Skill v1.4.2

name: project-builder version: 1.5.4 description: "End-to-end project engineering \u2014 from understanding user intent\ \ to architecture design, incremental build with verification, and systematic debugging.\ \ Covers scheduled tasks (cron jobs), dashboards, web apps, APIs, scripts, and any\ \ software the user wants built. Replaces coder + preview-dev with a unified methodology." tags:

• engineering
• development
• tasks
• dashboards
• preview
• debugging

tools:

• read_file
• write_file
• edit_file
• bash
• preview_serve
• preview_stop
• preview_check
• community_publish
• community_unpublish
• community_list
• register_task
• activate_task
• cancel_scheduled_task
• update_scheduled_task
• list_scheduled_tasks
• get_scheduled_task_log

delivery: script triggers:

• build me
• create a dashboard
• set up monitoring
• schedule a task
• make a web app
• write a script
• something is broken
• it's not working
• debug this
• fix this
• preview
• publish

Phase 0: SKILL DISCOVERY & REQUIRED READING

A. Pick the skills. Gather every data source the project needs. For each one, prefer a skill: check <available_skills>, and if nothing fits, try search_skills(query) for official + marketplace coverage. Skills are the most reliable layer — they ship tested clients, auth, and rate-limit handling. Web search is a last resort. Only write raw HTTP / SDK code when no skill can cover the source.

B. Read the platform rules for what the project touches. These rules live in references (not in your system prompt) so you must read_file them before writing code. Skipping this is the #1 cause of 401s, broken paths, and "worked locally, fails in preview" bugs.

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: "Approve this plan and proceed to Phase 2 BUILD?" Match the user's language when phrasing the question — never inject a hardcoded non-English string.

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

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.

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.

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

Dashboard UX Defaults (type=preview)

Decide sensible defaults yourself and render real data on first load. Treat filters as optional refinements users can adjust later — never as prerequisites that gate the initial view. Auto-refresh on a sensible interval. No "Click to load" / "Enter address" / "Select symbol" before anything appears.

Platform Rules

• Agent tools are tool calls only — not importable in scripts
• Preview paths must be relative (./path not /path)
• Hardcode the preview port in code, do not read from env. Each preview runs in its own pod and the env-port contract is not reliable across pods. Pick any free port (e.g. 8765), write it directly into the app, and pass the same number to preview(action="serve", port=...). The two must match exactly.
• Concurrent previews need different IDs. If two previews share the same dir, the newer one auto-kills the older one (same-dir replacement rule). When iterating, reuse the same id rather than inventing variants, or use distinct dirs.
• 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

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`

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