (default path):
Use WebFetch to retrieve HTML content directly.
No conversion needed.
Document URLs (PDF, DOCX, PPTX, XLSX):
Apply the leyline:document-conversion protocol.
This tries the markitdown MCP tool first for high-quality
markdown, then falls back to native Claude Code tools
(Read for PDFs, etc.), then informs the user if the
format is unsupported without markitdown.
Local files (user shares a file path):
Construct a file:// URI from the absolute path and
apply the leyline:document-conversion protocol.
Format detection heuristics:
URL Pattern
Format
Retrieval
*.pdf, arxiv.org/pdf/*
PDF
document-conversion
*.docx, *.doc
Word
document-conversion
*.pptx, *.ppt
PowerPoint
document-conversion
*.xlsx, *.xls
Excel
document-conversion
*.epub
E-book
document-conversion
drive.google.com/*
Various
document-conversion
Everything else
HTML/web
WebFetch (existing)
After retrieval (regardless of method), wrap the content
in external content boundary markers per
leyline:content-sanitization before proceeding to
Step 2 (EVALUATE).
Step 5: Scribe Validation (Required)
All knowledge corpus entries MUST pass scribe validation before finalizing.
Run Skill(scribe:slop-detector) on the new entry:
Score must be < 2.5 (Clean to Light)
No Tier 1 markers (delve, tapestry, comprehensive, leveraging, etc.)
Hedge word density < 15 per 1000 words
Use Agent(scribe:doc-verifier) to validate:
All file paths and URLs exist
All cross-references valid
Source attributions accurate
# Quick validation for knowledge corpus entry
/slop-scan docs/knowledge-corpus/[entry-name].md
# Doc verification is now agent-only:
Agent(scribe:doc-verifier) "Verify docs/knowledge-corpus/[entry-name].md"
DO NOT finalize entries with slop score > 2.5 - rewrite with concrete specifics.
Verification: Run the command with --help flag to verify availability.
Step 7: Discussion Promotion (Score 80+ Only)
When the evaluation score is 80-100 (evergreen), you
MUST execute the Discussion promotion workflow. If the
score is below 80, skip this step entirely.
Execute these steps in order:
Read modules/discussion-promotion.md for the
full GraphQL workflow
Tell the user: "This entry has reached evergreen
maturity. Publishing to GitHub Discussions. [Y/n]"
If the user says "n", skip to Step 8 (APPLY)
Run the gh api graphql commands from the module
to create or update a Discussion in the "Knowledge"
category
Update the local corpus entry with discussion_url
If the entry already has a discussion_url field,
update the existing Discussion instead of creating
a new one
If gh is unavailable or promotion fails, warn
the user and continue to Step 8 (APPLY)
Publishing is the default for qualifying entries. It
never blocks the intake workflow.
Evaluation Framework
Importance Criteria
Criterion
Weight
Questions
Novelty
25%
Does this introduce new patterns or concepts?
Applicability
30%
Can we apply this to current work?
Durability
20%
Will this remain relevant in 6+ months?
Connectivity
15%
Does it connect to multiple existing concepts?
Authority
10%
Is the source credible and well-reasoned?
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)
**Verification:** Run the command with `--help` flag to verify availability.
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
Verification: Run the command with --help flag to verify availability.
Storage Locations
Knowledge Type
Location
Format
Meta-learning patterns
docs/knowledge-corpus/
Full memory palace entry
Skill design insights
skills/*/modules/
Technique module
Tool/library knowledge
docs/references/
Quick reference
Temporary insights
Digital garden seedling
Lightweight note
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:
Who are you becoming? - Your aspirations as a developer
What excites you now? - Genuine enthusiasm, not "should"
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
Finding
Action
Supersedes
Archive old with gratitude, link as context
Contradicts
Evaluate both, keep what sparks joy
No longer aligned
Release with gratitude
Complements
Create bidirectional links
"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 directoriesfilter = 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 decisionif 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)
Verification: Run the command with --help flag to verify availability.
Filter Output Example
**Verification:** Run the command with `--help` flag to verify availability.
=== 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
Verification: Run the command with --help flag to verify availability.
Progressive Autonomy Integration
The marginal value filter respects autonomy levels (see plan Phase 4):
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.
Anchor-Question Clarity Gate
Usage signals score an entry after it is stored, from how it gets
accessed. They cannot catch a summary that was never clear enough to
be useful. Before an entry is written, gate it on the MMPO dual-probe
(arXiv:2605.30159), adapted from task state to knowledge value:
Value probe: what does this knowledge let us do? Name a
concrete capability or decision it enables, not a topic label.
Gap probe: what does this knowledge not answer? List the
bounded open questions it leaves, not generic "more research".
