reflexion
Record feedback on pattern effectiveness. Stores episodes that train the recommendation system and enable pattern discovery via learner.
When & Why to Use This Skill
The Reflexion skill enables AI agents to implement a robust self-learning loop by recording, retrieving, and analyzing feedback on task performance and pattern effectiveness. It acts as a specialized long-term memory layer that stores work 'episodes,' allowing agents to optimize future recommendations, avoid repeating past mistakes, and discover high-efficiency workflows through data-driven critiques and performance tracking.
Use Cases
- Post-Task Performance Evaluation: Recording detailed feedback after completing a task to document whether a specific technical pattern (e.g., a database adapter) worked as expected or required modifications.
- Historical Failure Analysis: Retrieving past failed episodes related to a specific topic (like 'MQTT configuration') to understand previous pitfalls and ensure the agent adopts a more successful approach in the current session.
- Workflow Optimization and Discovery: Providing structured data for learning systems to automatically identify and formalize new reusable patterns based on successful historical work episodes.
- Critique Synthesis for Process Improvement: Aggregating lessons learned across multiple sessions to generate a summary of architectural or operational gaps, helping to refine Standard Operating Procedures (SOPs).
| name | "reflexion" |
|---|---|
| description | "Record feedback on pattern effectiveness. Stores episodes that train the recommendation system and enable pattern discovery via learner." |
Reflexion - Evaluate Pattern Effectiveness
What This Skill Does
Records feedback on patterns and approaches used during work. This feedback:
- Trains the recommendation system for better pattern suggestions
- Provides data for
learnerskill to auto-discover new patterns - Tracks what works and what doesn't over time
Use this AFTER completing work to record what helped and what didn't.
Quick Reference
# Store feedback
agentdb reflexion store "session-id" "task description" reward success "critique"
# Retrieve similar experiences
agentdb reflexion retrieve "search query" --k 5 --only-successes
# Get critique summary
agentdb reflexion critique-summary "topic" true
Primary Method: Store Feedback
agentdb reflexion store \
"dp-004" \
"Used domain-adapter pattern for new HTTP source" \
1.0 \
true \
"Pattern was complete - followed Source trait steps exactly, tests passed first try"
Parameters (positional)
| Position | Parameter | Description |
|---|---|---|
| 1 | session-id | Feature ID (e.g., dp-004, air-011) |
| 2 | task | Description of what you did |
| 3 | reward | Success score 0-1 |
| 4 | success | true or false |
| 5 | critique | Specific feedback (required) |
| 6 | input | Optional: task input |
| 7 | output | Optional: task output |
| 8 | latency-ms | Optional: execution time |
| 9 | tokens | Optional: tokens used |
Examples
Pattern Worked Well
agentdb reflexion store \
"dp-004" \
"Used domain-adapter pattern for new HTTP source" \
1.0 \
true \
"Pattern was complete - followed Source trait steps exactly, tests passed first try"
Pattern Partially Worked
agentdb reflexion store \
"dp-004" \
"Used add-stream pattern but needed adjustment" \
0.6 \
true \
"Pattern missing retention field requirement added in v2.0 - should update pattern via save-pattern"
Pattern Failed
agentdb reflexion store \
"dp-004" \
"Pattern mqtt-routing failed for multi-topic subscription" \
0.2 \
false \
"Pattern assumes single topic per source - needs update for multi-topic. Used workaround with topic array."
No Pattern Found
agentdb reflexion store \
"dp-004" \
"Implemented TimescaleDB continuous aggregate - no existing pattern" \
0.85 \
true \
"No pattern existed. Created new approach using hypertable + continuous_aggregate. Should save as new pattern."
Retrieve Similar Experiences
# Find successful similar work
agentdb reflexion retrieve "HTTP source implementation" \
--k 5 \
--only-successes \
--min-reward 0.7
# Find failures to learn from
agentdb reflexion retrieve "MQTT configuration" \
--k 5 \
--only-failures
# Get synthesized summary
agentdb reflexion retrieve "parquet storage" \
--k 10 \
--synthesize-context
Retrieve Parameters
| Parameter | Description |
|---|---|
--k |
Number of results |
--only-successes |
Only successful episodes |
--only-failures |
Only failed episodes |
--min-reward |
Minimum reward threshold |
--synthesize-context |
Generate summary |
Get Critique Summary
Aggregate lessons from critiques:
# Get critique summary for failures
agentdb reflexion critique-summary "mqtt" true
# Get all critiques for a topic
agentdb reflexion critique-summary "architecture" false
Reward Scale
| Score | Meaning | When to Use |
|---|---|---|
| 1.0 | Perfect | Pattern/approach worked exactly as expected |
| 0.8 | Good | Minor adjustments needed |
| 0.6 | Partial | Significant modifications required |
| 0.4 | Weak | Marginally helpful, major workarounds |
| 0.2 | Failed | Didn't work, caused issues |
| 0.0 | Harmful | Actively wrong, wasted time |
Session ID Convention
Use consistent session IDs for aggregation:
| Session ID | Use For |
|---|---|
{feature-id} |
Feature work (e.g., dp-004, air-011) |
{feature-id}-{phase} |
Specific phase (e.g., dp-004-spec) |
maintenance |
Bug fixes, refactoring |
exploration |
Research, spikes, experiments |
Critique Best Practices
Good critiques (specific, actionable):
"Pattern was complete - followed steps exactly and deployment succeeded"
"Missing retention field that's now required in v2.0 schema"
"TimescaleDB connection pattern assumed localhost but we use Docker networking"
"Architecture pattern outdated - ADR-005 superseded the approach"
Poor critiques (vague, unusable):
"It worked" # Too vague
"Failed" # No actionable info
"Good pattern" # Doesn't explain what made it good
The Pattern Workflow
1. BEFORE work: get-pattern → Search for relevant patterns
2. DURING work: Apply patterns, note gaps and discoveries
3. AFTER work: reflexion → Record what helped (THIS SKILL)
save-pattern → Store NEW discoveries (if any)
learner → Auto-discover patterns from episodes (periodic)
After Recording Feedback
If your critique identifies a pattern that needs updating:
# 1. Record the feedback (this skill)
agentdb reflexion store \
"dp-004" \
"Used add-stream pattern" \
0.6 \
true \
"Pattern missing required retention field"
# 2. Update the pattern (save-pattern skill)
agentdb skill create \
"add-stream-v2" \
"Add Data Stream (v2.0): Now requires retention field. Steps: 1) Create config.yaml, 2) Add retention field (required), 3) Run sync..." \
"tags: streams, config, updated"
Related Skills
get-pattern- Search patterns BEFORE worksave-pattern- Store NEW patterns after discovering reusable approacheslearner- Auto-discover patterns from reflexion episodes
What NOT to Use This For
| Don't Record | Use Instead |
|---|---|
| New patterns you discovered | save-pattern |
| Swarm coordination state | claude-flow memory tools |
| Transient task/agent memory | claude-flow memory tools |
| Architecture decisions | save-pattern |
Reflexion is for FEEDBACK on work done, not storing new knowledge.