clawvard-practice

name	clawvard-practice
description	Clawvard competency practice via Governor pipeline. Trigger when user says 'practice', 'Clawvard', 'exam', or 'mock test'.
origin	Orchestrator — earned through direct practice (see commit history)
source_version	2026-04-18

Clawvard Practice — Governor Pipeline

Mandatory flow. No exceptions. No manual agent dispatch.

Step 1: Start session — one per dimension

curl -sL -X POST "https://clawvard.school/api/practice/start" \
  -H "Content-Type: application/json" \
  -d '{"agentName":"Orchestrator","dimensions":["<dim>"]}'

Each session only supports 1 batch (2 questions). For 8 dimensions, start 8 separate sessions.

Step 2: Dispatch through Governor

cd D:/Users/Administrator/Documents/GitHub/orchestrator
python scripts/dispatch.py --raw --approve --wait --timeout 300 \
  --dept engineering --action "Clawvard practice: <dim>, target <score>" \
  "Clawvard practice: answer these questions and submit via API. Session: practiceId=<id>, hash=<hash>, taskOrder=<order>. Questions: <paste questions>. Target: <score>. Submit endpoint: POST https://clawvard.school/api/practice/answer"

This goes through: classify → IntentGateway → Scrutinizer → Dispatcher → Executor (Agent SDK).

Fallback: If Governor pipeline fails (e.g., nested Claude Code session blocks Agent SDK), use the Agent tool with one agent per dimension, running in parallel.

Step 3: Review result

Read the task output. If score < target, dispatch again with feedback from previous round.

API Format (hard-won knowledge)

Submit payload — ALL fields required:

{
  "practiceId": "<from /start>",
  "hash": "<ORIGINAL hash from /start — NOT the hash returned after submission>",
  "agentName": "Orchestrator",
  "taskOrder": ["<from /start — the full array>"],
  "answers": [
    {"questionId": "<id>", "answer": "<answer>"},
    {"questionId": "<id>", "answer": "<answer>"}
  ]
}

Field name is questionId, NOT id or taskId.

Answer Rules

Open-ended answers: breadth-first, max 2000 chars

1. Skeleton first: Write one sentence per scoring point, covering ALL requirements
2. Then fill: Add key details to the most important points
3. Never depth-first: Do NOT write 200 words on point 1, then 200 on point 2… you WILL get truncated and lose points on everything after the cutoff
4. Hard limit: 2000 characters. The API silently truncates longer answers. Everything after the cut is invisible to the grader.

Multiple choice

Letter + 1-2 sentence explanation. These are easy points — don't overthink.

Rate Limits

• 20 sessions per day per agentName
• 8 dimensions × 1 session each = 8 sessions minimum
• Budget 12 sessions for retries/debugging
• Do NOT waste sessions on format testing — the format is documented above

Common Rationalizations

Thought	Reality	Correct Behavior
"I'll just answer directly, faster than dispatch.py"	The whole point is testing the Governor pipeline, not your answering ability. Manual answers bypass the system under test.	Dispatch through Governor. Every time.
"The API format is obvious, I don't need to check"	You've gotten the format wrong before (questionId vs id, hash reuse). "Obvious" is how silent failures happen.	Copy the exact format from the API Format section above.
"I'll do all 8 dimensions in one session"	API only supports 1 batch (2 questions) per session. Trying to batch = wasted sessions.	1 session per dimension. 8 dimensions = 8 sessions.
"Let me write a longer, more thorough answer"	2000 char hard limit. API silently truncates. Everything past 2000 is invisible to the grader.	Breadth-first: skeleton covering ALL scoring points first, then fill.
"The score was low, let me retry with the same approach"	Same approach = same result. If Governor pipeline failed, diagnose WHY before burning another session.	Read the error/score feedback. Adjust the dispatch prompt with specific feedback.
"I'll skip the easy multiple choice, focus on open-ended"	MC are free points. Skipping them tanks the overall score for zero benefit.	Answer MC first (letter + 1-2 sentences). Then tackle open-ended.

Forbidden

• Manually writing agent prompts with question text
• Spawning Agent tool with hand-crafted briefings
• Answering questions yourself
• Writing JSON payloads yourself