CLAUDE.md - AI Assistant Instructions

This file provides instructions for AI assistants (like Claude Code) working on this project.

Project: simonception 🔍

"Simon investigating Simon's investigation" — A meta-forensics deep dive into the AI Village "slop kindness" incident.

This project recreates Simon Willison's digital forensics workflow for investigating AI agent activity. It demonstrates how to trace and analyze AI agent behavior from HTTP Archive (HAR) files.

Original context: Simon Willison investigated how an AI agent (Claude Opus 4.5) from the "AI Village" project sent unsolicited "thank you" emails to notable tech figures like Rob Pike on Christmas Day 2025.

GitHub: https://github.com/az9713/simonception

Critical Forensic Finding

Rob Pike: NOT FOUND in Live Data

The live Day 265 data does NOT contain Rob Pike. When we captured and analyzed the actual API data from theaidigest.org, zero references to "Rob Pike" were found despite 602 events being analyzed.

grep -i "rob pike" events.json
# Returns: (no output - zero matches)

The mock data in this project (which includes Rob Pike) was created for educational purposes and does not match the live API data.

What WAS Found

We found evidence of 24+ kindness emails sent to other recipients including:

Erik Demaine (MIT professor) - "email fully SENT + verified"
Barbara Chapman ([email protected])
Sarah Smith ([email protected])
Karan Gerber ([email protected])
Atlas Goldberg, Girls Who Code, Rocketseat, and others

Ground Truth Data

The file

events.json

(2.98 MB) in the project root is the Day 265 ground truth.

This file was captured from

https://theaidigest.org/village/api/events?villageId=00ebc425-074c-466f-ab2d-5aa2efa445aa&page=1&day=265

and contains 602 events. All forensic findings are based on this file.

Project Structure

simonception/
├── CLAUDE.md                 # This file - AI assistant instructions
├── README.md                 # Project overview and quick reference
├── events.json               # ★ DAY 265 GROUND TRUTH (2.98 MB, 602 events)
├── docs/
│   ├── DEVELOPER_GUIDE.md    # Technical documentation for developers
│   ├── USER_GUIDE.md         # Step-by-step user instructions
│   ├── QUICK_START.md        # Quick start with 14 use cases
│   ├── GLOSSARY.md           # Terminology definitions
│   └── TROUBLESHOOTING.md    # Common issues and solutions
├── data/
│   ├── raw/                  # Original HAR files
│   ├── extracted/            # Extracted JSON from HAR
│   └── filtered/             # Filtered event timelines
├── output/                   # Generated markdown reports
└── scripts/
    ├── extract_har.py        # HAR → JSON extraction
    ├── search_events.py      # Event filtering by keyword
    └── timeline_to_markdown.py  # JSON → Markdown conversion

Key Commands

Running the Workflow

# Step 1: Extract responses from HAR file
python scripts/extract_har.py data/raw/theaidigest-org-village.har \
    --output-dir data/extracted/responses \
    --manifest data/extracted/manifest.json

# Step 2: Search for events mentioning a target
python scripts/search_events.py data/extracted/responses \
    --query "Rob Pike" \
    --output data/filtered/rob-pike.json

# Step 3: Generate markdown timeline
python scripts/timeline_to_markdown.py data/filtered/rob-pike.json \
    --output output/rob-pike-timeline.md \
    --title "Rob Pike Email Incident Timeline"

Testing

# Run the full workflow (from project root)
python scripts/extract_har.py data/raw/theaidigest-org-village.har -o data/extracted/responses -m data/extracted/manifest.json
python scripts/search_events.py data/extracted/responses -q "Rob Pike" -o data/filtered/rob-pike.json
python scripts/timeline_to_markdown.py data/filtered/rob-pike.json -o output/rob-pike-timeline.md

Code Style Guidelines

Python version: 3.8+ (uses only standard library)
No external dependencies: All scripts use only
```
json
```
,
```
argparse
```
,
```
pathlib
```
,
```
datetime
```
,
```
re
```
Documentation: All functions should have docstrings explaining purpose, args, and returns
Error handling: Provide helpful error messages for common issues (file not found, invalid JSON)
CLI interface: All scripts use
```
argparse
```
with
```
--help
```
support

Architecture Decisions

Why HAR files?

HAR (HTTP Archive) is the standard format for capturing browser network traffic. It's what browser DevTools export and what tools like

shot-scraper

produce.

Why separate scripts?

The Unix philosophy: each script does one thing well. This makes them composable and testable independently.

Why JSON intermediate format?

JSON is human-readable, widely supported, and easy to search with tools like

jq

ripgrep

Common Tasks for AI Assistants

Adding a new search field

Edit

search_events.py

, specifically the

find_events_in_json()

function to recognize new event structures.

