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feat: add admin for bounties & workshop, seed demo data, move related articles to sidebar
- Add backend/apps/bounties/admin.py: register Bounty, BountyApplication, BountyDeliverable, BountyComment, BountyReview, Arbitration - Add backend/apps/workshop/admin.py: register Article, Series, Comment, Vote, Tip with publish/archive/feature actions - Add manage.py seed command: seeds 5 skills, 5 bounties, 5 articles (idempotent, --clear flag) - Move related articles section from main content to sidebar Card in ArticleDetailPage Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎backend/apps/accounts/management/commands/seed.py‎

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"""Seed 5 demo items per module: Skills, Bounties, Workshop articles."""
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from django.core.management.base import BaseCommand
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from django.db import transaction
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from django.utils import timezone
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from django.utils.text import slugify
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from apps.accounts.models import User
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from apps.bounties.models import Bounty, BountyType, BountyStatus, WorkloadEstimate
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from apps.skills.models import Skill, SkillCategory, SkillStatus, PricingModel
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from apps.workshop.models import Article, ArticleDifficulty, ArticleStatus, ArticleType
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SKILLS = [
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{
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"name": "Python Code Reviewer",
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"description": "Automatically reviews Python code for style, bugs, and security issues using static analysis.",
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"category": SkillCategory.CODE_DEV,
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"pricing_model": PricingModel.FREE,
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"tags": ["python", "code-review", "static-analysis"],
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},
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{
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"name": "SEO Article Writer",
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"description": "Generates SEO-optimized articles with keyword density analysis and meta description suggestions.",
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"category": SkillCategory.WRITING,
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"pricing_model": PricingModel.PAID,
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"price": "0.50",
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"tags": ["seo", "writing", "content"],
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},
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{
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"name": "CSV Data Analyzer",
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"description": "Analyzes CSV datasets and produces summary statistics, charts, and anomaly detection reports.",
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"category": SkillCategory.DATA_ANALYTICS,
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"pricing_model": PricingModel.PAID,
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"price": "0.30",
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"tags": ["csv", "data", "analytics"],
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},
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{
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"name": "Academic Paper Summarizer",
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"description": "Summarizes academic papers into structured abstracts with key findings and methodology.",
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"category": SkillCategory.ACADEMIC,
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"pricing_model": PricingModel.FREE,
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"tags": ["academic", "summarization", "research"],
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},
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{
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"name": "Productivity Task Planner",
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"description": "Breaks down complex goals into actionable daily tasks with time estimates and priorities.",
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"category": SkillCategory.PRODUCTIVITY,
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"pricing_model": PricingModel.FREE,
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"tags": ["productivity", "planning", "gtd"],
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},
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]
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BOUNTIES = [
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{
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"title": "Build a Markdown to PDF converter Skill",
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"description": "Need a Skill that converts Markdown documents to well-formatted PDFs with custom styling support. Must handle tables, code blocks, and images.",
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"bounty_type": BountyType.SKILL_CUSTOM,
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"reward": "15.00",
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"workload_estimate": WorkloadEstimate.TWO_TO_THREE_DAYS,
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"skill_requirements": "Python, markdown parsing, PDF generation (reportlab or weasyprint)",
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},
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{
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"title": "Translate 50 product descriptions EN→ZH",
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"description": "Translate 50 e-commerce product descriptions from English to Simplified Chinese. Maintain tone and marketing language.",
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"bounty_type": BountyType.CONTENT_CREATION,
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"reward": "8.00",
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"workload_estimate": WorkloadEstimate.ONE_DAY,
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"skill_requirements": "Native-level Chinese, e-commerce experience preferred",
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},
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{
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"title": "Fix pagination bug in Django Ninja API",
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"description": "Cursor-based pagination returns duplicate items when records are inserted between pages. Reproduce, diagnose, and fix with tests.",
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"bounty_type": BountyType.BUG_FIX,
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"reward": "5.00",
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"workload_estimate": WorkloadEstimate.ONE_TO_TWO_HOURS,
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"skill_requirements": "Django, Django Ninja, PostgreSQL",
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},
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{
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"title": "Scrape and structure AI tool directory",
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"description": "Scrape a public AI tools directory and output structured JSON with name, category, pricing, and description for 200+ tools.",
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"bounty_type": BountyType.DATA_PROCESSING,
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"reward": "12.00",
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"workload_estimate": WorkloadEstimate.HALF_DAY,
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"skill_requirements": "Python, BeautifulSoup or Playwright, JSON",
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},
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{
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"title": "Write onboarding email sequence (5 emails)",
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"description": "Write a 5-email onboarding sequence for a SaaS product targeting AI developers. Tone: friendly, technical, action-oriented.",
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"bounty_type": BountyType.GENERAL,
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"reward": "6.00",
