القائمة
Use when budgeting, routing, and scaling a run to fit the ask.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | cost-aware-pipeline |
| description | Use when budgeting, routing, and scaling a run to fit the ask. |
A harness that cannot finish under its own cost gates — or that burns a build-sized budget answering a question — is anti-enhancement. The economics rest on four levers: tokens as the primary ledger, the scale gate, model routing by job, and killing branches before the expensive passes see them.
PQA runs on a Claude Code subscription: USD is synthetic; tokens, rate limits, and
context are real. Budget(max_usd, max_tokens, warn_at=0.8) trips on EITHER axis;
run_budget_tokens (default 800k) is the number that matters and run_budget_usd
(default 5.0) is the display/API-mode cap. Model identity flows through ONE
translation point — pqa.cost.resolve_model (aliases fable|opus|sonnet|haiku) — so
projection, dispatch, and recording can never price different models for the same call.
| Ask | Mode | Spend shape |
|---|---|---|
| compare / choose / review / explain | decide | frames + ONE judge pass over idea digests; < 30k tokens; output flagged judgment — not verifier-backed |
| single-file fix, rename, config tweak | patch | n_branches=2, no scout, abbreviated frame |
| feature / refactor with real unknowns | build | full loop, N from config |
Never silently upgrade a decide-ask into a build run — a question answered with a full superposition is the harness's worst failure mode: enormous spend for an answer one judgment pass could give. When unsure, ask the operator which mode they want.
| Tier | Work | Agents |
|---|---|---|
| fable | coding + quality-critical judgment | generator (every branch), unknown-scout, adversary, collapse-judge, baseline-runner (fair control) |
| sonnet | mechanical execution + orchestration | orchestrator, frame-loader, verifier, reconciler, self-reflector, instinct-synthesizer |
| haiku | bookkeeping | memory-curator, failure-taxonomist, eval-runner |
Pass model explicitly on every Task dispatch. Never burn fable tokens on arithmetic,
table rendering, or registry writes. The table is enforced by
scripts/validate_components.py — adding an agent means making a routing decision.
governor.would_abort(model, projected_in, projected_out) — if
True, do NOT dispatch: write the aborted RunReport (partial state,
memories_injected included) and stop. Projections are pessimistic by design
(max(len(prompt)//3, floor) input, fixed output floor): refuse a dispatch that
might fit rather than admit one that won't.governor.record(branch_id, model, actual_in, actual_out) from the
subagent's reported usage, then should_abort() as belt-and-braces.
should_abort() alone fires after the money is spent — that asymmetry is why both
gates exist.Order verification by cost: lint/type-check (seconds, zero tokens) kills compile-broken branches BEFORE the adversary, so the most expensive judgment pass only attacks branches that could win. The same shape applies everywhere: cheap gates first, fable last.
N=3 build run, 800k-token budget: frame (sonnet, ~25k) → 3 generators (fable, ~3×90k)
→ static early-kill (0 tokens — b1 dies on pyright; taxonomy row verifier: failed static checks before collision — pyright: 4 errors) → 2 adversaries (fable, ~2×60k)
→ 2 verifiers (sonnet, ~2×30k) → judge (fable, ~20k) → bookkeeping (haiku, ~10k)
≈ 410k total: completes with headroom.
The same run WITHOUT early-kill pays ~60k fable tokens attacking a branch that could never merge. The same ask phrased as "which approach is better?" should have cost < 30k in decide mode — two orders of magnitude apart, governed by the gate, not luck.
A run that does abort mid-loop records every live branch as budget: aborted at collide, 612k/800k tokens spent — and retrieval never reads those rows as refuted
(see failure-taxonomy).
should_abort() without would_abort() bills you for the
dispatch that broke the budget./cost or /budget dispatching a model. They are Bash wrappers over the
engine's ledger — a model call to read a number is the disease in miniature.Use when attacking branches to find what the verifier cannot catch.
Use when flagging branch conviction or calibrating instinct against outcomes.
Use when selecting the surviving branch on verifier evidence after collision.
Use when recording dead branches so future runs avoid re-litigating them.
Use when isolating branches in git worktrees and reconciling the survivor.
Use when verifying a branch with any stack's real test toolchain.