| name | evidence-scoring |
| description | Score anything — a draft, a build, a submission, a candidate, a vendor — using the seven-principle methodology from "Don't Let the LLM Pick a Number." The LLM never picks the score; it collects discrete signed evidence items and a formula computes the number. Use this skill whenever the user wants to evaluate something rigorously, build a custom scoring rubric, replace a vague 0-10 LLM judge, or set up reproducible scoring across runs. Triggers on phrases like "score this", "build me a rubric", "evaluate this", "judge X across multiple dimensions", "stop my LLM from picking 7 every time", or any request that smells like rubric-based scoring done seriously. |
| license | MIT |
Evidence Scoring — the seven-principle methodology, generic
The user brings the domain. You bring the structure. Together you produce a defensible 0–100 score whose number is computed from evidence, not chosen by the LLM.
This skill is the lifted methodology from the paper Don't Let the LLM Pick a Number. Its sister skills (what-works-feedback-judge, hackathon-judge) are pre-baked applications. Use this one when nothing pre-baked fits.
When to use this
- The user wants to score something but every off-the-shelf rubric is too generic.
- An existing LLM judge is producing 7-out-of-10s no matter the input.
- The user wants reproducibility across runs and reviewers.
- The user is willing to do a 5-minute setup conversation in exchange for stable scores.
If the user just wants quick feedback on a draft, use what-works-feedback-judge. If they want to score code submissions with optional demo video, use hackathon-judge. This skill is the toolkit underneath both.
Run the probe first
Before doing the setup conversation, run the calibration-probe skill on the candidate scoring model. The probe takes ~30 seconds and classifies the model into one of five regimes (CALIBRATED, INFLATION_LIKELY, DEFLATION_LIKELY, PICKS_A_NUMBER, JITTERY). The regime determines how much of this skill you should use:
- CALIBRATED — use Principles 1, 4, 5 only (lighter touch). The full counter-bias may over-correct.
- INFLATION_LIKELY — use everything in this skill. The methodology was designed for this case.
- DEFLATION_LIKELY — use the methodology but soften the counter-bias instructions.
- PICKS_A_NUMBER — halt. The formula cannot rescue a model that gives essentially the same number to everything.
- JITTERY — halt or ensemble (run the model 3–5 times and average).
- AMBIGUOUS or untested — proceed with the full setup but flag the run as "untested model" so the user knows to validate before scaling.
If the user has not run the probe, do not block on it — proceed with the full setup and surface the flag in the final report. The probe is a recommendation, not a hard gate. (Empirical rationale in paper/paper.md Sections 5.5 and 5.7 — calibration-conditional adoption rule across six v3 models.)
The seven principles (memorize these)
- Separate observation from scoring. The LLM finds evidence. A formula, not the LLM, produces the score.
- Discrete signed impact items. Every observation gets one of
{+5, +3, +2, +1, -1, -2, -3, -5}. No 0-10 picks. No "pretty good." Forces commitment.
- Diminishing returns.
normalized = net_impact / sqrt(total_items). The 40th item adds less than the 4th. Evidence farming is punished.
- Density-weighted confidence. Confidence = how much evidence the scorer found, not how sure the scorer feels. Sparse runs are visibly low-confidence.
- Anchored center. Sparse-evidence runs regress toward 50. The multiplier never exceeds 1.0 — high evidence confirms but never amplifies beyond raw.
- Bounded scale with self-check. Scores live in
[0, 100]. Across criteria, the spread must be ≥ 20. If the spread is smaller, the evaluator wasn't discriminating.
- Separation of LLM and deterministic computation. Independent passes by different model families collect evidence. Math, not the LLM, combines them.
Two application patterns
You will pick one based on the user's needs. Both use the same per-criterion formula; they differ in how many criteria run.
Pattern A — Pooled (one bucket, one score)
For simple checkers: one scope, one final number. Examples: "is this draft ready to ship?", "is this PR safe to merge?", "is this candidate worth a phone screen?".
items[] # all evidence in one pool
net_impact = sum(item.impact)
total_items = len(items)
normalized = net_impact / sqrt(total_items)
raw = clamp(50 + normalized * 8.0, 0, 100)
density = total_items / 20
multiplier = 0.75 + 0.25 * clamp(density, 0, 1) # never > 1.0
final = round(50 + (raw - 50) * multiplier)
confidence = clamp(density, 0, 1)
what-works-feedback-judge is a pooled skill (Working / Not working / Missing / Confusing — four labeled buckets that score as one pool).
Pattern B — Per-criterion matrix (5×5 or N×M)
For benchmarks: every dimension scored independently, then weighted-averaged. Examples: hackathon submissions, vendor evaluations, candidate scorecards, model benchmarks.
# For each criterion c:
per_criterion[c] = pooled_formula(items_in_c)
# Across criteria:
overall_score = round(sum(c.final * c.weight for c in criteria))
overall_confidence = min(c.confidence for c in criteria)
self_check_span = max(c.final) - min(c.final) # must be >= 20
Default 5×5 matrix when the domain doesn't suggest something specific:
| criterion_1 | criterion_2 | criterion_3 | criterion_4 | criterion_5 |
|---|
| requester | items… | items… | items… | items… | items… |
| sme | items… | items… | items… | items… | items… |
| end_user | items… | items… | items… | items… | items… |
| production | items… | items… | items… | items… | items… |
| adversary | items… | items… | items… | items… | items… |
Customize when the domain demands. A code review benchmark might use perspectives author / reviewer / future_maintainer / on_call / adversary. A pitch evaluation might use customer / investor / competitor / regulator / engineer. Forcing five perspectives is the principle, not the specific labels.
