gadaalabs/claude-code-on-steroids/chronicle

Learning From Experience

Overview

CHRONICLE — Chronicles are authoritative records of what happened and why — consulted before repeating history. When invoked: searches the ReasoningBank for patterns matching the current task before work begins, and stores a structured entry (problem → investigation → solution → reusable insight) after the task completes.

Core principle: Never solve the same problem twice without remembering what worked.

This skill adds self-learning capability to Superpowers. After each task, you store what worked. Before each task, you search for relevant past patterns.

Announce at start: "Running CHRONICLE to search and store patterns."

The Learning Loop

1. Search ReasoningBank → 2. Review matches → 3. Apply patterns → 4. Execute task → 5. Extract pattern → 6. Store in ReasoningBank

Phase 1: Pre-Task Pattern Retrieval

BEFORE starting any non-trivial task:

Progressive Filtering Protocol (Token-Efficient Search)

Use a 3-layer funnel — stop at the earliest layer that answers the question. Never read all pattern files upfront.

Layer 1 — Index scan (~50 tokens):
  Read MEMORY.md only. Identify candidate pattern files by name/description.
  → If no candidates match, skip to Layer 3 or declare no patterns found.
 
Layer 2 — Keyword grep (~100-200 tokens):
  Grep candidate files for specific keywords from the task.
  → Narrows candidates to top 3-5 matches.
 
Layer 3 — Full read (~500-1000 tokens each):
  Read only the confirmed-relevant files in full.
  → Maximum 3 files. Stop when pattern found.

Token savings vs. reading all files: ~5-10x. Never reach Layer 3 without Layer 1 and 2 first.

Step 1: Search ReasoningBank

The ReasoningBank lives in the project memory directory alongside MEMORY.md. Pattern files use the naming convention: pattern_<domain>_<keywords>.md

How to search — use Grep on the memory directory:

# Search for patterns by keyword
grep -rl "<keyword>" ~/.claude/projects/<project-hash>/memory/
grep -l "pattern_" ~/.claude/projects/<project-hash>/memory/
 
# Read MEMORY.md index first — it lists all stored patterns
cat ~/.claude/projects/<project-hash>/memory/MEMORY.md

In Claude Code sessions — use the Read and Grep tools:

1. Read MEMORY.md to see the pattern index
2. Grep the memory directory for relevant keywords
3. Read matching pattern files in full

Query construction — search for:

• Problem category: bug-fix, feature, refactor, integration, debugging
• Technologies: react, postgresql, redis, aws-lambda
• Keywords from the goal: latency, validation, caching, auth

Example searches:

grep -rl "connection-pool" ~/.claude/projects/<hash>/memory/
grep -rl "form-validation react" ~/.claude/projects/<hash>/memory/
grep -rl "circular-dependency" ~/.claude/projects/<hash>/memory/

Step 2: Review Top 5 Matches

For each match, extract:

• What worked: The successful approach
• What failed: Dead ends encountered
• Time to solution: How long it took
• Context differences: How is this situation different?

Step 3: Apply Relevant Patterns

If pattern applies directly:

"Found matching pattern from <key>: <one-sentence summary>. Applying this approach."

If pattern partially applies:

"Pattern from <key> is relevant but needs adaptation: <what changes>"

If no patterns found:

"No matching patterns in ReasoningBank. This is a novel problem — will create new pattern after solving."

Phase 2: Post-Task Pattern Storage

AFTER each task completes successfully:

Step 4: Extract the Pattern

Answer these questions:

1. Problem statement: What was the actual problem? (one sentence)
2. Solution approach: What worked? (2-3 sentences)
3. Key insight: What was the non-obvious part? (one sentence)
4. Failed attempts: What didn't work? (list)
5. Time to solution: How long did it take?
6. Applicability: When should this pattern be reused?

Step 5: Store in ReasoningBank

How to store — write a pattern file to memory:

File name: pattern_<domain>_<keywords>.md Location: ~/.claude/projects/<project-hash>/memory/

Then add a one-line entry to MEMORY.md:

- [Pattern: <name>](pattern_<domain>_<keywords>.md) — <one-line hook>

Pattern file format:

---
name: Pattern — <descriptive-name>
description: <one-line description for relevance matching>
type: project
---
 
## Pattern: <descriptive-name>
 
**Key:** `<domain>:<problem-type>:<keywords>`
**Timestamp:** `<ISO-8601>`
 
### Problem
<One paragraph describing the problem>
 
### Solution
<What worked, with code snippet if applicable>
 
### Failed Attempts
-<what you tried that didn't work and why>
 
### Key Insight
<The non-obvious part — this is the most important field>
 
### When to Apply
-<situation 1>
-<situation 2>
 
### When NOT to Apply
-<situation where this pattern is wrong>
 
### Metrics
-Time to solution: <minutes>
-Complexity: <low|medium|high>
-Confidence: <high|medium|low>

Key naming convention:

<domain>:<problem-type>:<keywords>
 
Examples:
- frontend:bug:react-state-sync
- backend:feature:jwt-authentication
- database:debug:connection-pool-exhaustion
- ml:feature:feature-engineering-pipeline
- ai:feature:rag-chunking-strategy

Step 6: Consolidate Memory (EWC++-style)

Before storing, check for conflicts:

1. Search for related keys:

`` grep -rl "<domain> <problem-type>" ~/.claude/projects/<hash>/memory/ ``

1. Check for contradictions:

• Does new pattern contradict old pattern?
• Is old pattern a special case of new pattern?
• Should old pattern be updated or deprecated?

