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# GENOME.md — compounding-intelligence
*Auto-generated codebase genome. See timmy-home#676.*
---
## Project Overview
**What:** A system that turns 1B+ daily agent tokens into durable, compounding fleet intelligence.
**Why:** Every agent session starts at zero. The same mistakes get made repeatedly — the same HTTP 405 is rediscovered as a branch protection issue, the same token path is searched for from scratch. Intelligence evaporates when the session ends.
**How:** Three pipelines form a compounding loop:
```
SESSION ENDS → HARVESTER → KNOWLEDGE STORE → BOOTSTRAPPER → NEW SESSION STARTS SMARTER
MEASURER → Prove it's working
```
**Status:** Early stage. Template and test scaffolding exist. Core pipeline scripts (harvester.py, bootstrapper.py, measurer.py, session_reader.py) are planned but not yet implemented. The knowledge extraction prompt is complete and validated.
---
## Architecture
```mermaid
graph TD
A[Session Transcript<br/>.jsonl] --> B[Harvester]
B --> C{Extract Knowledge}
C --> D[knowledge/index.json]
C --> E[knowledge/global/*.md]
C --> F[knowledge/repos/{repo}.md]
C --> G[knowledge/agents/{agent}.md]
D --> H[Bootstrapper]
H --> I[Bootstrap Context<br/>2k token injection]
I --> J[New Session<br/>starts smarter]
J --> A
D --> K[Measurer]
K --> L[metrics/dashboard.md]
K --> M[Velocity / Hit Rate<br/>Error Reduction]
```
### Pipeline 1: Harvester
**Status:** Prompt designed. Script not implemented.
Reads finished session transcripts (JSONL). Uses `templates/harvest-prompt.md` to extract durable knowledge into five categories:
| Category | Description | Example |
|----------|-------------|---------|
| `fact` | Concrete, verifiable information | "Repository X has 5 files" |
| `pitfall` | Errors encountered, wrong assumptions | "Token is at ~/.config/gitea/token, not env var" |
| `pattern` | Successful action sequences | "Deploy: test → build → push → webhook" |
| `tool-quirk` | Environment-specific behaviors | "URL format requires trailing slash" |
| `question` | Identified but unanswered | "Need optimal batch size for harvesting" |
Output schema per knowledge item:
```json
{
"fact": "One sentence description",
"category": "fact|pitfall|pattern|tool-quirk|question",
"repo": "repo-name or 'global'",
"confidence": 0.0-1.0
}
```
### Pipeline 2: Bootstrapper
**Status:** Not implemented.
Queries knowledge store before session start. Assembles a compact 2k-token context from relevant facts. Injects into session startup so the agent begins with full situational awareness.
### Pipeline 3: Measurer
**Status:** Not implemented.
Tracks compounding metrics: knowledge velocity (facts/day), error reduction (%), hit rate (knowledge used / knowledge available), task completion improvement.
---
## Directory Structure
```
compounding-intelligence/
├── README.md # Project overview and architecture
├── GENOME.md # This file (codebase genome)
├── knowledge/ # [PLANNED] Knowledge store
│ ├── index.json # Machine-readable fact index
│ ├── global/ # Cross-repo knowledge
│ ├── repos/{repo}.md # Per-repo knowledge
│ └── agents/{agent}.md # Agent-type notes
├── scripts/
│ ├── test_harvest_prompt.py # Basic prompt validation (2.5KB)
│ └── test_harvest_prompt_comprehensive.py # Full prompt structure test (6.8KB)
├── templates/
│ └── harvest-prompt.md # Knowledge extraction prompt (3.5KB)
├── test_sessions/
│ ├── session_success.jsonl # Happy path test data
│ ├── session_failure.jsonl # Failure path test data
│ ├── session_partial.jsonl # Incomplete session test data
│ ├── session_patterns.jsonl # Pattern extraction test data
│ └── session_questions.jsonl # Question identification test data
└── metrics/ # [PLANNED] Compounding metrics
└── dashboard.md
```
---
## Entry Points and Data Flow
### Entry Point 1: Knowledge Extraction (Harvester)
```
Input: Session transcript (JSONL)
templates/harvest-prompt.md (LLM prompt)
Knowledge items (JSON array)
Output: knowledge/index.json + per-repo/per-agent markdown files
```
### Entry Point 2: Session Bootstrap (Bootstrapper)
```
Input: Session context (repo, agent type, task type)
knowledge/index.json (query relevant facts)
2k-token bootstrap context
Output: Injected into session startup
```
### Entry Point 3: Measurement (Measurer)
```
Input: knowledge/index.json + session history
Velocity, hit rate, error reduction calculations
Output: metrics/dashboard.md
```
---
## Key Abstractions
### Knowledge Item
The atomic unit. One sentence, one category, one confidence score. Designed to be small enough that 1000 items fit in a 2k-token bootstrap context.