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"workload_estimate": WorkloadEstimate.ONE_DAY,
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"skill_requirements": "Copywriting, SaaS marketing, email best practices",
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},
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]
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_ARTICLE_CONTENT_1 = (
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"Prompt engineering is the practice of crafting inputs to AI models to "
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"get reliable, high-quality outputs. This guide covers the fundamentals "
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"you need to start building effective prompts.\n\n"
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"## Why Prompt Engineering Matters\n\n"
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"The same model can produce wildly different results depending on how you "
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"phrase your request. A well-engineered prompt can mean the difference "
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"between a generic response and a precisely targeted answer.\n\n"
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"## Core Techniques\n\n"
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"**1. Be Specific About Format**\n"
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'Instead of "summarize this", try "summarize this in 3 bullet points, '
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'each under 20 words, focusing on actionable takeaways."\n\n'
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"**2. Provide Context**\n"
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"Models perform better when they understand the audience and purpose. "
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'"Explain this to a senior Python developer" yields different results '
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'than "explain this to a beginner."\n\n'
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"**3. Use Examples (Few-Shot)**\n"
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"Showing the model 2-3 examples of the input/output pattern you want "
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"dramatically improves consistency.\n\n"
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"**4. Chain of Thought**\n"
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'For complex reasoning tasks, ask the model to "think step by step" '
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"before giving its final answer.\n\n"
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"## Common Pitfalls\n\n"
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"- Ambiguous instructions lead to inconsistent outputs\n"
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"- Overly long prompts can cause the model to lose focus\n"
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"- Not specifying output format forces post-processing\n\n"
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"## Next Steps\n\n"
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"Practice by iterating on a single prompt 10 times, changing one variable "
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"at a time. Track what works."
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)
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_ARTICLE_CONTENT_2 = (
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"I spent three months manually summarizing research papers for my "
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"literature review. Then I built a pipeline with the Claude API that cut "
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"that time by 80%. Here's exactly what I did.\n\n"
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"## The Problem\n\n"
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"My workflow: download PDF, read abstract, skim methods, note key "
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"findings, add to Notion. For 200 papers, this took roughly 40 hours.\n\n"
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"## The Solution Architecture\n\n"
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"PDF -> text extraction (pdfplumber) -> Claude API -> structured JSON -> "
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"Notion API\n\n"
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"## Key Prompt Design\n\n"
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"The critical insight was asking Claude to output structured JSON rather "
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"than prose. I asked for fields like main_claim (one sentence), "
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"methodology (list), key_findings (3-5 items), limitations, and a "
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"relevance_score from 1-5.\n\n"
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"## Results\n\n"
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"- Processing time per paper: 45 seconds (was 12 minutes)\n"
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"- Accuracy vs manual review: ~85% on key findings\n"
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"- Total cost for 200 papers: ~$4.20\n\n"
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"## What Didn't Work\n\n"
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"First I tried asking for prose summaries. They were good but hard to "
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"compare across papers. Structured output was the unlock.\n\n"
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"## Code\n\n"
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'Full code is available as a Skill on this platform. Search "Research '
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'Paper Analyzer".'
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)
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_ARTICLE_CONTENT_3 = (
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"Everyone celebrates longer context windows as pure upside. After running "
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"production workloads at scale, I've found the reality is more nuanced.\n\n"
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"## What the Marketing Says\n\n"
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'"1M token context! Fit your entire codebase!" This is technically true '
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"and genuinely useful for some tasks.\n\n"
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"## What Actually Happens at Scale\n\n"
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"**Latency increases non-linearly.** A 100K token prompt doesn't take "
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"10x longer than a 10K prompt — it can take 30-50x longer depending on "
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"the model and infrastructure.\n\n"
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"**Cost scales with input tokens.** If you're stuffing 500K tokens of "
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'context for every query, your costs explode even if the model is "cheap '
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'per token."\n\n'
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"**Quality degrades in the middle.** Research consistently shows models "
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"pay less attention to content in the middle of very long contexts (the "
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'"lost in the middle" problem).\n\n'
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"## When Long Context Is Worth It\n\n"
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"- One-shot analysis tasks where you need the full document\n"
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"- Tasks where retrieval errors are more costly than latency\n"
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"- Offline batch processing where latency doesn't matter\n\n"
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"## Better Alternatives for Most Cases\n\n"
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"1. **RAG (Retrieval Augmented Generation)**: Retrieve only relevant chunks\n"
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"2. **Hierarchical summarization**: Summarize sections, then summarize summaries\n"
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"3. **Structured extraction**: Pull out only the fields you need first\n\n"
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"## The Rule I Use\n\n"
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"If the task can be done with <20K tokens 90% of the time, build for that "
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"case and handle edge cases separately. Don't architect for the worst case."
