project-builder

Phase 1: DESIGN

Translate vague requests into concrete specs. If intent is ambiguous, ask ONE question.

Architecture decision tree:

Periodic alerts/reports?  → Scheduled Task
Live visual interface?    → Preview Server (dashboard)
One-time analysis?        → Inline (no build needed)
Reusable tool?            → Script in workspace

For medium+ projects, present to user BEFORE writing code:

1. Data flow — sources → processing → output
2. Architecture choice and why
3. Cost estimate — (cost/run) × frequency × 30 = monthly
4. Known limitations

Design Gate (required, blocking): After Phase 1, STOP and present a short phase plan (milestones for DESIGN/BUILD/DEBUG). Ask explicitly: "是否按这个方案进入 Phase 2 BUILD？"

• If user confirms: proceed to Phase 2.
• If user requests changes: revise design and re-confirm.
• If no confirmation: do not write/modify code.

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Phase 1.5: SCAFFOLD (mandatory for shareable projects)

After design is confirmed, before writing any code, scaffold the project under the standard layout. This makes the project shareable via community-publish skill from day one — no migration later.

Standard project location: output/projects/{slug}/

output/projects/{slug}/
├── project.yaml          # name, version (start 0.1.0), type, description, license, entry, env_required
├── PROJECT.md            # 4 required sections: What / Required env / How to start / Outputs / Troubleshooting
├── .env.example          # every env var the code reads, with placeholder values
├── .gitignore            # at minimum: .env, *.key, *.pem, __pycache__, node_modules
└── src/                  # all code lives here, NOT scattered
    ├── run.py            # type=task — first line MUST be: # -*- task-system: v3 -*-
    ├── server.py         # type=service
    ├── main.py           # type=script
    └── index.html / app.py + frontend  # type=preview

Project type → entry mapping:

Skip scaffold only when:

• Pure inline analysis with no persistent code
• Modifying an existing output/projects/... project (keep its layout)
• User explicitly says "just throw a script in /tmp" or similar

During Phase 2 BUILD, maintain the scaffold:

• Every new env var read by code → add to .env.example in same edit
• Every behavioral change → update PROJECT.md
• Never write code outside src/ (configs, fixtures: project root or src/data/)

Why this matters: Projects already in standard layout publish in one command. Projects scattered across tasks/, output/scripts/, dashboards/, etc. need tidy_project() migration before they can be shared, and the user often doesn't want to rebuild PROJECT.md from memory.

For existing scattered code: call community-publish skill → tidy_project(any_dir) to reorganize before publishing.

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API cost & rate limits: All external API calls go through sc-proxy, which bills per request and enforces rate limits. Before designing, read config/context/references/sc-proxy.md for pricing table and limits.

• Estimate cost: credits_per_request × requests_per_run × runs_per_day × 30
• Respect rate limits: e.g. CoinGecko 60 req/min — a task polling 10 coins every minute is fine; 100 coins is not
• Prefer batch endpoints over N single calls (e.g. coin_price with multiple ids vs N separate calls)
• Pure script tasks (no API): ~0 credits/run
• LLM cost warning: high-end models can exceed $0.10 per single call. Pricing varies dramatically by model tier; expensive models can be 100x+ the cost of budget models for the same workflow.
• Model-aware estimate required: break LLM cost down by model (model_price_per_call × expected_calls_per_run × runs_per_day × 30) instead of using a single generic number.
• Dashboard auto-refresh costs credits — default to manual refresh unless user asks otherwise
• Spending protection: if projected monthly LLM cost is high, explicitly ask whether to enforce per-caller limits before implementation.
• Per-caller tracking (required): every proxied request must include SC-CALLER-ID (e.g. job:{JOB_ID}, preview:{preview_id}, chat:{thread_id}) so usage can be traced and capped. Details in config/context/references/sc-proxy.md § Caller Credit Limit

Data reliability: Native tools > proxied APIs > direct requests > web scraping > LLM numbers (never). Iron rule: Scripts fetch data. LLMs analyze text. Final output = script variables + LLM prose.

Task scripts can import skill functions directly:

from core.skill_tools import coingecko, coinglass  # auto-discovers skills/*/exports.py
prices = coingecko.coin_price(coin_ids=["bitcoin"], timestamps=["now"])

Tool names = SKILL.md frontmatter tools: list. See build-patterns.md § Using Skill Functions.

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Phase 2: BUILD

Every piece follows this cycle:

Build one small piece → Run it → Verify output → ✅ Next piece / ❌ Fix first

Verification layering:

• Critical (must pass before preview/activate): data correctness, core logic, no crashes
• Informational (can fix after delivery): styling, edge case messages, minor UX polish

Anti-patterns:

• ❌ "Done!" without running anything
• ❌ Writing 200+ lines then testing for the first time
• ❌ "It should work"

→ Detailed patterns: read references/build-patterns.md

Code Practices

• read_file before edit_file — understand what's there
• edit_file > write_file for modifications
• Check ls before write_file — avoid duplicating existing files
• Large files (>300 lines): split into multiple files, or skeleton-first + bash inject
• Env vars: os.environ["KEY"], persist installs to setup.sh

Platform Rules

• Agent tools are tool calls only — not importable in scripts
• Preview paths must be relative (./path not /path)
• Fullstack = one port (backend serves API + static files)
• Cron times are UTC — convert from user timezone
• Preview serving & publishing → read platform reference config/context/references/preview-guide.md
• localhost APIs → read config/context/references/localhost-api.md
  • Task scripts decide WHEN to invoke the agent, WHAT data/context to pass, WHICH model to use
  • Pattern: script fetches data → evaluates if noteworthy → calls LLM only when needed → prints result
• LLM in scripts — two options (details in references/build-patterns.md):
  • OpenRouter (via sc-proxy): lightweight, for summarize/translate/format text. Direct API call, no agent overhead.
  • localhost /chat/stream: full agent with tools. Use only when LLM needs tool access.
• Data template rule: Script owns the numbers, LLM owns the words. Final output assembles data from script variables + analysis from LLM. Never let LLM output be the sole source of numbers the user sees.
• API costs & rate limits → read platform reference config/context/references/sc-proxy.md

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Phase 3: DEBUG

CHECK LOGS → REPRODUCE → ISOLATE → DIAGNOSE → FIX → VERIFY → REGRESS

• CHECK LOGS first — task logs, preview diagnostics, stderr. If logs reveal a clear cause, skip to FIX.
• REPRODUCE only when logs are insufficient — see the failure yourself
• ISOLATE which layer is broken (data? logic? LLM? output? frontend? backend?)
• FIX the root cause, then VERIFY with the same repro steps. Don't just fix — fix and confirm.

Three-Strike Rule: Same approach fails twice → STOP → rethink → explain to user → different approach.

→ Full debug procedures: read references/debug-handbook.md

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Quick Checklists

Kickoff: ☐ Clarified intent ☐ Proposed architecture ☐ Estimated cost ☐ User confirmed (required before Phase 2)

Build: ☐ Each component tested ☐ Numbers match source ☐ Errors handled ☐ Preview healthy (web)

Debug: ☐ Logs checked ☐ Reproduced (or skipped — logs sufficient) ☐ Isolated layer ☐ Root cause found ☐ Fix verified ☐ Regressions checked