git-developer-performance

name	git-developer-performance
version	1.0.0
description	[Git] Use when generating developer KPI, performance, contribution value, story point, man-day, or code-quality reports from local git commit history.

Quick Summary

Goal: Plan and generate a developer KPI-style quality-work report from local git history only. Workflow:

1. Set Goal + Plan - Declare the goal, trigger $plan, and create tasks per contributor.
2. Collect Packets - Run scripts/git-developer-performance.cjs to create commit inventory and work packets.
3. Analyze Work - Read patches per contributor; estimate value, story points, man-days, and quality impact.
4. Synthesize Report - Write quality-work-summary.md and evidence-proof.md outside .claude. Key Rules:

• Use only local git history. Do not query external services.
• Consolidate people by identity map, then normalized email, then high-confidence aliases such as DOMAIN\first.lastpart matching a full name; use --identity-map for exceptions.
• This is a large task: plan first, then create one todo task per contributor.
• The script collects evidence; AI must read changes and synthesize contributed value.
• Treat KPI values as evidence-based estimates, not a complete HR assessment.
• Report both man_days_traditional (no AI) and man_days_ai (AI coding assistant with project context).
• Traverse full merged branch history (not only first-parent) and attribute shared feature-branch implementation to each developer's own direct commits; merge authors get integration/admin signal unless conflict-resolution changes are explicitly inspected.
• Estimate implementation SP from direct authored diffs first; zero-change merge/admin commits are integration signal only.
• Discount generated files, migration designers, docs/spec output, i18n sorting, lockfiles, and repeated follow-up churn.
• For velocity mismatch or recheck requests, synthesize each contributor's direct authored work as one "giant commit" first, then split into atomic 1/2/3/5/8/13 SP clusters.
• Persist large rechecks to a report file outside .claude before finalizing, so context loss cannot erase evidence.
• Separate product/domain delivery, infrastructure/tooling work, docs/generated churn, and merge/admin integration; do not mix them silently into one velocity number.
• Run a velocity sanity check: both man-day ranges must be plausible for active days and the selected period.
• Keep output outside .claude; default root is reports/developer-performance/.

Git Developer Performance

Use when the user asks for developer KPI/performance, productivity, contribution value, story-point estimates, man-day estimates, quality impact, or quality-work reporting from git commits.

Required AI Workflow

Before analysis, set or declare this goal:

Plan and generate a developer performance quality-work report from local git history, then execute the plan and produce the report. Then trigger $plan or create equivalent plan artifacts. This skill is not a commit-list export. It requires reading direct commits and merge/admin commits per contributor, then synthesizing value. Use ultrathink/deep analysis for final synthesis when contributor count or churn is high.

Command

node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs [options]

Options: --branch <ref> defaults to develop then main; --days <n> defaults to 60; --since <date> overrides days; --until <date> defaults now; --out <dir> defaults to reports/developer-performance; --identity-map <csv> accepts identity,email,displayName,id; --json prints machine-readable result.

Examples:

node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs
node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs --branch release/1.4 --days 30
node .claude/skills/git-developer-performance/scripts/git-developer-performance.cjs --since 2026-01-01 --until 2026-03-31 --out reports/dev-performance-q1

Output

Creates a timestamped run folder containing:

• summary.md - team evidence report, authored signal sort, warnings, and integration/admin activity.
• analysis-plan.md - AI execution plan with one task per contributor.
• work-packets/*.md - per-contributor commit/change packets for qualitative analysis.
• quality-work-summary.md and evidence-proof.md - AI-written value synthesis and proof appendix.
• analysis/ - target folder for AI-written per-contributor synthesis.
• contributors.csv, commits.csv, developers/*.md, data/*.json - source evidence and deterministic aggregates.

Analysis Rules

• Read references/analysis-workflow.md before final synthesis.
• Treat contributors as people consolidated by identity map/email/high-confidence aliases, not raw display names.
• Count distinct contributors, then create one todo task per contributor from analysis-plan.md.
• For each contributor, inspect direct authored commits and merge/admin commits from work-packets/*.md.
• Use git show --stat --find-renames <hash> and targeted patches for high-impact commits.
• When several developers contribute to one feature branch, analyze each contributor's direct commits separately and never give the whole feature's implementation SP to the merge author or PR owner.
• Estimate work clusters with 1/2/3/5/8/13 story points, no-AI man-days, and AI-assisted man-days; state confidence.
• If a displayed theme is more than 13 SP, state that it is a sum of smaller atomic clusters, not one unsplit story.
• Do not add implementation SP for zero-file merge/admin commits; mention them separately as integration/admin signal.
• Discount non-implementation churn before estimating: generated code, EF designer snapshots, docs/specs, i18n sorting, lockfiles, and repeated follow-ups.
• Reconcile final SP/man-day totals against authored active days and team velocity intuition; if implausible, re-audit before delivery.
• Analyze contributed value: features/changes, bug fixes, refactors, tests/docs, integration/admin, and code quality.
• If there are many contributors, split contributor tasks across subagents with disjoint developer lists.
• Review identity and bulk-change warnings before comparing contributors.
• State that report quality depends on local git data quality when history is incomplete, stale, squashed, or bot/shared authors exist.

Verification

Before delivering a generated report:

1. Run node --test .claude/skills/git-developer-performance/tests/*.test.cjs.
2. Run the command for the requested repo/range.
3. Confirm the output path is outside .claude.

AI Mistake Prevention — Failure modes to avoid on every task:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

Closing Reminders

IMPORTANT MUST ATTENTION Goal: Plan and generate a developer KPI-style quality-work report from local git history only.

Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• Critical Thinking: trace every KPI/value claim; confidence >80% to act.
• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.

MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

IMPORTANT MUST ATTENTION use local git history only. IMPORTANT MUST ATTENTION trigger planning before qualitative analysis; this is a large task. IMPORTANT MUST ATTENTION default to develop, fallback to main, and use last 60 days when the user does not specify. IMPORTANT MUST ATTENTION do not present authored or integration signals as complete measures of human performance. IMPORTANT MUST ATTENTION shared feature-branch implementation credit follows direct commit authors, not merge authors; never let raw churn or zero-change merge/admin commits inflate implementation SP or man-day estimates. IMPORTANT MUST ATTENTION never publish a single ambiguous MD number; show no-AI and AI-assisted MD separately.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.