thrifty-dispatch

name

thrifty-dispatch

description

Lean tiered-build orchestrator (JSONL-dispatch architecture). Use for "thrifty fast / lean / dispatch", or a multi-sprint build where cost matters. The architect (Sonnet) writes a contract + a sprints.jsonl and calls a dispatch SCRIPT that loops cheap bare model calls (claude -p --bare, subscription, no API key) to write each sprint to disk — NO subagents. The orchestrator never ingests sprint outputs, only a manifest, so its context stays tiny. Then it runs the gate and surgical-fixes.

thrifty-dispatch — write JSONL, dispatch, verify (the lean architecture)

This is thrifty rebuilt on the speed-run / local_code_gen substrate: the LLM writes a spec; a deterministic script runs the per-sprint model calls. It exists because the subagent version is dominated by orchestrator cost — each subagent carries 40k of harness context and its report lands back in the orchestrator's context, which then gets re-read every turn (measured: 1.2M cache-read, $1.86 on a 3-sprint task). This architecture avoids both: bare calls ($0.02–0.05 each) and outputs go to disk, not into your context.

Why it's cheap (the two fixes)

1. Bare calls. Each sprint runs claude -p "<prompt>" --model {haiku|sonnet} --bare — --bare strips hooks/plugins/system-prompt, so a Haiku sprint call is ~$0.002–0.05 instead of ~$0.05 + 40k harness. Uses your subscription OAuth — no API key.
2. You never ingest outputs. The script writes files to disk and returns a manifest.json. You read the manifest (tiny), not the generated code. No context compounding, no re-read tax.

Your flow (you are the architect; keep YOUR turns + context lean)

1. Frame — confirm a spec exists first. thrifty executes a spec (requirements + what "done" means); it does the planning, but does NOT invent the requirements. If the user gave no spec or pointer, don't fabricate one and build — tell them thrifty executes a spec they bring (authored however they like — thrifty is plan-agnostic), offer to help draft/brainstorm it, and re-enter once it exists. Otherwise: read the spec / explore context. Choose a <slug>; work in a clean dir.

2. Write contract.md — the cross-sprint surface ONLY (shared interfaces/seam, conventions, ownership, dep graph). Lean — pin what crosses a boundary, trust the executor for interiors (Haiku is strong; this is not byte-pinning for a local model).

3. Write sprints.jsonl — one JSON object per line:

   {"id":"SPRINT-001","tier":"haiku","kind":"generate","deps":[],"brief":"...terse brief + acceptance criteria..."}
   

   • tier: generate sprints always run on Haiku (the script forces it) — codegen never escalates. If a unit feels too hard for Haiku, split it into smaller sprints; do not reach for a stronger executor. Sonnet is used only for planning and the fix loop.
   • kind: generate (model emits <FILE path="...">...</FILE> blocks → script writes them) or brief (model emits markdown → saved to briefs/<id>.md).
   • deps: sprint ids that must finish first (script runs in dependency waves, parallel within a wave).
   • For a split-planning pass, first emit kind:"brief" sprints (tier sonnet) to write briefs, then kind:"generate" sprints (tier haiku) that consume them. For small jobs, just write the briefs inline here and skip the brief pass.

4. Dispatch — run the script via Bash. Prefer --run "<gate>" (full pipeline: plan → execute → gate → bounded fix), or bare for execute-only:

   python3 <this-skill-dir>/dispatch.py --run "node --test"
   

   It loops the sprints, makes the bare calls (parallel within a dep wave), writes files to disk, and emits manifest.json + run_manifest.json. Do not read the generated files into your context — read the manifests.

   Run it in the BACKGROUND and narrate progress to the user — a multi-minute silent Bash call is a bad UX. The script streams flushed milestones you should relay as they appear, e.g. [plan] done: … N sprints → [execute] ok SPRINT-003 → … (3/N) → [gate] PASS → [done] … = $X. The manifests are the durable checkpoints (plan_manifest.json after planning, manifest.json after execute, run_manifest.json at the end), so poll/tail and report: "✅ Planned N sprints → ⏳ executing 3/N → ✅ gate passed, $X." Keep these summaries lean — relay the milestones, not the file contents.

5. Verify (gate) — run the task's gate yourself with Bash (node --test, pytest, a CLI smoke). This is the independent check; cheap, no model. Gate on the command the project actually ships with (npm run build, tsc -b && vite build, go build ./...), not a weaker proxy that can pass while the real build fails (e.g. a bare tsc --noEmit is often a no-op under a tsc -b project-reference setup). If unsure, read package.json scripts / build config and gate on that.

6. Surgical-fix loop — for each failure, read ONLY the failing test + the relevant code, diagnose (code bug vs the executor's own bad test), and fix in place — or re-dispatch that one sprint with a corrective note appended to its brief. Re-run the gate. Bound it: a couple of fix passes, then surface to the human.

7. Report — what was built, gate result, and total cost (your orchestrator session /status + manifest.json's dispatch_cost_usd).

Verified (task 01, wordfreq, 3 sprints)

Dispatch tier cost $0.078 (3 Haiku bare calls), one-shot 18/19 tests, 19/19 after one surgical fix — vs the subagent thrifty's $1.86 for the same task. The one failure was the executor writing a test that contradicted the contract's tokenizer (word0 collapses to word under "split on non-letters") — caught by the gate, fixed in one edit. That is the loop working as designed.

Rules / gotchas

• Executors emit text, not tool use. --bare strips tools; the model outputs <FILE> blocks and the script writes them. Don't expect the sprint call to run tests or read files — that's the orchestrator's job at the gate.
• Confirm model routing in a new environment once: claude -p "hi" --model haiku --bare --output-format json should cost ~$0.002 (Haiku rate), not ~$0.05+ (a fallback to a bigger model). Verified working as of this writing.
• Keep the contract lean and your own turns few — the orchestrator's context × turns is the cost that this architecture exists to minimize. Write the two files, call the script, read the manifest, gate, fix. Don't re-read artifacts.