The setup conversation (5 min)
Before scoring anything, walk through this with the user. Capture answers as you go.
- What are we scoring? (one sentence — "the new homepage hero", "this Next.js refactor", "this Series-A pitch deck", etc.)
- Pooled or matrix? (One score? → pooled. Multiple dimensions you want to see separately? → matrix.)
- If matrix: what are the criteria? Aim for 3–5 unless they have a strong reason for more. Each one should be scorable independently — if two criteria can't be separated cleanly, merge them.
- If matrix: what are the perspectives? Use the default 5 unless the domain has obvious better ones. Forcing multiple stakeholder viewpoints is the principle.
- What do +5 and −5 look like in this domain? Get one concrete example of each per criterion. The anchors are the calibration. Vague anchors → vague scores.
- Weights? Only ask if matrix. Default-equal is fine if the user can't articulate weights. Write them out: must sum to 1.0.
- What planted traps exist (if any)? A "trap" is something the scorer is supposed to catch and penalize. Fake records in a dataset, jurisdiction violations in a legal doc, hallucinated facts in a research summary, single-API-call-as-AI in a "novel AI" pitch. List them; assign expected impact (
+5 if caught, -5 if normalized is the canonical pattern).
Show the user the catalog before scoring. Iterate until they say "yes, that matches the work."
Collecting evidence
Now run the scorer. For each item:
- Description — short, specific, falsifiable. Not "good UX" — "user can recover from a deleted item with one click at app/items/page.tsx:84".
- Evidence — file:line, timestamp, page number, quote. The reader must be able to verify.
- Impact — exactly one of
{+5, +3, +2, +1, -1, -2, -3, -5}.
- Cell (if matrix) — which perspective × criterion this item belongs to.
Hard rules:
- 5 items per cell maximum. Anything past 5 is padding; sqrt normalization gives diminishing returns anyway.
- 3 items per cell minimum target. Fewer than 3 means you didn't look hard enough.
- No invented evidence. If you didn't observe it, don't list it. Sparse evidence is honest signal — fabricated evidence corrupts the formula.
- Impact must come from the set. No
+4. No -2.5. The discrete set is what removes the LLM-pick-a-number anchor; if you let yourself off the hook, you'll drift back to picking 7.
Computing the score
Run the formula. Show your math — the user needs to see the numbers, not trust the black box.
net_impact <X>, total_items <N> → normalized <V>
raw_score <R>, density <D> → multiplier <M>
final_score <S>, confidence <C>
Then add 1–2 plain sentences explaining what specifically pushed the score up or down. The math + the prose together = a defensible report.
If matrix: also report per-criterion scores, the weighted overall, the overall confidence (= min across criteria), and whether the self-check passed (span ≥ 20). If the self-check fails, the evaluator was not discriminating; flag it explicitly and offer to re-examine the items.
When something doesn't fit
- The user wants pairwise comparisons, not absolute scores. That's a different methodology (see
lechmazur/writing-style). Pairwise can complement absolute scoring as a diagnostic, but don't replace the formula with it.
- The user wants a sub-score below 0 or above 100. Don't. Clamp. The bounded scale is principle 6 for a reason.
- The user has fewer than 3 items per cell. Either look harder, or accept the low confidence and report it honestly. Density is signal — don't paper over it.
- All scores cluster within a 20-point band. Self-check failure. Re-examine items. The evaluator was either soft or unable to distinguish — both are surfaceable as feedback.
- The model probes PICKS_A_NUMBER or JITTERY. The methodology won't help — switch models or accept that automated scoring on this model is unreliable. The formula cannot rescue intrinsically weak signal. Run
calibration-probe if the regime is not yet known.
Final report format
# <subject> — Evidence Scoring report
**Final: <overall_score>/100** — confidence <overall_confidence>, self-check <pass/fail>
## Per criterion
| criterion | final | conf | net | items | weight |
|---|---|---|---|---|---|
| brief_fidelity | 78 | 1.0 | +18 | 22 | 0.30 |
| trap_handling | 92 | 0.85 | +24 | 17 | 0.25 |
| production_correctness | 41 | 0.7 | -10 | 14 | 0.20 |
| domain_judgment | 60 | 0.6 | +4 | 12 | 0.15 |
| long_horizon_carry | 58 | 0.5 | +1 | 10 | 0.10 |
## Top items
- (+5, requester × brief_fidelity) — <description, evidence>
- (+5, adversary × trap_handling) — <description, evidence>
- (-5, production × production_correctness) — <description, evidence>
- ...
## Math (show your work)
- brief_fidelity: net +18, items 22 → normalized +3.84 → raw 80.7, density 1.10 → multiplier 1.0 → final 81
- trap_handling: ...
## Why this score
1–3 paragraphs. Top drivers in plain prose. Where the evidence was thin. What the self-check told us.
Reference
- Paper:
paper/paper.md in this repo (CodefiLabs/pickanumber)
- Worked rescoring example:
examples/impeccable-rescoring.md — what changes when an existing LLM-judged benchmark drops the LLM-picks-a-number step
- Companion analysis:
examples/cua-bench-analysis.md — when the methodology partially fits (deterministic-reward benchmarks)
- Sister skills:
calibration-probe (preflight regime classifier — run before this skill), what-works-feedback-judge (pooled, 4-bucket idea-readiness), hackathon-judge (per-criterion, project submissions with optional video)
Done
When the report lands, stop. Don't volunteer recommendations. The score plus the top items plus the math is the deliverable; what the user does with it is their call.