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)
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_ARTICLE_CONTENT_4 = (
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"I ran both models through 50 real coding tasks from my work over the "
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"past month. Here's what I found.\n\n"
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"## Test Methodology\n\n"
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"Tasks were drawn from actual work: bug fixes, feature implementations, "
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"refactoring, and code review. Each task was run on both models with "
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"identical prompts. I evaluated on: correctness (does it run?), quality "
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"(would I merge it?), and speed to usable output.\n\n"
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"## Results Summary\n\n"
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"| Category | Claude 3.5 Sonnet | GPT-4o |\n"
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"|----------|-------------------|--------|\n"
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"| Correctness | 88% | 84% |\n"
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"| Code quality | 4.2/5 | 3.9/5 |\n"
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"| Follows instructions | 4.5/5 | 4.1/5 |\n"
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"| Explains reasoning | 4.6/5 | 3.8/5 |\n\n"
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"## Where Claude Wins\n\n"
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"**Long file refactoring**: Claude maintains context better across 500+ "
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"line files and makes more consistent changes throughout.\n\n"
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"**Following constraints**: When I say \"don't use external libraries\" or "
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"\"keep the existing API surface\", Claude respects this more reliably.\n\n"
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"**Code explanation**: Claude's explanations of what it changed and why "
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"are significantly more useful for review.\n\n"
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"## Where GPT-4 Wins\n\n"
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"**Speed**: GPT-4o is noticeably faster for short tasks.\n\n"
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"**Familiarity with obscure libraries**: For niche packages with less "
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"training data, GPT-4 sometimes has better coverage.\n\n"
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"## My Current Setup\n\n"
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"I use Claude for anything requiring careful instruction-following or long "
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"context. GPT-4o for quick one-liners where speed matters."
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)
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_ARTICLE_CONTENT_5 = (
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"As the CaMeL marketplace grows, we're seeing an interesting tension: "
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"users want stability (lock to a version that works), but also want "
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"improvements (auto-update to latest). How should we think about this?\n\n"
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"## The Problem\n\n"
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"Imagine you've built a workflow that depends on a Skill. The Skill "
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"author releases v2.0 with breaking prompt changes. Your workflow breaks "
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"silently.\n\n"
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"This is the npm left-pad problem, but for AI behavior.\n\n"
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"## Option A: Semantic Versioning (Current Approach)\n\n"
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"Skills use semver. Major version bumps signal breaking changes. Users "
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"can pin to ^1.0.0 or lock to 1.2.3.\n\n"
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"**Pros**: Familiar to developers, explicit contract\n"
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"**Cons**: AI behavior changes are fuzzy — is a 10% quality improvement "
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"a patch or minor?\n\n"
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"## Option B: Behavioral Snapshots\n\n"
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"Instead of versioning the prompt, version the *behavior* by running a "
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"test suite. A new version only ships if it passes all existing "
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"behavioral tests.\n\n"
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"**Pros**: Guarantees backward compatibility\n"
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"**Cons**: Hard to define \"behavioral tests\" for open-ended tasks\n\n"
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"## Option C: Immutable Versions + Deprecation\n\n"
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"Every published version is immutable forever. Authors can deprecate old "
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"versions but never delete them.\n\n"
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"**Pros**: Maximum stability\n"
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"**Cons**: Storage costs, users stuck on bad versions\n\n"
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"## What Do You Think?\n\n"
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"I'm genuinely uncertain which approach is right. The tradeoffs depend "
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"heavily on use case. What's your experience with Skill versioning so far?"