### Knowledge Store
A directory structure that mirrors the fleet's mental model:
- `global/` — knowledge that applies everywhere (tool quirks, environment facts)
- `repos/` — knowledge specific to each repo
- `agents/` — knowledge specific to each agent type
### Confidence Score
0.01.0 scale. Defines how certain the harvester is about each extracted fact:
- 0.91.0: Explicitly stated with verification
- 0.70.8: Clearly implied by multiple data points
- 0.50.6: Suggested but not fully verified
- 0.30.4: Inferred from limited data
- 0.10.2: Speculative or uncertain
### Bootstrap Context
The 2k-token injection that a new session receives. Assembled from the most relevant knowledge items for the current task, filtered by confidence > 0.7, deduplicated, and compressed.
---
## API Surface
### Internal (scripts not yet implemented)
| Script | Input | Output | Status |
|--------|-------|--------|--------|
| `harvester.py` | Session JSONL path | Knowledge items JSON | PLANNED |
| `bootstrapper.py` | Repo + agent type | 2k-token context string | PLANNED |
| `measurer.py` | Knowledge store path | Metrics JSON | PLANNED |
| `session_reader.py` | Session JSONL path | Parsed transcript | PLANNED |
### Prompt (templates/harvest-prompt.md)
The extraction prompt is the core "API." It takes a session transcript and returns structured JSON. It defines:
- Five extraction categories
- Output format (JSON array of knowledge items)
- Confidence scoring rubric
- Constraints (no hallucination, specificity, relevance, brevity)
- Example input/output pair
---
## Test Coverage
### What Exists
| File | Tests | Coverage |
|------|-------|----------|
| `scripts/test_harvest_prompt.py` | 2 tests | Prompt file existence, sample transcript |
| `scripts/test_harvest_prompt_comprehensive.py` | 5 tests | Prompt structure, categories, fields, confidence scoring, size limits |
| `test_sessions/*.jsonl` | 5 sessions | Success, failure, partial, patterns, questions |
### What's Missing
1. **Harvester integration test** — Does the prompt actually extract correct knowledge from real transcripts?
2. **Bootstrapper test** — Does it assemble relevant context correctly?
3. **Knowledge store test** — Does the index.json maintain consistency?
4. **Confidence calibration test** — Do high-confidence facts actually prove true in later sessions?
5. **Deduplication test** — Are duplicate facts across sessions handled?
6. **Staleness test** — How does the system handle outdated knowledge?
---
## Security Considerations
1. **No secrets in knowledge store** — The harvester must filter out API keys, tokens, and credentials from extracted facts. The prompt constraints mention this but there is no automated guard.
2. **Knowledge poisoning** — A malicious or corrupted session could inject false facts. Confidence scoring partially mitigates this, but there is no verification step.
3. **Access control** — The knowledge store has no access control. Any process that can read the directory can read all facts. In a multi-tenant setup, this is a concern.
4. **Transcript privacy** — Session transcripts may contain user data. The harvester must not extract personally identifiable information into the knowledge store.
---
## The 100x Path (from README)
```
Month 1: 15,000 facts, sessions 20% faster
Month 2: 45,000 facts, sessions 40% faster, first-try success up 30%
Month 3: 90,000 facts, fleet measurably smarter per token
```
Each new session is better than the last. The intelligence compounds.
---
*Generated by codebase-genome pipeline. Ref: timmy-home#676.*

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#!/usr/bin/env python3
"""
Comprehensive test script for knowledge extraction prompt.
Validates prompt structure, requirements, and consistency.
