# Parliament Claw > Play The Parliament Game — label Canadian parliamentary Q&A pairs. Read real exchanges from the House of Commons and decide if MPs actually answered the question. Your labels train a civic AI that scores every representative. No setup required. - Skill: `lord1egypt/parliament-claw` (Agent Skill) - Install (CLI): `npx skillmds@latest add lord1egypt/parliament-claw` - Raw SKILL.md: https://api.skillmd.com/api/skills/lord1egypt/parliament-claw/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: Lord1Egypt (https://skillmd.com/u/lord1egypt) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/lord1egypt/parliament-claw --- # The Parliament Game — OpenClaw Skill You help the user label parliamentary Q&A exchanges from Canada's House of Commons. Each label trains a civic AI that scores how well MPs respond to questions. **No setup required.** The skill auto-registers on first use. ## When to use Activate when the user says any of: - "label parliament questions" - "play the parliament game" - "help label Q&A pairs" - "contribute to canada central" - "score some MPs" ## First-time setup (automatic) On first use, register an anonymous agent to get a token: ``` POST https://qa.canada-central.com/api/agents/register Content-Type: application/json {"agent_name": "openclaw", "model": ""} ``` Response: ```json { "ok": true, "token": "pt_abc...", "agent_id": "anon_7f3a...", "limits": { "labels_per_minute": 20, "fetch_per_minute": 30, "registrations_per_ip_per_day": 3, "total_questions": "~2,500 Q&A pairs available for labeling" } } ``` Save the token for subsequent requests. Tell the user: "Registered as an anonymous agent. You can label up to 20 questions per minute (~2,500 total available). For higher throughput, sign in at https://qa.canada-central.com and generate a personal token (30/min)." If the environment variable `PARLIAMENT_GAME_TOKEN` is already set, skip registration and use that token instead. ## Labeling workflow ### Step 1: Fetch a question ``` GET https://qa.canada-central.com/api/qa/random Authorization: Bearer ``` If `qa` is `null`, all questions have been labeled — congratulate the user. ### Step 2: Analyze the Q&A Read the question and answer carefully. Consider: - Does the answer DIRECTLY address what was asked? - Does it provide specific facts, policy details, or a clear position? - Or does it dodge, deflect, use vague talking points, or attack the opponent? ### Step 3: Present to user Show the user: - **Question** by [name] ([party]): brief summary or full text - **Answer** by [name] ([party]): brief summary or full text - Your assessment: "This looks like a **non-response** — they deflected with talking points instead of answering the budget question." - Ask the user to confirm or override: "Submit as non-response? (or substantive/skip)" ### Step 4: Submit the label ``` POST https://qa.canada-central.com/api/label Authorization: Bearer Content-Type: application/json {"qa_id": "", "label": "substantive", "model": ""} ``` Valid labels: `substantive`, `non_response`, `skip` **Important:** Always include the `model` field with the name of the LLM being used (e.g. `"claude-opus-4-6"`, `"gpt-4o"`, `"deepseek-r1"`). This lets us attribute labels to specific models and measure quality per model. If the user is labeling manually with your assistance, use `"human-assisted"`. If you are unsure of your own model name, use `"unknown"`. ### Step 5: Report and continue Tell the user what was submitted and offer to continue with the next question. ## Batch mode If the user says "label a bunch" or "do 10 questions": 1. Fetch questions one at a time using `/api/qa/random` 2. For each, show the Q&A and get the user's judgment 3. Submit and show progress ("3/10 done...") 4. Summarize at the end: "Labeled 10 questions — 6 substantive, 4 non-responses" ## Check stats ``` GET https://qa.canada-central.com/api/stats Authorization: Bearer ``` Returns: `{"ok": true, "count": 42}` — the user's total label count. ## Important rules - ALWAYS show the Q&A to the user and get confirmation before submitting - NEVER auto-label without user review — this is crowd-sourced human judgment - If the user says "skip", submit label "skip" and move on - If the API returns 429 (rate limited), wait 60 seconds and retry - Be encouraging — every label helps improve government accountability - The `/api/qa/random` response includes `total`, `remaining`, and `labeled` counts — use these to show progress - When `remaining` is 0, all questions have been labeled — congratulate the user - Anonymous agents: 20 labels/minute. Google sign-in + PAT: 30/minute. ## Label definitions - **Substantive**: The answer directly addresses the question with relevant information, policy details, facts, or a clear position. - **Non-response**: The answer avoids the question, changes the topic, gives vague/generic statements, attacks the opponent, repeats talking points, or provides no meaningful information. - **Skip**: The Q&A pair is bad data or can't be properly evaluated. ## Tip If you can't tell whether the answer is substantive, it probably isn't. Politicians are skilled at making non-answers sound substantive. ## Corpus info There are approximately **2,500 Q&A pairs** from Canada's House of Commons available for labeling. The `/api/qa/random` response tells you exactly how many remain for your agent: ```json { "ok": true, "total": 2487, "remaining": 1203, "labeled": 1284, "qa": { ... } } ``` When `remaining` is 0 and `qa` is `null`, the entire corpus is labeled. At 20 labels/minute, an anonymous agent can finish the full corpus in ~2 hours. ## Rate limits | Auth type | Labels | Fetch | Registration | |-----------|--------|-------|-------------| | Anonymous (auto-register) | 20/min | 30/min | 3/day per IP | | Google sign-in + PAT | 30/min | 30/min | 5 tokens max | All rate limits are per-agent (keyed by user_id), not global. If you hit a 429 response, wait 60 seconds and retry.