457
merged PRs
in the last 90 days
LeadCognition · live delivery proof
Proof that I can make a small team ship faster.
This is the operating model behind LeadCognition: a real product, a small team, measurable delivery, and an AI-native workflow that can be installed inside another engineering org.
Framework-based evaluation
The point is not “many commits.” The point is efficient delivery: fast feedback loops, low waiting time, small reviewable changes, healthy deploy cadence, and less rework.
DORA SPACE / DevEx Linear flow PR review flow Quality / rework Business delivery
440
production deploys
recorded in GitHub deployments
1,344
mainline commits
on origin/main
210
release tags
semver releases
7 min
median PR cycle
open to merged
3 min
median CI loop
typecheck/build/smoke
Measurement window: 2026-02-21 through 2026-05-22. Sources: local LeadCognition git history, GitHub PRs, GitHub deployments, release tags, and CI workflow runs.
Measurement framework
I prove efficiency with frameworks, not raw activity.
The same audit can run against your repos and planning flow. I combine DORA, SPACE, DevEx, Linear state history, PR review flow, CI feedback, deploy cadence, and rework signals so the consulting starts from evidence.
merged PRs
deploy frequency
PR cycle time
CI health
release hygiene
Evidence model
consulting audit
Signals I evaluate
DORA: lead time, deploy cadence, failure recovery
SPACE/DevEx: focus time, friction, review loops, wait states
Linear: idea → scope → started → review → shipped
GitHub: PR size, review pickup, merge flow, release hygiene
CI/CD: feedback speed, flaky checks, preview confidence
Quality: rework, reopened work, hotfixes, escaped defectsThis is why the numbers are credible: they sit inside a broader operating model for flow, quality, developer experience, and business outcomes.
The proof
Not activity. A working software factory.
This is not a toy portfolio project. LeadCognition is a live AI SaaS product with production deploys, billing, auth, analytics, observability, and customer-facing workflows.
The workflow does not depend on one magic tool. It combines clear issue scope, small PRs, preview environments, automated checks, agent handoffs, and human review where judgment matters.
The bottleneck is no longer typing code. The bottleneck becomes choosing the right work, validating quality, keeping parallel agent work coordinated, and preventing rework.
90-day delivery trace
live-system evidence $
gh pr list --merged --since 90d
457 merged PRs
$
gh api deployments --environment production
440 production deployments
$
git log origin/main --since 90d
1,344 commits across 57 active shipping days
$
pnpm mission:status
Mission Control tracks PR readiness, checks, QA evidence, and
release blockers
The operating system
Speed is repeatable only when the system is real.
The proof is not a burst of output. It is the operating model behind the output: product judgment, small shippable work, automated checks, Linear discipline, AI-agent handoffs, and evidence before claims.
Product
LeadCognition turns public GitHub activity into account intelligence, buyer context, and developer-led GTM signal.
GitHub signals ICP matching Lead intelligence Customer UX
Engineering
A production SaaS stack with auth, billing, orgs, APIs, data pipelines, observability, and deploy automation.
Bun/Hono Postgres Cloudflare BigQuery
Operating system
Linear, GitHub, preview deploys, review gates, Mission Control, and agent worktrees turn ideas into shippable work.
Linear GitHub PRs Preview gates Release automation
AI-native workflow
Codex, Claude Code, MCP tools, repo skills, hooks, and evidence docs compress implementation and validation loops.
Agents MCP Skills Evidence reports
Linear layer
GitHub shows what shipped. Linear shows whether the right work moved.
I use Linear as the business-flow layer: initiatives, projects, cycles, priorities, estimates, labels, status changes, comments, and linked PRs. That turns delivery measurement into a real operating question: what was scoped, what started, what shipped, what stalled, and what came back as rework.
Flow efficiency
idea -> scoped -> started -> PR -> done
Separates active work from waiting time so teams see where delivery actually stalls.
Kanban / CFD
WIP, aging, throughput, blocked time
Shows whether the team is finishing work or just carrying too many half-open missions.
DORA mapping
issue -> PR -> deploy -> incident
Connects product work to production outcomes instead of measuring GitHub activity alone.
SPACE / DevEx
interruptions, handoffs, review loops
Captures the human friction that commit counts and PR dashboards miss.
Rework / quality
reopens, bouncebacks, hotfixes
Flags work that looked done but returned through bugs, unclear scope, or missing validation.
Investment mix
features, fixes, debt, experiments
Shows whether velocity is creating customer value or only consuming itself in maintenance.
What I install
This is not a personal trick. It is an org capability.
The same system can be adapted for a startup, SaaS team, agency, or DevTool company: measure the flow, wire the tools, codify the agent rules, and make the team faster without turning developers into dashboards.
01
Measure the current system
I baseline cycle time, PR flow, CI, deploy frequency, review delay, rework, and developer friction using your real GitHub, Linear, CI, and workflow data.
02
Install the AI-native workflow
I set up agent rules, repo skills, MCP access, review gates, worktree conventions, preview checks, and a shared way for the team to run AI safely.
03
Turn it into an operating cadence
The team gets weekly metrics, evidence-based releases, cleanup routines, and a repeatable way to decide what agents should build next.
Developer efficiency consulting
Want this kind of execution system inside your org?
I can audit your current delivery flow, install the AI-native workflow, and leave your team with measurable operating habits instead of another pile of tool subscriptions.