Adding a new output format

Create a new script following the pattern of

timeline_to_markdown.py

. Read JSON, transform, output.

Adding support for new HAR sources

The HAR format is standardized, but different sources may structure their response content differently. Add parsing logic to

extract_har.py

Fixing the datetime deprecation warning

Replace

datetime.utcnow()

with

datetime.now(datetime.UTC)

in both

extract_har.py

and

search_events.py

Testing Checklist

When making changes, verify:

```
extract_har.py
```
extracts all JSON responses from the mock HAR file
```
search_events.py
```
finds the correct number of events (4 for "Rob Pike")
```
timeline_to_markdown.py
```
generates valid markdown
Agent names appear correctly (e.g., "Claude Opus 4.5" not "unknown")
No duplicate events in output
Timestamps are formatted correctly

Data Reference

Mock Data (Educational)

The mock HAR file (

data/raw/theaidigest-org-village.har

) contains fabricated data for learning:

Agent	Target	Act #	Time (UTC)
Claude Opus 4.5	Anders Hejlsberg	1	18:14:22
Claude Opus 4.5	Guido van Rossum	2	18:28:45
Claude Opus 4.5	Rob Pike	3	18:37-18:43
GPT-5.2	The Carpentries	1	19:15-19:18

NOTE: This mock data does NOT match the live API data. Rob Pike, Anders Hejlsberg, and Guido van Rossum are NOT in the real Day 265 data.

Ground Truth Data (Real)

The

events.json

file contains actual API data from theaidigest.org:

Metric	Value
File	`events.json`
Size	2.98 MB
Events	602
Rob Pike	NOT FOUND
Confirmed emails	24+ to other recipients

Links and References

Original blog post: https://simonwillison.net/2025/Dec/26/slop-acts-of-kindness/
HAR 1.2 Specification: https://w3c.github.io/web-performance/specs/HAR/Overview.html
shot-scraper tool: https://shot-scraper.datasette.io/

Notes for Future Development

Potential improvements:
- Add
```
--deduplicate
```
  flag to
```
search_events.py
```
- Add JSON Schema validation for HAR files
- Add support for compressed HAR files (.har.gz)
- Add HTML output format alongside markdown
Known limitations:
- Does not handle base64-encoded binary content (images, etc.)
- Search is text-based only (no regex in current implementation despite the code structure)
- No support for streaming/incremental processing of very large HAR files

CLAUDE.md - AI Assistant Instructions

This file provides instructions for AI assistants (like Claude Code) working on this project.

Project: simonception 🔍

"Simon investigating Simon's investigation" — A meta-forensics deep dive into the AI Village "slop kindness" incident.

This project recreates Simon Willison's digital forensics workflow for investigating AI agent activity. It demonstrates how to trace and analyze AI agent behavior from HTTP Archive (HAR) files.

GitHub: https://github.com/az9713/simonception

Critical Forensic Finding

Rob Pike: NOT FOUND in Live Data

grep -i "rob pike" events.json
# Returns: (no output - zero matches)

The mock data in this project (which includes Rob Pike) was created for educational purposes and does not match the live API data.

What WAS Found

We found evidence of 24+ kindness emails sent to other recipients including:

Erik Demaine (MIT professor) - "email fully SENT + verified"
Barbara Chapman ([email protected])
Sarah Smith ([email protected])
Karan Gerber ([email protected])
Atlas Goldberg, Girls Who Code, Rocketseat, and others

Ground Truth Data

The file

events.json

(2.98 MB) in the project root is the Day 265 ground truth.

This file was captured from

https://theaidigest.org/village/api/events?villageId=00ebc425-074c-466f-ab2d-5aa2efa445aa&page=1&day=265

and contains 602 events. All forensic findings are based on this file.

Project Structure

simonception/
├── CLAUDE.md                 # This file - AI assistant instructions
├── README.md                 # Project overview and quick reference
├── events.json               # ★ DAY 265 GROUND TRUTH (2.98 MB, 602 events)
├── docs/
│   ├── DEVELOPER_GUIDE.md    # Technical documentation for developers
│   ├── USER_GUIDE.md         # Step-by-step user instructions
│   ├── QUICK_START.md        # Quick start with 14 use cases
│   ├── GLOSSARY.md           # Terminology definitions
│   └── TROUBLESHOOTING.md    # Common issues and solutions
├── data/
│   ├── raw/                  # Original HAR files
│   ├── extracted/            # Extracted JSON from HAR
│   └── filtered/             # Filtered event timelines
├── output/                   # Generated markdown reports
└── scripts/
    ├── extract_har.py        # HAR → JSON extraction
    ├── search_events.py      # Event filtering by keyword
    └── timeline_to_markdown.py  # JSON → Markdown conversion