"""
import json
import re
from pathlib import Path
def test_prompt_structure():
"""Test that the prompt has the required structure."""
prompt_path = Path("templates/harvest-prompt.md")
if not prompt_path.exists():
return False, "harvest-prompt.md not found"
content = prompt_path.read_text()
# Check for required sections
required_sections = [
"System Prompt",
"Instructions",
"Categories",
"Output Format",
"Confidence Scoring",
"Constraints",
"Example"
]
for section in required_sections:
if section.lower() not in content.lower():
return False, f"Missing required section: {section}"
# Check for required categories
required_categories = ["fact", "pitfall", "pattern", "tool-quirk", "question"]
for category in required_categories:
if category not in content:
return False, f"Missing required category: {category}"
# Check for required output fields
required_fields = ["fact", "category", "repo", "confidence"]
for field in required_fields:
if field not in content:
return False, f"Missing required output field: {field}"
# Check prompt size (should be ~1k tokens, roughly 4k chars)
if len(content) > 5000:
return False, f"Prompt too large: {len(content)} chars (max ~5000)"
if len(content) < 1000:
return False, f"Prompt too small: {len(content)} chars (min ~1000)"
return True, "Prompt structure is valid"
def test_confidence_scoring():
"""Test that confidence scoring is properly defined."""
prompt_path = Path("templates/harvest-prompt.md")
content = prompt_path.read_text()
# Check for confidence scale definitions
confidence_levels = [
("0.9-1.0", "explicitly stated"),
("0.7-0.8", "clearly implied"),
("0.5-0.6", "suggested"),
("0.3-0.4", "inferred"),
("0.1-0.2", "speculative")
]
for level, description in confidence_levels:
if level not in content:
return False, f"Missing confidence level: {level}"
if description.lower() not in content.lower():
return False, f"Missing confidence description: {description}"
return True, "Confidence scoring is properly defined"
def test_example_quality():
"""Test that examples are clear and complete."""
prompt_path = Path("templates/harvest-prompt.md")
content = prompt_path.read_text()
# Check for example input/output
if "example" not in content.lower():
return False, "No examples provided"
# Check that example includes all categories
example_section = content[content.lower().find("example"):]
# Look for JSON example
json_match = re.search(r'\{[\s\S]*"knowledge"[\s\S]*\}', example_section)
if not json_match:
return False, "No JSON example found"
example_json = json_match.group(0)
# Check for all categories in example
for category in ["fact", "pitfall", "pattern", "tool-quirk", "question"]:
if category not in example_json:
return False, f"Example missing category: {category}"
return True, "Examples are clear and complete"
def test_constraint_coverage():
"""Test that constraints cover all requirements."""
prompt_path = Path("templates/harvest-prompt.md")
content = prompt_path.read_text()
required_constraints = [
"No hallucination",
"only extract",
"explicitly",
"partial",
"failed sessions",
"1k tokens"
]
for constraint in required_constraints:
if constraint.lower() not in content.lower():
return False, f"Missing constraint: {constraint}"
return True, "Constraints cover all requirements"
def test_test_sessions():
"""Test that test sessions exist and are valid."""
test_sessions_dir = Path("test_sessions")
if not test_sessions_dir.exists():
return False, "test_sessions directory not found"
session_files = list(test_sessions_dir.glob("*.jsonl"))
if len(session_files) < 5:
return False, f"Only {len(session_files)} test sessions found, need 5"
# Check each session file
for session_file in session_files:
content = session_file.read_text()
lines = content.strip().split("\n")
# Check that each line is valid JSON
for i, line in enumerate(lines, 1):
try:
json.loads(line)
except json.JSONDecodeError as e:
return False, f"Invalid JSON in {session_file.name}, line {i}: {e}"
return True, f"Found {len(session_files)} valid test sessions"
def run_all_tests():
"""Run all tests and return results."""
tests = [
("Prompt Structure", test_prompt_structure),
("Confidence Scoring", test_confidence_scoring),
("Example Quality", test_example_quality),
("Constraint Coverage", test_constraint_coverage),
("Test Sessions", test_test_sessions)
]
results = []
all_passed = True
for test_name, test_func in tests:
try:
passed, message = test_func()
results.append({
"test": test_name,
"passed": passed,
"message": message
})
if not passed:
all_passed = False
except Exception as e:
results.append({
"test": test_name,
"passed": False,
"message": f"Error: {str(e)}"
})
all_passed = False
# Print results
print("=" * 60)
print("HARVEST PROMPT TEST RESULTS")
print("=" * 60)
for result in results:
status = "✓ PASS" if result["passed"] else "✗ FAIL"
print(f"{status}: {result['test']}")
print(f" {result['message']}")
print()
print("=" * 60)
if all_passed:
print("ALL TESTS PASSED!")
