darkamenosa/pi-setup/pi-share

pi-share / buildwithpi Session Loader

Load and parse session transcripts from pi-share URLs (shittycodingagent.ai, buildwithpi.ai, buildwithpi.com, pi.dev).

When to Use

Loading sessions: Use this skill when the user provides a URL like:

• https://shittycodingagent.ai/session/?<gist_id>
• https://buildwithpi.ai/session/?<gist_id>
• https://buildwithpi.com/session/?<gist_id>
• https://pi.dev/session/?<gist_id>
• https://pi.dev/session/#<gist_id>
• Or just a gist ID like 46aee35206aefe99257bc5d5e60c6121
• Or hash-prefixed shorthand like #46aee35206aefe99257bc5d5e60c6121

Human summaries: Use --human-summary when the user asks you to:

• Summarize what a human did in a pi/coding agent session
• Understand how a user interacted with an agent
• Analyze user behavior, steering patterns, or prompting style
• Get a human-centric view of a session (not what the agent did, but what the human did)

The human summary focuses on: initial goals, re-prompts, steering/corrections, interventions, and overall prompting style.

How It Works

1. Session exports are stored as GitHub Gists
2. The URL contains a gist ID after the ?
3. The gist contains a session.html file with base64-encoded session data
4. The helper script fetches and decodes this to extract the full conversation

Usage

# Get full session data (default)
node ~/.pi/agent/skills/pi-share/fetch-session.mjs "<url-or-gist-id>"
 
# Get just the header
node ~/.pi/agent/skills/pi-share/fetch-session.mjs <gist-id> --header
 
# Get entries as JSON lines (one entry per line)
node ~/.pi/agent/skills/pi-share/fetch-session.mjs <gist-id> --entries
 
# Get the system prompt
node ~/.pi/agent/skills/pi-share/fetch-session.mjs <gist-id> --system
 
# Get tool definitions
node ~/.pi/agent/skills/pi-share/fetch-session.mjs <gist-id> --tools
 
# Get human-centric summary (what did the human do in this session?)
node ~/.pi/agent/skills/pi-share/fetch-session.mjs <gist-id> --human-summary

Human Summary

The --human-summary flag generates a ~300 word summary focused on the human's experience:

• What was their initial goal?
• How often did they re-prompt or steer the agent?
• What kind of interventions did they make? (corrections, clarifications, frustration)
• How specific or vague were their instructions?

This uses claude-haiku-4-5 via pi -p to analyze the condensed session transcript.

Session Data Structure

The decoded session contains:

interface SessionData {
  header: {
    type: "session";
    version: number;
    id: string;           // Session UUID
    timestamp: string;    // ISO timestamp
    cwd: string;          // Working directory
  };
  entries: SessionEntry[];  // Conversation entries (JSON lines format)
  leafId: string | null;    // Current branch leaf
  systemPrompt?: string;    // System prompt text
  tools?: { name: string; description: string }[];
}

Entry types include:

• message - User/assistant/toolResult messages with content blocks
• model_change - Model switches
• thinking_level_change - Thinking mode changes
• compaction - Context compaction events

Message content block types:

• text - Text content
• toolCall - Tool invocation with toolName and args
• thinking - Model thinking content
• image - Embedded images

Example: Analyze a Session

# Pipe entries through jq to filter
node ~/.pi/agent/skills/pi-share/fetch-session.mjs "<url>" --entries | jq 'select(.type == "message" and .message.role == "user")'
 
# Count tool calls
node ~/.pi/agent/skills/pi-share/fetch-session.mjs "<url>" --entries | jq -s '[.[] | select(.type == "message") | .message.content[]? | select(.type == "toolCall")] | length'