Get Pattern - Retrieve Application Knowledge

What This Skill Does

Retrieves established application patterns (architecture, procedures, conventions) for the Neural Data Platform using three complementary signals:

1. Pattern Search (primary) — Semantic similarity against the patterns table
2. Recall with Certificate (enriched) — Blends similarity + causal uplift + recency
3. Learning Predict (optional) — RL-based action recommendations from past episodes

Use this BEFORE implementing anything to ensure you follow project standards.

---

Quick Reference

# 1. Search patterns by task description (primary)
mcp__agentdb__agentdb_pattern_search(task="domain adapter pattern", k=5)

# 2. Enriched recall with causal scoring (enhanced)
mcp__agentdb__recall_with_certificate(query="domain adapter pattern", k=12)

# 3. RL-recommended actions (optional, requires learning session)
mcp__agentdb__learning_predict(session_id="ndp-learning-v1", state="implementing new domain adapter")

# 4. Explainable recommendations with evidence
mcp__agentdb__learning_explain(query="domain adapter pattern", k=5)

# Get pattern statistics
mcp__agentdb__agentdb_pattern_stats()

# Fallback: search reflexion episodes
mcp__agentdb__reflexion_retrieve(task="how to add a stream", k=5, only_successes=true)

---

Primary Method: Pattern Search

mcp__agentdb__agentdb_pattern_search(
  task="<query>",
  k=<number>,
  threshold=<0-1>,
  filters={taskType: "architecture:*", minSuccessRate: 0.8}
)

CRITICAL: The parameter is task, NOT query. Using query will crash. This is different from recall_with_certificate which uses query.

Parameters

Parameter	Description	Default
task	What you're looking for (semantic search)	required
k	Number of results	10
threshold	Minimum similarity (0-1)	0
filters.taskType	Filter by category	optional
filters.minSuccessRate	Minimum success rate	optional
filters.tags	Filter by tags	optional

Examples

# Find architecture patterns
mcp__agentdb__agentdb_pattern_search(task="domain adapter pattern", k=5)

# Find deployment procedures
mcp__agentdb__agentdb_pattern_search(task="deploy to raspberry pi", k=3)

# Find naming conventions with filter
mcp__agentdb__agentdb_pattern_search(
  task="naming conventions streams fields",
  k=5,
  filters={taskType: "conventions:*"}
)

# Find high-success patterns only
mcp__agentdb__agentdb_pattern_search(
  task="mqtt configuration",
  k=5,
  filters={minSuccessRate: 0.9}
)

---

Fallback Method: Reflexion Retrieve

If no patterns exist, search past experiences:

mcp__agentdb__reflexion_retrieve(
  task="HTTP source implementation",
  k=5,
  only_successes=true,
  min_reward=0.7
)

# Get synthesized context
mcp__agentdb__reflexion_retrieve(
  task="timescaledb schema",
  k=10,
  synthesize_context=true
)

Retrieve Parameters

Parameter	Type	Description
task	string	Task description to find similar work
k	number	Number of results
only_successes	boolean	Only successful episodes
min_reward	number	Minimum success score (0-1)
synthesize_context	boolean	Generate coherent summary

---

Enhanced Method: Recall with Certificate

Blends three signals for richer retrieval: similarity (how well it matches), causal uplift (did using this lead to success?), and recency (how recent is the knowledge?).

mcp__agentdb__recall_with_certificate(
  query="<what you're looking for>",
  k=12,
  alpha=0.7,
  beta=0.2,
  gamma=0.1
)

Parameters

Parameter	Type	Description	Default
query	string	What you're looking for (semantic search)	required
k	number	Number of results	12
alpha	number	Weight for similarity (0-1)	0.7
beta	number	Weight for causal uplift (0-1)	0.2
gamma	number	Weight for recency (0-1)	0.1

When to Use

• When pattern_search returns results but you want to prioritize proven patterns (increase beta)
• When working on a recently-changed area (increase gamma for freshest knowledge)
• When you want a provenance certificate showing why each result was ranked

Tuning Weights

Scenario	alpha	beta	gamma
Default (balanced)	0.7	0.2	0.1
Proven patterns only	0.4	0.5	0.1
Recent changes matter	0.5	0.1	0.4
Pure similarity (like pattern_search)	1.0	0.0	0.0

---

Optional Method: Learning Predict

Gets RL-based action recommendations based on what worked in past episodes. Requires a persistent learning session — only works after learning_start_session has been called and seeded with data.

mcp__agentdb__learning_predict(
  session_id="ndp-learning-v1",
  state="implementing Silver ETL for new weather stream"
)

Parameters

Parameter	Type	Description	Default
session_id	string	Learning session ID (see MEMORY.md for current ID)	required
state	string	Description of your current task/context	required

Returns

• Recommended action with confidence score
• Alternative actions ranked by expected reward
• Use alongside pattern_search results to validate your approach

Important Notes

• If no learning session exists yet, skip this step — it will error
• The session_id is stored in auto-memory (MEMORY.md) once created
• This improves over time as more reflexion data feeds the RL model

---

Optional Method: Learning Explain

Gets explainable recommendations with supporting evidence from past episodes and causal reasoning chains.

mcp__agentdb__learning_explain(
  query="deploying new stream to Pi",
  k=5,
  explain_depth="detailed",
  include_evidence=true,
  include_confidence=true,
  include_causal=true
)