else:
print("SOME TESTS FAILED!")
print("=" * 60)
return all_passed, results
if __name__ == "__main__":
all_passed, results = run_all_tests()
# Save results to file
with open("test_results.json", "w") as f:
json.dump({
"all_passed": all_passed,
"results": results,
"timestamp": "2026-04-14T19:05:00Z"
}, f, indent=2)
print(f"Results saved to test_results.json")
# Exit with appropriate code
exit(0 if all_passed else 1)

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{"role": "user", "content": "Deploy the application to production"}
{"role": "assistant", "content": "I'll deploy the application to production.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ssh deploy@prod-server 'cd /app && git pull && docker-compose up -d'"}}]}
{"role": "tool", "content": "ssh: connect to host prod-server port 22: Connection refused"}
{"role": "assistant", "content": "Connection refused. Let me check the server status.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ping -c 3 prod-server"}}]}
{"role": "tool", "content": "PING prod-server (192.168.1.100): 56 data bytes\nRequest timeout for icmp_seq 0\nRequest timeout for icmp_seq 1\nRequest timeout for icmp_seq 2\n--- prod-server ping statistics ---\n3 packets transmitted, 0 packets received, 100.0% packet loss"}
{"role": "assistant", "content": "The server appears to be down. Let me check if there's an alternative deployment method."}
{"role": "user", "content": "Try using the VPS instead"}
{"role": "assistant", "content": "I'll try deploying to the VPS.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ssh deploy@vps-server 'cd /app && git pull && docker-compose up -d'"}}]}
{"role": "tool", "content": "Permission denied (publickey)."}
{"role": "assistant", "content": "Permission denied. The SSH key might not be set up correctly. Let me check the SSH configuration.", "tool_calls": [{"function": "terminal", "arguments": {"command": "cat ~/.ssh/config"}}]}
{"role": "tool", "content": "Host vps-server\n HostName 167.99.126.228\n User deploy\n IdentityFile ~/.ssh/id_rsa_vps"}
{"role": "assistant", "content": "The SSH config looks correct. Let me check if the key exists.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ls -la ~/.ssh/id_rsa_vps"}}]}
{"role": "tool", "content": "ls: /Users/user/.ssh/id_rsa_vps: No such file or directory"}
{"role": "assistant", "content": "The SSH key doesn't exist. This is a pitfall - we need to generate the SSH key first."}
{"role": "user", "content": "Generate a new SSH key for the VPS"}
{"role": "assistant", "content": "I'll generate a new SSH key.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ssh-keygen -t rsa -b 4096 -f ~/.ssh/id_rsa_vps -N ''"}}]}
{"role": "tool", "content": "Generating public/private rsa key pair.\nYour identification has been saved in /Users/user/.ssh/id_rsa_vps\nYour public key has been saved in /Users/user/.ssh/id_rsa_vps.pub\nThe key fingerprint is:\nSHA256:abc123... user@machine"}
{"role": "assistant", "content": "SSH key generated. Now I need to add it to the VPS."}

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{"role": "user", "content": "Check the Gitea API for open issues"}
{"role": "assistant", "content": "I'll check the Gitea API for open issues.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -H 'Authorization: token $GITEA_TOKEN' https://forge.alexanderwhitestone.com/api/v1/repos/Timmy_Foundation/compounding-intelligence/issues?state=open"}}]}
{"role": "tool", "content": "curl: (6) Could not resolve host: forge.alexanderwhitestone.com"}
{"role": "assistant", "content": "DNS resolution failed. Let me check the network connectivity.", "tool_calls": [{"function": "terminal", "arguments": {"command": "nslookup forge.alexanderwhitestone.com"}}]}
{"role": "tool", "content": "Server: 8.8.8.8\nAddress: 8.8.8.8#53\n\n** server can't find forge.alexanderwhitestone.com: NXDOMAIN"}
{"role": "assistant", "content": "The domain doesn't resolve. This might be a DNS issue. Let me try using the IP address directly.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -H 'Authorization: token $GITEA_TOKEN' https://167.99.126.228/api/v1/repos/Timmy_Foundation/compounding-intelligence/issues?state=open"}}]}
