| name | worth-ledger |
| description | Measure whether shipped changes were actually liked and what they were worth — elicit valuations from the user, record worth against tokens spent, and feed the results back into stopping thresholds and direction weights. Use at the end of a cycle, after a batch of changes lands, or when deciding where effort pays. |
Worth Ledger
Unmeasured improvement is indistinguishable from motion. The worth ledger
closes the loop: every cycle's output gets appraised — was it liked? what was
it worth? — and the appraisal recalibrates everything upstream: the hunt's
stopping threshold V, the interview's direction weights, and the honest
answer to "is this project still paying for attention?"
Lives at .forward/worth.jsonl, one JSON object per appraised change or batch:
{"ts":"2026-07-09","change":"idempotent ingest retries","commits":["abc123"],"area":"ingest/retry","finds_addressed":2,"probes_spent":9,"verdict":"loved","worth_probes":40,"note":"user: 'this was the thing silently corrupting batches'"}
worth_probes denominates value in the same unit as spend (probes ≈ one
focused attention-unit), so value-per-token is a dimensionless ratio you can
compare across areas, cycles, and projects. area and finds_addressed
link each appraisal back to the hunt ledger — without them the calibration
below is not computable, only narratable. When only a verdict exists (no
elicited magnitude), use the default anchors — disliked 0, shrug 1,
fine 3, loved 9 — and record that anchors were used; an elicited worth
overrides anchors and records its rationale in note.
Eliciting from the user
Appraisal is one light moment per cycle, batched — never a survey after every
diff. Show the shipped changes compactly (one line each, with the observable
effect, not the implementation), then ask two things:
- Verdict per change or batch: loved / fine / shrug / disliked. "Shrug"
is a real answer and important to make cheap to give — a user who must
praise everything gives you no signal.
- Worth, anchored concretely: "Which of these would you have paid a
dedicated hour for? Which shouldn't I have bothered with?" Relative
ranking beats absolute numbers; people rank reliably and estimate poorly.
If the user engages beyond the batch question, mine the conversation — a
spontaneous "oh that one was great" is the highest-fidelity datum the ledger
ever gets. If no human is available, self-appraise conservatively, mark
"appraiser":"self", and let a later human verdict overwrite it.
Feeding back
- Stopping threshold: per area,
V = Σ worth_probes / Σ finds_addressed
over that area's records — probes of value per find, computable by replay
with no interpretation. High-worth areas earn longer hunts (silence
threshold 3·V grows); persistently shrug-rated areas get released sooner.
- Direction weights: sum worth by dimension (depth/breadth/reach/...).
A dimension whose changes keep landing "loved" is where the next
autonomous cycle defaults; a dimension the user keeps shrugging at is
evidence the interview's excitement answer has gone stale — re-ask it.
- The project-level curve: value-per-probe across cycles is the health
metric. Declining curve + statistical silence everywhere = the well is dry;
say so and recommend stopping. A rising curve after a find-rich cycle is
the hazard principle working — report it as license to keep spending.
Benchmarking honesty
The ledger is also the anti-slop instrument: it makes "the agent did a lot"
and "the agent did a lot of good" distinguishable. Report both numbers,
always — probes spent and worth returned — and never quote one without the
denominator.
