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 ```bash # 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: ```typescript 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 ```bash # 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' ```