{"role": "tool", "content": "curl: (60) SSL certificate problem: self signed certificate"}
{"role": "assistant", "content": "SSL certificate issue. The server is using a self-signed certificate. Let me try with -k flag to skip SSL verification.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -k -H 'Authorization: token $GITEA_TOKEN' https://167.99.126.228/api/v1/repos/Timmy_Foundation/compounding-intelligence/issues?state=open"}}]}
{"role": "tool", "content": "[]"}
{"role": "assistant", "content": "No open issues found. The API returned an empty array."}
{"role": "user", "content": "Check if there are any pull requests"}
{"role": "assistant", "content": "Let me check for pull requests.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -k -H 'Authorization: token $GITEA_TOKEN' https://167.99.126.228/api/v1/repos/Timmy_Foundation/compounding-intelligence/pulls?state=open"}}]}
{"role": "tool", "content": "[{"id": 20, "title": "feat: add harvester.py + session_reader.py — session knowledge extractor (closes #8)", "state": "open", "user": {"login": "Rockachopa"}, "created_at": "2026-04-14T17:28:20Z"}]"}
{"role": "assistant", "content": "Found 1 open pull request: PR #20 - "feat: add harvester.py + session_reader.py — session knowledge extractor (closes #8)" by Rockachopa."}

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{"role": "user", "content": "How do I deploy a new version of the application?"}
{"role": "assistant", "content": "Here's the deployment process:\n1. Run tests: pytest\n2. Build Docker image: docker build -t app:latest .\n3. Push to registry: docker push registry.example.com/app:latest\n4. Update production: ssh prod 'cd /app && docker-compose pull && docker-compose up -d'\n5. Verify: curl https://app.example.com/health"}
{"role": "user", "content": "What if tests fail?"}
{"role": "assistant", "content": "If tests fail:\n1. Check the error logs: pytest -v\n2. Fix the failing tests\n3. Re-run tests: pytest\n4. If still failing, check recent changes: git log --oneline -10\n5. Consider reverting: git revert HEAD"}
{"role": "user", "content": "What if the Docker build fails?"}
{"role": "assistant", "content": "If Docker build fails:\n1. Check Dockerfile syntax: docker build --no-cache -t app:latest .\n2. Check for missing dependencies: cat requirements.txt\n3. Check disk space: df -h\n4. Clean up Docker: docker system prune -f\n5. Try building with verbose output: docker build --progress=plain -t app:latest ."}
{"role": "user", "content": "What if the push to registry fails?"}
{"role": "assistant", "content": "If push to registry fails:\n1. Check registry credentials: docker login registry.example.com\n2. Check registry space: curl https://registry.example.com/v2/_catalog\n3. Check image size: docker images app:latest\n4. Try tagging with version: docker tag app:latest registry.example.com/app:v1.0.0\n5. Push with version tag: docker push registry.example.com/app:v1.0.0"}
{"role": "user", "content": "What if the production update fails?"}
{"role": "assistant", "content": "If production update fails:\n1. Check SSH connection: ssh prod 'echo connected'\n2. Check disk space on prod: ssh prod 'df -h'\n3. Check Docker status: ssh prod 'docker ps -a'\n4. Check logs: ssh prod 'docker-compose logs --tail=100'\n5. Rollback: ssh prod 'cd /app && git checkout HEAD~1 && docker-compose up -d'"}

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@@ -0,0 +1,12 @@
{"role": "user", "content": "I need to implement a new feature for the application"}
{"role": "assistant", "content": "I can help you implement a new feature. What feature would you like to add?"}
{"role": "user", "content": "I want to add user authentication"}
{"role": "assistant", "content": "User authentication is a good feature. I have a few questions:\n1. What authentication method do you prefer? (JWT, OAuth, Session-based)\n2. Do you need social login? (Google, GitHub, etc.)\n3. What user data do you need to store?\n4. Do you need role-based access control?\n5. What's your database? (PostgreSQL, MySQL, SQLite)"}
{"role": "user", "content": "I prefer JWT, no social login, just email and password, no roles, PostgreSQL"}
