ssr-session-review-learning-capture

name	ssr - Session Review (Learning Capture)
description	Capture learnings at end of session for continuous improvement. Reviews what worked, what did not, surprises, and procedure gaps.
output	{"format":"prose"}

Session Review (Learning Capture)

Review the current session and capture learnings for future sessions.

Input: $ARGUMENTS (optional: specific area to focus review on)

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Purpose

Sessions contain valuable learning opportunities that are lost if not captured. This procedure systematically extracts learnings to improve future sessions.

Principle: Every session should teach. Capture what worked, what did not, and what surprised you.

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Step 1: Session Summary

Summarize what happened in this session:

Aspect	Description
User's initial input	[what they brought]
Type	GOAL / PROBLEM / QUESTION / DECISION / SITUATION / FEELING
Procedures invoked	[list of /procedures used]
Outcome	[what was achieved]
Session duration	[if known]

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Step 2: What Worked

Identify approaches that were effective:

What Worked	Why It Worked	Reusable?
[approach 1]	[reason for success]	YES / NO
[approach 2]	[reason for success]	YES / NO
[approach 3]	[reason for success]	YES / NO

Questions to ask:

• Which procedure generated the most value?
• What questions unlocked insight?
• What framing helped the user?
• What would I do again?

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Step 3: What Did Not Work

Identify approaches that were ineffective:

What Did Not Work	Why It Failed	Avoidable?
[approach 1]	[reason for failure]	YES / NO
[approach 2]	[reason for failure]	YES / NO

Questions to ask:

• Where did I go off track?
• What procedure was wrong for this situation?
• What assumption was incorrect?
• What would I skip next time?

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Step 4: Surprises

Identify unexpected findings:

Surprise	Implication
[unexpected finding 1]	[what it means for future]
[unexpected finding 2]	[what it means for future]

Questions to ask:

• What was harder than expected?
• What was easier than expected?
• What did the user teach me?
• What did I not anticipate?

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Step 5: Procedure Gaps

Identify missing or inadequate procedures:

Gap Type	Description	Proposed Solution
Missing procedure	[needed but does not exist]	[what it would do]
Inadequate procedure	[exists but insufficient]	[what needs improvement]
Wrong chaining	[procedures invoked in wrong order]	[correct order]
Overly complex	[procedure too heavy for situation]	[simpler alternative]

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Step 6: Convergent Validation of Learnings

For each potential learning, apply convergent validation:

Learning	Grounded?	Fixed Point?	Convergent?	Practical?	Score
[learning 1]	[O/T/D marker?]	[stable under re-analysis?]	[multiple paths lead here?]	[actionable?]	[0-4]
[learning 2]	[O/T/D marker?]	[stable under re-analysis?]	[multiple paths lead here?]	[actionable?]	[0-4]

Decision:

• 4 checks pass: Accept with high confidence
• 3 checks pass: Accept with medium confidence
• 2 checks pass: Flag for review (preliminary)
• <2 checks pass: Reject (do not capture)

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Step 7: Capture Validated Learnings

For each accepted learning, format for storage:

LEARNING CAPTURE:

ID: [auto-generated]
TYPE: what_worked | what_didnt | surprise | insight | pattern | procedure_gap
CONTENT: [the learning in one sentence]
CONTEXT: [when/where this applies]
DOMAIN: goal | problem | question | decision | situation | feeling | general
CONFIDENCE: high | medium | preliminary
EVIDENCE: [supporting evidence from session]
GROUNDING: [O: observed] | [T: tested] | [D: derived]

Execute: Store using python scripts/session_learning.py capture [type] [content] [context]

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Step 8: Review Previous Learnings

Check if any previous learnings were validated or contradicted in this session:

Previous Learning	Session Evidence	Update
[learning ID]	[what happened]	VALIDATE / CONTRADICT / NO CHANGE

Execute: Update using python scripts/session_learning.py validate [learning_id] [true/false]

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Step 9: Test Guess Outcomes

For any recommendations or actions taken based on guesses in this session:

Recommendation	Based On Guess	Outcome	Update Confidence
[what was recommended]	[underlying guess]	WORKED / DIDNT_WORK / UNKNOWN	[increase/decrease/no change]

Questions to ask:

• Did the recommended action achieve its goal?
• Did any assumption prove false?
• Did any guess prove correct?

Capture outcomes:

• If WORKED: capture_learning(type="what_worked", content="[recommendation] based on [guess]")
• If DIDNT_WORK: capture_learning(type="what_didnt", content="[recommendation] failed because [reason]")

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Step 10: Check for Contradictions

Review any potential conflicts detected:

Execute: python scripts/session_learning.py conflicts

For each conflict:

Learning A	Learning B	Resolution
[ID] [content]	[ID] [content]	KEEP_A / KEEP_B / KEEP_BOTH / NEEDS_MORE_DATA

Resolve: Archive the contradicted learning using validate with false

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Output Format

## SESSION REVIEW

### Summary
- Input: [user's input]
- Type: [GOAL/PROBLEM/etc]
- Procedures: [list]
- Outcome: [result]

### What Worked
1. [learning] - [why]
2. [learning] - [why]

### What Did Not Work
1. [learning] - [why]

### Surprises
1. [finding] - [implication]

### Procedure Gaps
1. [gap] - [proposed solution]

### Learnings Captured
[list of learning IDs captured]

### Previous Learnings Updated
[list of learning IDs validated/contradicted]

### Recommendations for Future
1. [recommendation]
2. [recommendation]

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Integration Points

At session end: Invoke /ssr to capture learnings.

At session start: Retrieve relevant learnings with:

python scripts/session_learning.py context [domain]

Periodically: Review learning stats with:

python scripts/session_learning.py stats

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Execution Checklist

• Session summarized
• What worked identified (aim for 2-3)
• What did not work identified (aim for 1-2)
• Surprises noted
• Procedure gaps identified
• Learnings validated through convergent checks
• Accepted learnings captured to storage
• Previous learnings updated if applicable

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Execute now: Review this session and capture learnings.