AI Coding Metrics

Measures coding assistants and coding agents without collapsing results into vanity metrics or one blended score.

The critical distinction is mode: assistants help inline or in chat; agents execute multi-step work and need task-level measurement. Do not measure them as if they were the same thing.

When to Use This Skill

Trigger	Example
Designing a pilot or rollout scorecard	"We're rolling out Copilot to 200 engineers — what do we measure?"
Diagnosing usage-up / outcomes-flat	"Seat utilization is 80% but PR throughput is unchanged"
Comparing assistant vs. agent workflows	"Should we instrument these separately?"
Building an ROI model or leadership report	"Finance wants a renewal decision by Q3"
Designing an experiment better than vendor benchmarks	"We can't trust the vendor's numbers — how do we run our own study?"

Defaults

Rule	Rationale
Start from the decision, not the telemetry available	Prevents instrument-what-is-easy bias
Separate assistant and agent funnels	Mixing hides which workflow drives results
Pair every speed metric with quality + experience	Speed alone is misleading
Aggregate at team level	Individual dashboards become surveillance
Treat benchmarks as capability signals, not business KPIs	Benchmark gaps do not equal production gaps

Workflow

1. Define the decision.
2. Pick the program mode: assistant, agent, or mixed.
3. Build the minimum viable scorecard.
4. Choose the study design.
5. Produce one deliverable.

ASCII Flow

AI coding metrics request
  -> decision to support: buy, renew, improve, prove, or diagnose
  -> split mode: assistant, agent, or mixed
  -> select scorecard families: adoption, delivery, quality, economics, experience
  -> choose study design and baseline window
  -> collect team-level and task-level evidence
  -> report confidence, sample size, and confounds
  -> deliver ROI model, dashboard, experiment plan, or executive report

Quick Reference

Decision to Deliverable Map

Decision	Default Output
buy, renew, or cut a tool	ROI model plus executive report
improve adoption	adoption metrics plus survey
prove delivery impact	productivity metrics plus experiment plan
check quality drift	quality metrics plus dashboard
understand trust or friction	developer-experience metrics plus survey
evaluate coding agents	agent-execution metrics plus experiment plan

Program Modes

Mode	Unit of Analysis	Primary Emphasis
assistant	developer-day, team-week, repo-month	adoption, delivery, quality, experience
agent	task, PR, workflow run	task success, merge, revert, review burden, cost per accepted change
mixed	team-week plus task-level samples	separate the two funnels before combining results

Metric Families

Use the smallest scorecard that can answer the decision:

Family	What It Tells You
adoption	whether usage is real and sustained
delivery	whether software flow is faster where AI actually touches the path
quality	whether speed gains are offset by defects, rework, review burden, or declining extension robustness
economics	whether the value justifies tool and operating cost
experience	whether developers trust the tool and want to keep using it
agent execution	whether autonomous workflows succeed in production, not just in demos

Study Design Defaults

Minimum baseline: 8 weeks of pre-intervention data. Two-week baselines produce noisy causal inference — week-to-week variance in PR throughput, review lag, and defect escape routinely exceeds the signal size of AI tooling effects.

Situation	Design
new pilot, no control group	before/after with ≥8 weeks baseline
enough comparable teams	matched A/B or stratified assignment
teams resist permanent denial of tools	crossover design
agent workflow change on one task family	task-level shadow comparison or reviewer-blind evaluation
leadership wants a fast answer	balanced scorecard with explicit caveats, not a causal claim

Measurement Checklist

Use before publishing any AI coding report:

• Baseline established (≥8 weeks before intervention)
• Assistant and agent funnels tracked separately
• Every speed metric paired with at least one quality metric
• Sample size, confidence level, and study design stated
• Confounds documented (team changes, release pressure, policy changes)
• Vendor evidence labeled as vendor evidence
• Usage measured after stabilization (not week-1 novelty period)
• Review burden and rework cost included in ROI model
• Edit-capable agents measured across evolving-spec checkpoints, including late-checkpoint cost and quality slopes
• Aggregated at team level (no manager-visible individual dashboards)

Current Evidence Posture (as of 2026-08-21)