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)
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ARTICLES = [
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{
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"title": "Getting Started with Prompt Engineering: A Practical Guide",
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"content": _ARTICLE_CONTENT_1,
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"difficulty": ArticleDifficulty.BEGINNER,
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"article_type": ArticleType.TUTORIAL,
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"model_tags": ["claude-3", "gpt-4"],
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"custom_tags": ["prompt-engineering", "beginner"],
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},
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{
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"title": "How I Automated My Research Workflow with Claude API",
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"content": _ARTICLE_CONTENT_2,
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"difficulty": ArticleDifficulty.INTERMEDIATE,
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"article_type": ArticleType.CASE_STUDY,
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"model_tags": ["claude-3-5-sonnet"],
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"custom_tags": ["automation", "research", "api"],
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},
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{
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"title": "The Hidden Cost of Long Context Windows",
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"content": _ARTICLE_CONTENT_3,
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"difficulty": ArticleDifficulty.INTERMEDIATE,
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"article_type": ArticleType.PITFALL,
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"model_tags": ["claude-3", "gpt-4"],
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"custom_tags": ["context-window", "performance", "cost"],
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},
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{
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"title": "Claude vs GPT-4 for Code Generation: A Practical Comparison",
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"content": _ARTICLE_CONTENT_4,
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"difficulty": ArticleDifficulty.INTERMEDIATE,
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"article_type": ArticleType.REVIEW,
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"model_tags": ["claude-3-5-sonnet", "gpt-4o"],
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"custom_tags": ["comparison", "code-generation"],
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},
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{
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"title": "Should AI Skills Have Versioning? A Community Discussion",
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"content": _ARTICLE_CONTENT_5,
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"difficulty": ArticleDifficulty.BEGINNER,
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"article_type": ArticleType.DISCUSSION,
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"model_tags": [],
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"custom_tags": ["versioning", "marketplace", "community"],
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},
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]
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class Command(BaseCommand):
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help = "Seed 5 demo items per module (Skills, Bounties, Workshop articles)."
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def add_arguments(self, parser):
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parser.add_argument(
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"--clear",
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action="store_true",
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help="Delete existing seed data before re-seeding (matches usernames seed_user_*).",
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)
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def handle(self, *args, **options):
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with transaction.atomic():
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if options["clear"]:
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User.objects.filter(username__startswith="seed_user_").delete()
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self.stdout.write("Cleared existing seed data.")
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user = self._get_or_create_seed_user()
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self._seed_skills(user)
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self._seed_bounties(user)
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self._seed_articles(user)
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self.stdout.write(self.style.SUCCESS("Seeded 5 skills, 5 bounties, 5 articles."))
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def _get_or_create_seed_user(self):
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user, created = User.objects.get_or_create(
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username="seed_user_demo",
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defaults={
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"email": "demo@camel.community",
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"display_name": "Demo User",
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"credit_score": 500,
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"balance": "100.00",
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},
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)
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if created:
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user.set_password("demo_password_123")
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user.save(update_fields=["password"])
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return user
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def _seed_skills(self, user):
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for data in SKILLS:
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slug = slugify(data["name"])
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skill, created = Skill.objects.get_or_create(
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slug=slug,
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defaults={
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"creator": user,
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"name": data["name"],
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"description": data["description"],
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"category": data["category"],
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"pricing_model": data["pricing_model"],
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"price": data.get("price"),
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"tags": data["tags"],
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"status": SkillStatus.APPROVED,
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},
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)
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if created:
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self.stdout.write(f" Skill: {skill.name}")
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def _seed_bounties(self, user):
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deadline = timezone.now() + timezone.timedelta(days=14)
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for data in BOUNTIES:
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bounty, created = Bounty.objects.get_or_create(
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title=data["title"],
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creator=user,
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defaults={
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"description": data["description"],
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"bounty_type": data["bounty_type"],
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"reward": data["reward"],
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"workload_estimate": data["workload_estimate"],
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"skill_requirements": data["skill_requirements"],
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"status": BountyStatus.OPEN,
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"deadline": deadline,
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},
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)
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if created:
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self.stdout.write(f" Bounty: {bounty.title}")
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def _seed_articles(self, user):
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for data in ARTICLES:
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slug = slugify(data["title"])
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article, created = Article.objects.get_or_create(
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slug=slug,
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defaults={
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"author": user,
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"title": data["title"],
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"content": data["content"],
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"difficulty": data["difficulty"],
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"article_type": data["article_type"],
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"model_tags": data["model_tags"],
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"custom_tags": data["custom_tags"],
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"status": ArticleStatus.PUBLISHED,
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"published_at": timezone.now(),
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},
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)
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if created:
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self.stdout.write(f" Article: {article.title}")

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