The deployment playbook

Build things that outlive the window

Most people will spend the free window having impressive conversations, and on July 8 they'll have nothing to show for it. Chats evaporate; assets compound. This is the full deployment guide for the other approach: point Fable 5 at building durable artifacts — reusable skills, agent scaffolds, diligence templates, tested code, operating manuals — that keep paying off on whatever model you run next. Cheaper models can execute what the frontier model authored.

⏳ The window ends end of day July 7 (US Central).

The principle: author on Fable, execute on anything

Fable 5's edge is judgment over long horizons — the design, the review, the synthesis, the test of whether a thing is actually right. That edge is exactly what you want baked into artifacts. A skill file written and stress-tested by Fable runs fine on Opus 4.8 in August. A migration with a Fable-authored test suite stays correct no matter who edits it next. A diligence template distilled from your ten best past decisions doesn't care which model fills it in. Spend window tokens on authorship; save the cheap tokens for execution.

The six durable assets

Asset 01 · Reusable skills

Turn your recurring workflows into tested skills

Anything you do more than weekly — report formats, review checklists, content pipelines, deploy rituals — becomes a skill/command file. Fable is unusually good at writing these AND at testing them against real examples before you trust them.

I'm building a library of reusable skills so future (cheaper) models can run my
workflows with your rigor. Watch me describe [workflow]. Write it as a skill file:
trigger, steps, quality bar, failure modes. Then TEST it against these 3 real past
examples [paste], score its own output against what I actually shipped, and revise
the skill until it matches or beats them. Deliver the final skill file only.

After July 7: the skill runs on Opus/Sonnet daily; you re-author on Fable only when a workflow changes.

Asset 02 · Agent scaffolds

Have the best orchestrator design your orchestration

Fable manages subagent fleets better than any model — so have it design the scaffolding your smaller models will run inside: task decomposition templates, verification loops, memory-file conventions, checkpoint patterns. The scaffold outlives the engine.

Design an agent scaffold for [recurring job: e.g. weekly competitive research].
Deliverables: (1) an orchestrator prompt that decomposes the job for parallel
subagents, (2) a fresh-context verifier prompt that checks their work against the
spec, (3) a memory-file convention (one lesson per file, one-line summary on top),
(4) a dry run on this real example [paste], with the scaffold revised from what
the dry run exposed. Assume the agents running this next month are weaker than
you — build the guardrails they'll need.

After July 7: Sonnet 5 staffs the fleet at $2/$10 — inside a structure a frontier model designed.

Asset 03 · Diligence & decision templates

Distill your best judgment into rubrics

Investors, buyers, hiring managers: your edge is pattern recognition from past calls. Fable can read a stack of your past decisions — deals, vendors, hires, projects — and extract the rubric you were unconsciously using, then harden it with the cases where you were wrong.

Here are [N] past decisions with outcomes [attach memos/notes]. Reverse-engineer
the evaluation rubric I was actually using: the criteria that predicted the good
outcomes, the red flags I missed on the bad ones. Deliver a one-page diligence
template with scoring anchors and a "walk away if" list — written so a colleague
(or a cheaper model) applying it cold reaches my quality of judgment.

After July 7: every new deal gets the template on any model — the judgment is in the document now.

Asset 04 · Tested code

Clear the backlog nobody wanted to touch — with the tests that keep it cleared

The migration, the rewrite, the dependency upgrade: window-perfect work because Fable's first-shot correctness on well-specified problems is its signature. The durable part isn't the code — it's the test suite that guards it.

Scope this [migration/refactor] end to end. Before writing implementation code,
write the test suite that defines "done" — including the regression cases that
would catch the failures this codebase has actually had [link history]. Then
implement until the suite passes, and verify with a fresh-context review subagent
against the spec. The tests are the deliverable; the code just has to pass them.

After July 7: any model (or any human) can touch the code — the Fable-authored tests hold the line.

Asset 05 · Operating manuals

Write the runbook while the writer is brilliant

SOPs, runbooks, onboarding docs — the boring compounding assets. Fable can read a quarter of tickets, transcripts, or shell history and produce the manual your operation never wrote.

Read [tickets/transcripts/history]. Write the operating manual this reveals we
need: the recurring situations, the correct responses, the escalation triggers,
the mistakes that repeat. Organize by situation, not by system. Flag every place
where the source material shows us contradicting ourselves — those are decisions
we owe ourselves, listed separately as an appendix.

After July 7: the manual onboards people and models alike. Update quarterly on whatever's cheap.

Asset 06 · Your knowledge base

Bootstrap the memory your future agents will read

Anthropic's own playbook says Fable performs best with a memory system — and recommends bootstrapping it by having the model mine your past sessions for lessons. Do that mining now, with the best miner.

Reflect on our previous sessions and project history [point at logs/repo].
Use subagents to identify the core themes, corrections, and confirmed approaches.
Store them as memory files — one lesson per file, one-line summary at the top,
including why each mattered. Don't save what the repo already records. This
memory will be read by weaker models later: write for them.

After July 7: every future session, on every model, starts smarter. (This is Recipe 05 supercharged.)

The remaining days, scheduled

WhenDoOn which model
Each morningCheck usage, verify last night's output, file the artifacts (commit skills/templates/manuals), note lessons in memoryOpus / Sonnet — don't spend Fable on admin
Each afternoonPrep the night's brief: pick ONE asset, gather its inputs (repos, docs, examples), write the goal-reason-constraints promptOpus — prep is cheap-model work
Each eveningLaunch the Fable run with checkpoints + memory instructions (Recipe 07), let it work longFable 5 — this is the window spend
July 7, eveningLast run: the biggest single job left. July 8+: execution moves to the ladder — Opus 4.8 daily, Sonnet 5 for fleets, Fable only if credits genuinely pencil

Moral: the window isn't a free trial of a chatbot — it's four days of frontier authorship. Leave July 8 holding artifacts, not screenshots.

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