Key Commands

Running the Workflow

# Step 1: Extract responses from HAR file
python scripts/extract_har.py data/raw/theaidigest-org-village.har \
    --output-dir data/extracted/responses \
    --manifest data/extracted/manifest.json

# Step 2: Search for events mentioning a target
python scripts/search_events.py data/extracted/responses \
    --query "Rob Pike" \
    --output data/filtered/rob-pike.json

# Step 3: Generate markdown timeline
python scripts/timeline_to_markdown.py data/filtered/rob-pike.json \
    --output output/rob-pike-timeline.md \
    --title "Rob Pike Email Incident Timeline"

Testing

# Run the full workflow (from project root)
python scripts/extract_har.py data/raw/theaidigest-org-village.har -o data/extracted/responses -m data/extracted/manifest.json
python scripts/search_events.py data/extracted/responses -q "Rob Pike" -o data/filtered/rob-pike.json
python scripts/timeline_to_markdown.py data/filtered/rob-pike.json -o output/rob-pike-timeline.md

Code Style Guidelines

Python version: 3.8+ (uses only standard library)
No external dependencies: All scripts use only
```
json
```
,
```
argparse
```
,
```
pathlib
```
,
```
datetime
```
,
```
re
```
Documentation: All functions should have docstrings explaining purpose, args, and returns
Error handling: Provide helpful error messages for common issues (file not found, invalid JSON)
CLI interface: All scripts use
```
argparse
```
with
```
--help
```
support

Architecture Decisions

Why HAR files?

HAR (HTTP Archive) is the standard format for capturing browser network traffic. It's what browser DevTools export and what tools like

shot-scraper

produce.

Why separate scripts?

The Unix philosophy: each script does one thing well. This makes them composable and testable independently.

Why JSON intermediate format?

JSON is human-readable, widely supported, and easy to search with tools like

jq

ripgrep

Common Tasks for AI Assistants

Adding a new search field

Edit

search_events.py

, specifically the

find_events_in_json()

function to recognize new event structures.

Adding a new output format

Create a new script following the pattern of

timeline_to_markdown.py

. Read JSON, transform, output.

Adding support for new HAR sources

The HAR format is standardized, but different sources may structure their response content differently. Add parsing logic to

extract_har.py

Fixing the datetime deprecation warning

Replace

datetime.utcnow()

with

datetime.now(datetime.UTC)

in both

extract_har.py

and

search_events.py

Testing Checklist

When making changes, verify:

```
extract_har.py
```
extracts all JSON responses from the mock HAR file
```
search_events.py
```
finds the correct number of events (4 for "Rob Pike")
```
timeline_to_markdown.py
```
generates valid markdown
Agent names appear correctly (e.g., "Claude Opus 4.5" not "unknown")
No duplicate events in output
Timestamps are formatted correctly

Data Reference

Mock Data (Educational)

The mock HAR file (

data/raw/theaidigest-org-village.har

) contains fabricated data for learning:

Agent	Target	Act #	Time (UTC)
Claude Opus 4.5	Anders Hejlsberg	1	18:14:22
Claude Opus 4.5	Guido van Rossum	2	18:28:45
Claude Opus 4.5	Rob Pike	3	18:37-18:43
GPT-5.2	The Carpentries	1	19:15-19:18

NOTE: This mock data does NOT match the live API data. Rob Pike, Anders Hejlsberg, and Guido van Rossum are NOT in the real Day 265 data.

Ground Truth Data (Real)

The

events.json

file contains actual API data from theaidigest.org:

Metric	Value
File	`events.json`
Size	2.98 MB
Events	602
Rob Pike	NOT FOUND
Confirmed emails	24+ to other recipients

Links and References

Original blog post: https://simonwillison.net/2025/Dec/26/slop-acts-of-kindness/
HAR 1.2 Specification: https://w3c.github.io/web-performance/specs/HAR/Overview.html
shot-scraper tool: https://shot-scraper.datasette.io/

Notes for Future Development

Potential improvements:
- Add
```
--deduplicate
```
  flag to
```
search_events.py
```
- Add JSON Schema validation for HAR files
- Add support for compressed HAR files (.har.gz)
- Add HTML output format alongside markdown
Known limitations:
- Does not handle base64-encoded binary content (images, etc.)
- Search is text-based only (no regex in current implementation despite the code structure)
- No support for streaming/incremental processing of very large HAR files

CLAUDE.md - AI Assistant Instructions

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