Claim	Evidence	Caveat
AI amplifies existing strengths and weaknesses	DORA 2025 AI report; conditional-impact model confirmed	Not a universal accelerant
Experienced developers ~19% slower with early-2025 tools (RCT)	METR July 2025 RCT, realistic open-source tasks	Specific to early-2025 tooling generation
METR believes developers more sped-up in 2026 than 2025	METR Feb 2026 update	30-50% of participants declined no-AI tasks (selection bias); unreliable signal
Self-reported: median 1.4-2x value of work from AI (2026)	METR May 2026 survey, n=349	Self-report; METR found 40pp gap between perceived and actual gains in 2025 study
Throughput +66%, PR review time +441%, incidents per PR +243%	Faros AI 2026 telemetry, 22k devs / 4k teams	Organizational telemetry, not RCT; PRs merged without review up +31%
DORA 2025: 90% of developers use AI daily	DORA 2025 AI report	Adoption does not equal delivery impact
Modeled first-year AI ROI ~39% (500-person org); adoption raises change-failure rate (5%->6%), an "instability tax"	DORA 2026 ROI of AI-Assisted Software Development report (Apr 2026)	Vendor-modeled scenario, not a cross-org RCT; treat the 39% figure as an illustrative scenario, not a universal benchmark
AI yields 35-40% gains on simple tasks but ~10% on complex legacy code	DORA 2026 ROI report	Reinforces task-complexity segmentation already required by this skill's study design defaults
DX Core 4 unifies DORA + SPACE + DevEx into 4 dimensions (Speed, Effectiveness, Quality, Business Impact)	DX Core 4, formalized publicly Apr 2026	Vendor framework; specific benchmarks need independent replication
One-shot pass rates can miss degradation across repeated agent edits	SlopCodeBench v1, Mar 2026 preprint	Python experiments only; trajectory signals are not correctness proofs or universal targets

Anti-Gaming Checklist

Reject a scorecard or report if any of the following apply:

• Single blended AI productivity score mixing usage, speed, sentiment, and quality
• Seat activation or prompt volume cited as delivery impact
• Cross-team comparison without controlling for stack, task mix, staffing, or release pressure
• Measurement period is <8 weeks or includes week-1 novelty window
• Vendor benchmark cited as production ROI evidence
• Review burden excluded from ROI model
• Individual-level AI usage visible to managers
• Directional before/after movement stated as causal without controlled design
• SlopCodeBench averages or trajectory signals used as organizational targets or causal ROI evidence

Navigation

References

• references/adoption-metrics.md — assistant and agent adoption funnels, metric definitions, stall patterns, privacy rules
• references/productivity-metrics.md — DORA and SPACE applied to AI workflows, delivery stack decomposition, confound management
• references/quality-metrics.md — defect, complexity, test, security, and technical debt metrics with targets and alert thresholds
• references/roi-framework.md — full cost model (including review burden), benefit model, scenario planning, executive report structure
• references/developer-experience-metrics.md — satisfaction surveys, cognitive load, friction indicators, trust calibration, DX anti-patterns
• references/agent-execution-metrics.md — agent funnel, core metrics, reviewer burden, scorecards for pilot / scaling / executive decisions
• references/benchmarking-methodology.md — A/B, before/after, crossover, shadow designs; statistical rigor; confound management
• references/theory-of-constraints-applied.md — bottleneck identification before instrumenting, throughput accounting for ROI, DBR for review-queue protection, CRT for stalled rollouts, evaporating cloud for adoption-vs-quality tensions
• references/evidence-update.md — load when citing current research: METR RCT (2025 baseline), METR 2026 update (selection-bias caveat), DORA 2025 AI report, DX Core 4, Faros 2026 telemetry

Assets and data

• assets/metric-dashboard-template.md
• assets/adoption-survey-template.md
• assets/roi-calculator-template.md
• assets/executive-report-template.md
• assets/experiment-design-template.md
• data/sources.json
• data/sample-ai-metrics.json

Scripts

• scripts/roi_calculator.py
• scripts/README.md

Cross-References

• ../dev-context-engineering/SKILL.md
• ../ai-agents/SKILL.md
• ../qa-observability/SKILL.md
• ../product-management/SKILL.md

Fact-Checking

• Known bugs, regressions, framework/compiler/runtime footguns, and version-specific crash or workaround guidance must be verified against current primary web sources before being treated as current fact.
• Verify current research claims, benchmark status, and vendor telemetry specifics before final advice.
• Prefer peer-reviewed, official, and first-party telemetry docs over social or vendor marketing claims.
• If live verification is unavailable, mark current-evidence claims as unverified.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.