[skills-eval] dotnet-aspnetcore: 2 skills, 20% pass — 2 to strengthen

[AbhitejJohn]

Context — cross-family skill evaluation: dotnet-aspnetcore

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

What this measures. Every runnable skill in dotnet/skills was run through Vally (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-aspnetcore at a glance (portfolio scorecard)

Plugin	Skills	Cells	Pass	Impact	Tie-trials	Err	avg ΔTok	avg ΔTurns	avg ΔTools	Headline
dotnet-aspnetcore	2	10	20%	0.267	3	0	+6,372	+0.37	+0.30	Weak lift, both skills marginal

Insights. 2 skill(s); mean impact 0.27; 1/2 help ≥1 frontier model. No frontier-validated exemplar. Weakest: configuring-opentelemetry-dotnet (0.19).

Address first: nothing critical — polish the STRENGTHEN rows below.

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
minimal-api-file-upload	1/5	Sonnet	0.35	1	2	0	3	100%	-630	-0.13	-0.27	STRENGTHEN · 🟡 Marginal/mixed (passes 1/5 — Sonnet). Diagnosis: misses both frontier models — likely assumes context they solve unaided. Try: (1) sharpen the trigger so it fires only where it wins; (2) add 1–2 opinionated, concrete steps that change behaviour; (3) add trials to separate signal from noise. If frontier models never benefit, scope it explicitly to weaker models or reconsider its value. ([both-frontier-miss])
configuring-opentelemetry-dotnet	1/5	GPT	0.19	2	3	0	3	87%	+13,374	+0.87	+0.87	STRENGTHEN · 🟡 Marginal/mixed (passes 1/5 — GPT). Diagnosis: model-dependent — helps some families, not others. Try: (1) sharpen the trigger so it fires only where it wins; (2) add 1–2 opinionated, concrete steps that change behaviour; (3) add trials to separate signal from noise. ([frontier-miss])

⚠️ Coverage gap — 2 skills excluded from the cross-family run

The matrix scores a skill only if it has a tests/dotnet-aspnetcore/<skill>/eval.vally.yaml. The following dotnet-aspnetcore skills ship a SKILL.md but were never measured — so they carry zero evidence of impact (not counted in the pass rate or scorecard above):

• Has a base eval.yaml but no eval.vally.yaml — add a cross-family Vally config to fold them into the matrix: convert-blazor-server-to-webapp, dotnet-webapi

Until these have an eval.vally.yaml, we cannot tell whether they help, hurt, or are inert. Recommended: add the cross-family eval configs and re-run so they appear in the next scorecard.

---

_{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.}

[github-actions]

🎯 Issue Triage

Summary: This is an auto-generated skill evaluation report for the dotnet-aspnetcore plugin, covering 2 skills evaluated across a 5-model cross-family matrix. Both skills (minimal-api-file-upload and configuring-opentelemetry-dotnet) scored marginal lift (20% pass rate, avg impact 0.27) and need strengthening. Additionally, 2 skills lack eval.vally.yaml coverage gaps.

Type: task — a work item tracking evaluation results with concrete action items per skill.

Assigned owners: @webreidi and @AbhitejJohn assigned directly. The primary codeowners for this area — @dotnet/aspnet — could not be assigned as they are a team; they should be notified directly.

Confidence: High — issue clearly maps to area-aspnet / dotnet-aspnetcore, type is unambiguously a task, and CODEOWNERS path /plugins/dotnet-aspnetcore/ maps cleanly to @dotnet/aspnet.

Similar issues: No duplicate open issues found.

Triage details

• Area mapping: /plugins/dotnet-aspnetcore/ → @dotnet/aspnet per CODEOWNERS
• CODEOWNERS path match: /plugins/dotnet-aspnetcore/ and /tests/dotnet-aspnetcore/
• Existing labels: area-aspnet (kept) and skill (kept) — both remain accurate
• Action items in issue: STRENGTHEN both measured skills; add eval.vally.yaml for convert-blazor-server-to-webapp and dotnet-webapi
• Confidence rationale: High — single area, obvious task type, clear owners

Generated by Issue Triage for issue #890 · 70.8 AIC · ⊞ 26.8K · ◷

[AbhitejJohn]

Verifying fixes for this plugin's skills

To re-run the cross-family evaluation and confirm a change moves the pass rate, cherry-pick the dispatch workflow from #920 and dispatch it scoped to this plugin:

git fetch origin abhitejjohn-cross-family-eval-workflow
git cherry-pick 7c97b7346          # single commit in #920
git push origin HEAD

gh workflow run evaluation-run.yml --ref <your-branch> \
  -f plugins="dotnet-aspnetcore" \
  -f executors="opus,gpt,sonnet46,haiku,mai" -f runs=3

To verify a single skill faster, scope with skills (the skill directory name, e.g. one of the skills listed above):

gh workflow run evaluation-run.yml --ref <your-branch> \
  -f skills="<skill-dir>" -f executors="opus,gpt" -f runs=3

Each skill is run skilled-vs-baseline across the 5-family executor matrix, with the judge never in the executor's family (default judge = latest Opus; GPT when Opus executes). Results assemble into a cross-family pass matrix you can compare against the numbers in this issue. Full inputs/usage: #920.