# Principle Bind To Named Inventory

> Apply after an audit, scoring review, or a request for a "real classifier" / "less arbitrary weights" when existing scorers, rankers, or weight formulas were already named. Pick among those named gaps before proposing a new model or library.

- Skill: `edbertchan/principle-bind-to-named-inventory` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add edbertchan/principle-bind-to-named-inventory`
- Raw SKILL.md: https://api.skillmd.com/api/skills/edbertchan/principle-bind-to-named-inventory/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: EdbertChan (https://skillmd.com/u/edbertchan)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/edbertchan/principle-bind-to-named-inventory

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# Bind to Named Inventory

After an audit (or any pass that names N existing scorers/rankers/formulas
and their failures), the next plan must pick among **those named gaps**.

Do not add a new model, library, or parallel scorer until each named system
is mapped: what it scores, whether its weights are literal or labeled, and
which failure the user is pointing at.

**Must always:**

- Bind phrases like "real classifier" or "these weights are arbitrary" onto
  a named scorer from the inventory ("the `.5/.3/.2` mix is finding-priority
  ranking; flag `a` appearing in two frauds is `fraud_learning` case
  frequency") before proposing sklearn, logistic regression, or a new file.
- Prefer replace-or-feed an existing scorer over adding a fourth.
- If the user is not comfortable with ML, teach the existing named scorers
  first. Offer a no-library path before a new dependency.

**Must never:**

- Box the user into a new prediction target that was not in the audit.
- Treat "I need a real classifier" as license to invent a model while an
  exact-string transfer gap already named in the same session sits unused.

**Battle-tested:** A scoring audit named three heuristics and that held-out
flag types often get weight 0 because the type strings do not match across
companies. The follow-up "we need a real classifier; the `.5/.3/.2` weights
are arbitrary" was boxed into logistic finding-usefulness vs a 9-company
fraud detector. The user had to invent "event X has a,b,c; event Q has
a,d,e; then a should weigh more" — which was already `fraud_learning`
prevalence, blocked by exact-string match. The reinvention is the cost: the
named scorer was there the whole time and the audit had already said why it
was blocked.