Value probe
Gap probe
Decision
Names a concrete use
Bounded, specific gaps
Store at the evaluated score
Names a concrete use
"Everything" / empty
Store, but cap at seedling until gaps are named
Vague ("useful background")
Any
Do not store as knowledge: capture the key quote only or skip
This gate is qualitative and complements, does not replace, the
Importance Criteria score: a summary can score well on Applicability
yet still fail the value probe if it cannot name what it enables.
When memory-palace:memory-clarity-probe is installed, delegate the
dual-probe evaluation to it and use its Recommendation as the gate.
Usage Signals
Signal
Weight
Description
ACCESS
+0.1
Entry was accessed/read
CITATION
+0.3
Entry was cited in another context
POSITIVE_FEEDBACK
+0.5
User marked as helpful
NEGATIVE_FEEDBACK
-0.3
User marked as unhelpful
CORRECTION
+0.2
Entry was corrected/updated
STALE_FLAG
-0.4
Entry marked as potentially outdated
Quality Decay Model
Knowledge entries decay over time unless validated:
Maturity
Half-Life
Decay Curve
Seedling
14 days
Exponential
Growing
30 days
Exponential
Evergreen
90 days
Logarithmic
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 lineagefrom 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
)
Verification: Run the command with --help flag to verify availability.
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 confidenceprint(f"RL Signal: {rl_signal['action']} (weight: {rl_signal['weight']})")
Verification: Run the command with --help flag to verify availability.
Workflow Example
User shares: "Check out this article on structured concurrency"
intake:source:"https://example.com/structured-concurrency"# PHASE 3: Marginal Value Filtermarginal_value:redundancy:level:partial_overlapoverlap_score:0.65matching_entries: [async-patterns, python-concurrency]
delta:type:novel_insightvalue_score:0.75novel_aspects: [structured, taskgroup, context-manager]
teaching_delta:"Introduces structured concurrency pattern"integration:decision:standaloneconfidence:0.80rationale:"Novel insights justify standalone entry"# Continue with evaluation if filter passesevaluation:novelty:75# New pattern for error handlingapplicability:90# Directly relevant to async codedurability:85# Core concept, won't age quicklyconnectivity:70# Links to error handling, async patternsauthority:80# Well-known author, cited sourcestotal:82# Evergreen, store and applyrouting:type:bothlocal_application:-Refactorasyncerrorhandlingincurrentproject-Addstructuredconcurrencypatterntocodebasemeta_application:-Createmoduleinrelevantskill-Addtoknowledge-corpusasreferencestorage:location:docs/knowledge-corpus/structured-concurrency.mdformat:memory_palace_entrymaturity:growingpruning:displaces:-Oldasyncerrorpatterns(markdeprecated)complements:-Existingerrorhandlingmodule-Asyncpatternsdocumentation
Verification: Run the command with --help flag to verify availability.
Queue Processing
Research sessions and external content are automatically queued for review in docs/knowledge-corpus/queue/.
Processing Queue Entries
# List pending queue entriesls -1t docs/knowledge-corpus/queue/*.yaml
# Review specific entrycat 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
Verification: Run the command with --help flag to verify availability.
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
**Verification:** Run the command with `--help` flag to verify availability.
pending_review → [Review] → approved/rejected
approved → [Create Entry] → processed
processed → [Archive] → queue/archive/
Verification: Run the command with --help flag to verify availability.
Automation
Run uv run python 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
Discussion Promotion: Invoked in Step 7 (PROMOTE)
for evergreen entries (score 80+). Publishing is the
default action.
See modules/discussion-promotion.md for full workflow.
Hook Integration
Memory-palace hooks automatically detect content that may need knowledge intake processing:
Automatic Triggers
Hook
Event
When Triggered
url_detector
UserPromptSubmit
User message contains URLs
web_content_processor
PostToolUse (WebFetch/WebSearch)
After fetching web content
local_doc_processor
PostToolUse (Read)
Reading files in knowledge paths
research_queue_integration
SessionEnd
Research sessions with 3+ WebSearch calls
Hook Signals
When hooks detect potential knowledge content, they add context messages:
**Verification:** Run `pytest -v` to verify tests pass.
Memory Palace: New web content fetched from {url}.
Consider running knowledge-intake to evaluate and store if valuable.
Verification: Run the command with --help flag to verify availability.
**Verification:** Run the command with `--help` flag to verify availability.
Memory Palace: Reading local knowledge doc '{path}'.
This path is configured for knowledge tracking.
Consider running knowledge-intake if this contains valuable reference material.
Verification: Run the command with --help flag to verify availability.
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, in [0, 100]maturity:"growing"# seedling, growing, evergreenrouting_type:"both"# local, meta, bothlast_updated:"2025-12-06T..."
Verification: Run the command with --help flag to verify availability.
