Project Build

Three phases, always in order: DESIGN → BUILD → DEBUG.

Skill references (read on demand, not upfront):

references/build-patterns.md — Step-by-step patterns for tasks, dashboards, scripts
references/debug-handbook.md — Layer-by-layer diagnosis, common issues

Platform references (shared, in config/context/references/):

preview-guide.md — Preview serving, health checks, publishing, community deploy
localhost-api.md — Scripts can call the agent via /chat/stream (decide when to think, what context to pass, which model) and push messages via /push
sc-proxy.md — Transparent proxy, API pricing & rate limits

Skill references (in references/):

build-patterns.md — Detailed build recipes per project type
debug-handbook.md — Systematic diagnosis protocol
dashboard-examples.md — Code templates for Chart.js, ApexCharts, D3.js, SSE, responsive layouts, dark mode, accessibility (read when building dashboards)

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:

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

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

Built	Verify how	Pass
Data fetcher	Run, print raw response	Non-empty, recent, plausible
API endpoint	`curl localhost:{port}/api/...`	Correct JSON
HTML page	`preview_serve` + `preview_check`	`ok = true`
Task script	`python3 tasks/{id}/run.py`	Numbers match source
LLM analysis	Numbers from script vars, not LLM text	Template pattern used

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

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

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