1. Consolidation actions:

• Merge: If patterns are similar, combine into one
• Deprecate: If new pattern strictly better, mark old as deprecated
• Specialize: If contexts differ, clarify boundaries
• Keep both: If both valid in different contexts

Example consolidation:

OLD: `backend:auth:session-tokens` — "Use session tokens for auth"
NEW: `backend:auth:jwt-tokens` — "Use JWT for stateless auth"
 
Consolidation: Both valid. Update OLD to clarify "Use session tokens WHEN server-side state OK"
               Update NEW to clarify "Use JWT WHEN stateless required"

Pattern Quality Gates

Before storing, verify:

• [ ] Pattern actually worked (verified with tests or production success)
• [ ] Generalizable beyond this specific case
• [ ] Clear when to apply vs. not apply
• [ ] Includes minimal working code snippet
• [ ] Documents failed attempts (anti-patterns)
• [ ] Has descriptive key name (searchable)

ReasoningBank Storage — Native Memory Integration

CRITICAL: Use the auto-memory system, not docs/patterns/.

The Claude Code auto-memory system (~/.claude/projects/<project-hash>/memory/) is loaded at EVERY session start via MEMORY.md. Patterns stored there are automatically available in future conversations without any manual search.

Storage Protocol

Step 1: Write the pattern file

~/.claude/projects/<project-hash>/memory/pattern_<domain>_<keyword>.md

Use the standard pattern format (see Step 5 in Phase 2 above).

Step 2: Register in MEMORY.md

Add one line to MEMORY.md:

- [Pattern: <name>](pattern_<domain>_<keyword>.md) — <one-line hook describing when to apply>

This makes it auto-loaded next session.

Finding Your Memory Directory

The project memory directory for this session is shown in system context. It follows the pattern:

~/.claude/projects/<hash-of-project-path>/memory/

For this project: Check the MEMORY.md path shown in system context at conversation start.

Storage Priority

Never use `docs/patterns/` as primary storage — it requires manual search and is not auto-loaded.

Auto-Memory Structure

~/.claude/projects/<hash>/memory/
├── MEMORY.md                          ← index (auto-loaded)
├── pattern_frontend_react-state.md    ← pattern files
├── pattern_backend_jwt-auth.md
├── pattern_ml_data-drift.md
├── pattern_ai_rag-chunking.md
├── pattern_debug_race-condition.md
└── pattern_arch_microservice-split.md

Pattern Retrieval During Task Execution

If stuck mid-task:

STOP. Search ReasoningBank:
grep -rl "<current-blocker>" ~/.claude/projects/<hash>/memory/
 
If found: Apply suggested approach
If not found: This is novel — document the breakthrough when you find it

Pattern-Enhanced Subagent Dispatch

When dispatching subagents, include relevant patterns:

Task N: <task name>
 
**Relevant Patterns Found:**
1. `<key-1>` — <one-sentence summary>
2. `<key-2>` — <one-sentence summary>
 
**Apply these patterns:**
<specific guidance based on past success>
 
**Avoid these anti-patterns:**
<what failed before>

Metrics and Improvement

Track pattern effectiveness:

Red Flags

Never:

• Store patterns that didn't actually work
• Store without documenting failed attempts
• Store with vague key names (unsearchable)
• Skip consolidation (leads to contradictions)
• Store only successes (document failures too)

Integration with Other Skills

Used by:

• superpowers:architect — Search for past design approaches
• superpowers:hunter — Search for similar bug patterns
• superpowers:phantom — Include patterns in task context
• superpowers:blueprint — Reference patterns in plan steps

Calls:

• superpowers:sentinel — Verify pattern worked before storing

Example: Complete Learning Cycle

TASK: Fix React state sync bug
 
PRE-TASK:
> Searching ReasoningBank: "frontend:bug:react-state"
> Found 2 matches:
> 1. frontend:bug:react-state-sync — "Use useEffect dependency array"
> 2. frontend:bug:stale-closure — "Capture latest state with ref"
> Applying pattern #1 — relevant to this sync issue
 
EXECUTION:
> Applied pattern: Added dependency array to useEffect
> Bug fixed ✓
 
POST-TASK:
> Extracting pattern:
>   Problem: React component showed stale data after prop change
>   Solution: Added missing dependency to useEffect array
>   Key insight: Dependency array was incomplete (missing user.id)
>   Failed: Tried force re-render (wrong approach)
>   Time: 15 minutes
>   Applicability: Any useEffect with stale data
>
> Storing: `frontend:bug:useeffect-incomplete-deps`
> Consolidating: No conflicts found
> Pattern stored ✓

Final Rule

Every solved problem → pattern stored
Every new problem → pattern searched
Never solve twice without remembering