{"role": "assistant", "content": "Great choices. Here's the implementation plan:\n1. Create user model with email and password hash\n2. Create JWT token generation and validation\n3. Create login endpoint: POST /api/auth/login\n4. Create register endpoint: POST /api/auth/register\n5. Create middleware for protected routes\n6. Add password hashing with bcrypt\n7. Add rate limiting for login attempts\n8. Add password reset functionality\n9. Add email verification\n10. Test all endpoints"}
{"role": "user", "content": "What about password reset?"}
{"role": "assistant", "content": "For password reset:\n1. Create reset token table\n2. Create forgot password endpoint: POST /api/auth/forgot-password\n3. Send reset email with token\n4. Create reset password endpoint: POST /api/auth/reset-password\n5. Validate reset token and expiration\n6. Update password and delete token\n7. Send confirmation email"}
{"role": "user", "content": "What about email verification?"}
{"role": "assistant", "content": "For email verification:\n1. Add email_verified field to user model\n2. Create verification token table\n3. Send verification email on registration\n4. Create verify email endpoint: GET /api/auth/verify-email?token=...\n5. Validate token and update email_verified\n6. Add middleware to check email_verified for certain routes\n7. Add resend verification email endpoint"}
{"role": "user", "content": "What about rate limiting?"}
{"role": "assistant", "content": "For rate limiting:\n1. Use Redis for rate limit storage\n2. Create rate limit middleware\n3. Set limits: 5 login attempts per minute, 3 password resets per hour\n4. Return 429 Too Many Requests when limit exceeded\n5. Add X-RateLimit headers to responses\n6. Log rate limit violations\n7. Add whitelist for trusted IPs"}

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@@ -0,0 +1,13 @@
{"role": "user", "content": "Clone the repo at forge.alexanderwhitestone.com/Timmy_Foundation/compounding-intelligence"}
{"role": "assistant", "content": "I'll clone the repository for you.", "tool_calls": [{"function": "terminal", "arguments": {"command": "git clone https://forge.alexanderwhitestone.com/Timmy_Foundation/compounding-intelligence.git"}}]}
{"role": "tool", "content": "Cloning into 'compounding-intelligence'...\nremote: Enumerating objects: 15, done.\nremote: Counting objects: 100% (15/15), done.\nremote: Compressing objects: 100% (12/12), done.\nremote: Total 15 (delta 2), reused 0 (delta 0), pack-reused 0\nReceiving objects: 100% (15/15), done.\nResolving deltas: 100% (2/2), done."}
{"role": "assistant", "content": "Repository cloned successfully. Let me check the structure.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ls -la compounding-intelligence/"}}]}
{"role": "tool", "content": "total 8\ndrwxr-xr-x 5 user staff 160 Apr 14 17:21 .\ndrwxr-xr-x 12 user staff 384 Apr 14 17:21 ..\ndrwxr-xr-x 13 user staff 416 Apr 14 17:21 .git\n-rw-r--r-- 1 user staff 1234 Apr 14 17:21 README.md\ndrwxr-xr-x 3 user staff 96 Apr 14 17:21 knowledge\n-rw-r--r-- 1 user staff 456 Apr 14 17:21 metrics\ndrwxr-xr-x 4 user staff 128 Apr 14 17:21 scripts\ndrwxr-xr-x 3 user staff 96 Apr 14 17:21 templates"}
{"role": "user", "content": "What files are in the templates directory?"}
{"role": "assistant", "content": "Let me check the templates directory.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ls -la compounding-intelligence/templates/"}}]}
{"role": "tool", "content": "total 0\ndrwxr-xr-x 3 user staff 96 Apr 14 17:21 .\ndrwxr-xr-x 5 user staff 160 Apr 14 17:21 ..\n-rw-r--r-- 1 user staff 0 Apr 14 17:21 .gitkeep"}
{"role": "assistant", "content": "The templates directory currently only contains a .gitkeep file. No templates have been added yet."}
{"role": "user", "content": "Create a harvest-prompt.md template in the templates directory."}
{"role": "assistant", "content": "I'll create the harvest-prompt.md template for you.", "tool_calls": [{"function": "terminal", "arguments": {"command": "cat > compounding-intelligence/templates/harvest-prompt.md << 'EOF'\n# Knowledge Extraction Prompt\n\nExtract knowledge from session transcripts.\nEOF"}}]}
{"role": "tool", "content": ""}
{"role": "assistant", "content": "Template created successfully. The harvest-prompt.md file is now in the templates directory."}