bean

name

bean

description

Run a task as a recursive convergence loop, not a fixed checklist: investigate the most decisive open question, record what you learn as typed evidence in a ledger, let a compiler score what is still weak or contradictory, revise beliefs that new evidence overturns, and loop until the evidence converges — then deliver. Continuously spins up subagents and uses whatever skills/connectors exist. Verbose by default. Trigger on /bean, on "do this thoroughly / be systematic / deep work mode", OR when a task spans multiple files, sources, or sessions. Do NOT trigger on ordinary tasks a direct attempt handles fine. Auto-trigger applies only where the runtime allows it; on Codex, bean runs only on an explicit /bean.

bean

bean runs a task the way the Fable model worked: it keeps looking where a one-shot answer would stop, kills its own incorrect beliefs as it learns, validates its work against a real signal before declaring done, and improves from its own accumulated notes. It is a recursive convergence loop, not a fixed sequence of phases.

bean runs on a runtime: a claim ledger (typed evidence with tiers, that can be superseded) plus a compiler that scores convergence (conflicts, coverage gaps, weak evidence, undischarged risks) and exits nonzero until it is reached. bean ships its own self-contained compiler — bean-check, a single static binary — as the default, so the loop has a real gate that runs anywhere. In strict mode it gates load-bearing claims on a real external oracle (verified_by + bean-verify), and a native Stop hook (bean-hook) couples it to execution so the agent can't finish until it converges. grainulator/wheat is an optional richer backend when present. See references/runtime.md.

A note on what this is. bean shapes the procedure a model follows, not its capability ceiling. The recursion and self-correction it prescribes are only as good as the model running them. Treat this as a loop discipline, not a capability transplant.

When NOT to use this

If a task has one obvious correct approach and fits in a single pass, do it directly. The convergence loop earns its cost only when the right answer is not knowable up front — when you have to investigate, get it partly wrong, and correct toward it.

Match the deliverable to the request. If you were asked only to assess — review, audit, recommend — deliver the assessment with its residual; don't quietly turn it into edits. Reversible actions the task plainly implies are fine to take; destructive or scope-expanding ones get confirmed first (see the mutation policy in references/runtime.md).

The loop

Frame once, then iterate the inner loop until it converges. This is loop-until-dry on the claim graph: you stop when there is nothing decisive left to learn, not after a fixed number of steps.

Frame (once)

State the question or goal. Seed the ledger with the known constraints as claims. Confirm the task earns the loop (otherwise just answer it).

Iterate until converged

1. Survey the environment. Re-assess what's available this round: loaded skills, subagent tooling, MCP connectors, data sources. Fable's strength was learning the environment first and then acting on a grounded picture — do this every round, not once. See references/discover.md.

2. Investigate the most decisive open front. Let the compiler's signal pick the target: the unresolved conflict, the weakest-evidence topic, the missing claim type — the one thing whose answer most changes the outcome. Spin up subagents and use existing skills/connectors to gather evidence; don't do serially what you can fan out. See references/delegate.md.

3. Record as typed claims with evidence tiers. Write what you learned into the ledger — factual / constraint / risk / recommendation / estimate, each at its evidence tier (stated < web < documented < tested < production). The ledger is your memory across rounds; ground claims in sources actually read, never assumption. See references/verify.md.

4. Compile. Run the runtime's compiler (bean-check by default). It returns the convergence signal: unresolved conflicts, coverage gaps, single-source / type-monoculture topics, weak-evidence / below-bar claims, and undischarged risks (with wheat, also a confidence read), and exits nonzero until convergence. This is a check that can fail — "it looks right" is not it. See references/bean-check.md.

5. Revise beliefs. When new evidence overturns an earlier claim, supersede it ("kill the incorrect belief") and resolve the conflict, rather than letting the contradiction stand. The loop goes forward and backward. See references/belief-revision.md.

6. Converged? Don't stop at "looks done, but…". Drive every open front to one of three terminal states — confirmed non-issue, fixed-and-verified, or a true residual you genuinely can't reach from here. This includes every concern you noticed: a risk or contradiction you logged is an open front, and recording it is not resolving it — proceeding past a logged-but-undischarged concern is the dominant failure mode. A caveat that's none of those three is a place you stopped early; keep looking where a one-shot answer would quit. Converged = no unresolved conflicts + evidence bar met for every load-bearing claim + a full round drove no front to a new state. Otherwise go back to step 1 on the next-most-decisive front. On high-stakes work, run an independent adversarial / cross-model check before declaring done. See references/convergence.md and references/codex-blindspot.md.

Deliver

Present the converged answer plus the residual: the weakest surviving claims, anything still unverified, and what would change the conclusion. See references/self-critique.md.

Verbose by default

bean works in the open. Each round, surface the ledger state, the compiler's signal, what that signal made you investigate next, and which beliefs you revised. The point is that an observer can see the answer converge and where any error entered. /bean quiet collapses the per-round narration but never hides an unresolved conflict or an unverified load-bearing claim. See references/verbosity.md.

What this skill doesn't do

It doesn't make the model smarter. A model that can't tell good evidence from bad, or won't actually revise a wrong belief, will converge on a confident wrong answer faster. bean structures the iteration; the judgment inside each step is the model's. When a task is genuinely beyond the model, say so rather than looping to a plausible-sounding wrong result.