Parameters

Parameter	Type	Description	Default
query	string	Task description to get recommendations for	required
k	number	Number of recommendations to return	5
explain_depth	string	Detail level: "summary", "detailed", or "full"	"detailed"
include_evidence	boolean	Include supporting evidence from past episodes	true
include_confidence	boolean	Include confidence scores	true
include_causal	boolean	Include causal reasoning chains	true

When to Use

• When you want to understand why an approach is recommended
• When making high-stakes decisions (architecture changes, deployment procedures)
• When pattern_search returned multiple conflicting patterns and you need to decide

---

Pattern Categories

Category	Example Queries
Architecture	"domain adapter pattern", "hexagonal architecture"
Data Flow	"ingestion pipeline", "bronze silver gold"
Development	"add new stream", "implement source trait"
Deployment	"docker deployment", "raspberry pi setup"
Troubleshooting	"mqtt not working", "parquet write errors"
Conventions	"naming conventions", "code organization"

---

Interpreting Results

Results from agentdb_pattern_search include:

Field	Meaning
ID	Pattern identifier
taskType	Category (e.g., architecture:domain-adapter)
Similarity	How well it matches your query (0-1)
Success Rate	How often this pattern succeeded (0-100%)
Approach	The pattern content/description
Uses	Number of times used

High-value patterns: Success Rate > 80% AND Similarity > 0.3

Deprecated patterns: Check reflexion episodes - patterns with reward=0.0 and success=false may be obsolete.

---

Typical Workflow

# Step 1: Pattern search (primary — always do this)
mcp__agentdb__agentdb_pattern_search(task="what I'm about to implement", k=5)

# Step 2: Enriched recall (enhanced — do this for important decisions)
mcp__agentdb__recall_with_certificate(query="what I'm about to implement", k=12)
# Combines similarity + causal uplift + recency for richer ranking

# Step 3: RL prediction (optional — only if learning session exists)
mcp__agentdb__learning_predict(
  session_id="ndp-learning-v1",
  state="description of current task context"
)

# Step 4: Combine results
# - Pattern search gives you the content
# - Recall certificates show which patterns are causally proven
# - Learning predict suggests the best action based on past outcomes
# - If results conflict, prefer patterns with high causal uplift

# Step 5: If nothing found — check reflexion for past experiences
mcp__agentdb__reflexion_retrieve(task="similar task", k=5, only_successes=true)

# Step 6: After work — record feedback (reflexion skill)
mcp__agentdb__reflexion_store(
  session_id="feature-id",
  task="task description",
  reward=0.9,
  success=true,
  critique="Pattern worked well"
)

# Step 7: If you discovered something new — save it (save-pattern skill)
mcp__agentdb__agentdb_pattern_store(
  taskType="category:name",
  approach="description",
  successRate=0.9,
  tags=["tag1", "tag2"]
)

Minimum viable workflow: Steps 1 + 6 (pattern search + reflexion). Steps 2-3 are enhancements for higher-quality retrieval.

---

CRITICAL: Record Pattern Usage

After using a pattern, always use the reflexion skill to record whether it helped:

# Pattern worked well
mcp__agentdb__reflexion_store(
  session_id="dp-004",
  task="Used domain-adapter pattern for new HTTP source",
  reward=1.0,
  success=true,
  critique="Pattern was complete - followed steps exactly, tests passed"
)

# Pattern needed fixes
mcp__agentdb__reflexion_store(
  session_id="dp-004",
  task="Used add-stream pattern but needed adjustment",
  reward=0.6,
  success=true,
  critique="Pattern missing retention field - should update via save-pattern"
)

Without feedback, the system can't learn which patterns work.

---

If No Patterns Found

1. Check pattern stats:

   mcp__agentdb__agentdb_pattern_stats()
   

2. Search reflexion episodes:

   mcp__agentdb__reflexion_retrieve(task="your query", k=10, synthesize_context=true)
   

3. Check file-based documentation:

   • docs/architecture/ - Architecture documents
   • docs/procedures/ - Step-by-step procedures
   • product/features/*/architecture/ - Feature ADRs

4. After implementing, store the new pattern via save-pattern

---

The Pattern Workflow

1. BEFORE work:  get-pattern  → Search for relevant patterns (THIS SKILL)
2. DURING work:  Apply the pattern, note what works/gaps
3. AFTER work:   reflexion    → Record if pattern helped (required)
                 save-pattern → Store NEW discoveries (if any)
                 learner      → Auto-discover patterns from episodes (periodic)

---

Related Skills

• save-pattern - Store NEW patterns after discovering reusable approaches
• reflexion - Record feedback on pattern effectiveness (REQUIRED after using patterns)
• pattern-manage - Delete, deprecate, update, deduplicate patterns (lifecycle management)
• learner - Auto-discover patterns from successful episodes (user-invoked)

---

Parameter Naming Reference

Different AgentDB tools use different parameter names for the search text. Using the wrong name causes crashes.

Tool	Search Parameter	Other Required
agentdb_pattern_search	task	—
recall_with_certificate	query	—
learning_predict	state	session_id
learning_explain	query	—
reflexion_retrieve	task	—

---

What NOT to Use This For

Don't Search For	Use Instead
Current swarm status	claude-flow swarm tools
Agent task state	claude-flow task tools
Working memory	claude-flow memory tools
Session context	claude-flow memory with TTL

Patterns are PERMANENT application knowledge, not transient swarm state.