Documentation, planning, context management, and communication systems for teams building with AI tools.
9 workflows online
contextonline
Brain
Project memory, retrieval, and session hygiene for AI development work.
Use Brain when repo knowledge needs to survive beyond one chat: architecture notes, workflow rules, implementation decisions, risks, and focused context packets.
Commands
brain prep --task "..."brain search "slopstack ..."brain session run -- npm run lint
AGENTS.md plus focused docs for reusable project instructions.
A lightweight documentation pattern for AI coding tools: keep the root contract small, then route agents to architecture, content, UI, and task guides only when needed.
A bounded execution loop for agent changes that isolates work, checkpoints progress, gates risky actions, and publishes only verified artifacts.
Use this loop when an agent can edit files, run commands, call external tools, or work across long-running sessions. It separates the control plane from disposable compute, keeps credentials outside the workspace, and makes approval plus verification explicit before anything leaves the sandbox.
Commands
Create one isolated workspace per taskPersist checkpoints outside the sandboxRequire approval before external writesRun project checks before publishing artifacts
A conversation-to-PR workflow that turns shared decisions into bounded agent work with explicit targets, provenance, validation, and review.
Use this workflow when a team wants to start or steer an agent from Slack, Teams, or another shared discussion. It filters the thread into a durable brief, separates participation from write authority, pins the repository and branch, and carries the work through an independently reviewed pull request.
Commands
Name the repository, base branch, issue, and write-authorized ownerSummarize the thread into goals, non-goals, decisions, and acceptance criteriaStart one shared agent session for one taskVerify the diff, checks, provenance, and required approvals
Best for
Turning product or engineering discussions into implementation work
Shared incident follow-ups that need a traceable code change
Teams that want visible agent steering without weakening merge controls
A privacy-aware telemetry loop that connects agent traces and adoption metrics to reviewed outcomes.
Use this loop to instrument production agents without treating transcripts as observability. It defines stable identities, traces workflow and tool boundaries, limits payload capture, emits low-cardinality metrics, and joins each run to evidence such as review, merge, deployment, or rollback status.
Commands
Assign stable agent, workflow, run, task, and trace IDsTrace agent, model, plan, retrieval, and tool operationsRedact sensitive payloads before telemetry exportJoin each run to review, deployment, or business outcomes
A review loop that matches scrutiny to change risk, turns findings into testable claims, and rechecks the final commit before merge.
Use this workflow for agent-authored or AI-reviewed changes that need more than a generic diff pass. It establishes a risk contract, bounds review context, separates blocking findings from suggestions, records resolution evidence, and keeps final acceptance with an independent owner.
Commands
Classify the change by impact, sensitivity, and system boundariesChoose review depth and required independent ownersAdjudicate every finding with evidence and a resolution reasonRerun checks and review the final head commit before merge
A deadline-aware workflow for finding model dependencies, proving replacement behavior, and removing retired endpoints without service interruption.
Use this workflow when a provider or AI platform deprecates a model, alias, or request parameter. It turns the notice into an owned inventory, tests the replacement against an explicit behavior contract, stages a reversible rollout, verifies that retired traffic reaches zero, and removes the old path before shutdown.
Commands
Inventory model IDs, aliases, policies, SDK defaults, and hidden fallbacksRecord the retirement deadline, replacement, owner, and failure modeTest the replacement against behavior, safety, latency, and cost gatesCanary the change with observability and a time-bounded rollback pathProve zero retired-model traffic, then remove compatibility code
Best for
Applications pinned to provider model IDs or preview endpoints
Enterprise coding assistants governed by model-access policies