[skills-eval] dotnet-nuget: 1 skill, 100% pass — 1 P1

## Context — cross-family skill evaluation: `dotnet-nuget`

This issue is **self-contained**: it captures everything a skill author needs to act on the `dotnet-nuget` plugin without opening the full report.

**What this measures.** Every runnable skill in `dotnet/skills` was run through [Vally](https://github.com/microsoft/evaluate) (0.7) on a **cross-family matrix**: 5 executor model families — `opus-4.8`, `gpt-5.5`, `sonnet-4.6`, `haiku-4.5`, `mai-flash` — each judged by a **different family** (judge ≠ executor; default judge = latest Opus, or GPT when Opus is the executor). For every skill, a **skilled** run is compared against a **baseline** (no-skill) run and scored per executor. This removes single-model and self-judging bias, so a skill that only helps one model family — or only its own family's judge — is visible.

- **Data source:** cross-family CI grid (run `29228914412` + backfills) — 5 executors × 85 runnable skills, **419 scored cells** over 84 skills.
- **Row grain:** one row per **skill**, aggregated across its (up to 5) executor cells. `avgN` is the mean trial count behind the cells (trials 1–17; higher = more statistically trustworthy). `thin-N` flags directional-only rows.

### `dotnet-nuget` at a glance (portfolio scorecard)

| Plugin | Skills | Cells | Pass | Impact | Tie-trials | Err | avg ΔTok | avg ΔTurns | avg ΔTools | Headline |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- |
| **dotnet-nuget** | 1 | 5 | 100% | 0.875 | 0 | 0 | +253,199 | +7.58 | +9.32 | Best outcome, worst cost — trim it |

**Insights.** 1 skill(s); mean impact **0.88**; 1/1 help ≥1 frontier model. No frontier-validated exemplar.

**Address first:** `convert-to-cpm` — cost.

### How to read the table

Each skill is scored **skilled vs. baseline** on these axes:

| Signal | Column | What it means | Good |
| --- | --- | --- | --- |
| Breadth | `Families ✓` (n/5) | How many of the **5 model families** the skill helps (per-family pass count, max `5/5`) | 4–5 / 5 |
| Where | `Passed on` | *Which* families passed. **Frontier = latest Opus + latest GPT** are **bold**; Sonnet 4.6 / Haiku 4.5 / MAI Flash are mid/low-weight | frontier ✓ |
| Magnitude | `Impact` (−1…+1) | How strongly the judge prefers *skilled* over *baseline* | ≥ 0.4 |
| Decisiveness | `Ties▵` | Trials where the judge saw **no difference** → skill is *inert* | low |
| Safety | `Loss▵` | Trials where **skilled was WORSE** than baseline → skill misfires | ~0 |
| Reliability | `Err` | Trials that errored/crashed in setup or judging | 0 |
| Efficiency | `ΔTok` / `ΔTurns` / `ΔTools` | Extra tokens / agent turns / tool calls vs baseline | ≤ 0 |
| Confidence | `avgN` | Mean trials behind the verdict; low N = directional only | ≥ 3 |
| Invocation | `Call%` | Share of skilled trials where the model actually **invoked the skill** | ~100% |

> **`Families ✓` is cell-level (max `5/5`); `Ties▵`/`Loss▵` are trial-level tallies summed across *all* families** (including the ones where the skill failed). A high `Families ✓` next to non-zero `Loss▵` is not a contradiction — see `Passed on` and the Action text for where losses landed.

**Action buckets** (each skill has one primary action; `[flags]` note secondary concerns):

| Bucket | Priority | Meaning |
| --- | --- | --- |
| FIX-RELIABILITY | 🔴 P0 | Errored trials / no verdict — stabilize the harness before trusting the score |
| FIX-DISCOVERY | 🔴 P0 | Model doesn't invoke it (`Call% < 50%`) — a triggering/description problem |
| FIX-REGRESSION | 🔴 P0 | Skilled is worse than baseline on many trials — the skill misfires |
| ADD-DECISIVENESS | 🟠 P1 | Called ~100% but ties dominate, ~0 impact — inert; needs sharper behavioral steps |
| TRIM-COST | 🟠 P1 | Passes but with heavy token/turn overhead — trim verbosity |
| EXEMPLAR | 🟢 keep | Broad, strong, reliable win — use as a template |
| EFFICIENT-WIN | 🟢 protect | Wins *and* cuts turns/tools — the ideal shape |
| KEEP-POLISH | 🟢 | Solid majority win; minor polish + more trials |
| STRENGTHEN | 🟡 P2 | Marginal/mixed lift — sharpen triggers & success criteria |

### Per-skill actions

| Skill | Families ✓ | Passed on (frontier **bold**) | Impact | Ties▵ | Loss▵ | Err | avgN | Call% | ΔTok | ΔTurns | ΔTools | Action |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| `convert-to-cpm` | 5/5 | **Opus**, **GPT**, Sonnet, Haiku, MAI | 0.88 | 0 | 1 | 0 | 8 | 100% | +253,199 | +7.58 | +9.32 | **TRIM-COST** · 🟠 **Passes but heavy (+253,199 tok).** Value is real; cost isn't justified. Trim: (1) cut redundant/whole-file reads — point to specific sections; (2) replace "explore everything" with a targeted checklist; (3) move deep reference material behind links instead of inlining it. *([costly])* |

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_{Generated from the cross-family Call-to-Action report (`CALL-TO-ACTION.md` §4–§5; companion `IMPACT-ANALYSIS.md`). Regenerate the underlying tables with `node deep-metrics.mjs "$env:TEMP\cf-ci" agg-ci` → `node gen-cta-tables.mjs agg-ci`. Numbers are directional where `avgN` is low; treat single-trial cells as hypotheses to confirm with more runs.}