
More autonomy and parallelism without skipping control, isolation or the review queue.
MODULE 5 // TEAMWORK
Teamwork in 10x Workflow: five lessons on an agent working for the team - the smallest helper instead of a platform, an agent built from SDK primitives, review on every PR, a registry of shared skills and async delegation. The module's second thesis: AI artifacts are code - one source of truth, versions, distribution. This module closes the 10xChampion block: one of two projects is enough, and screenshots are the proof.
TEAM CONSTELLATION // CLICK A NODE TO FLY TO A SECTION
Module recap · five lessons in 30 seconds
What to build and what to buy? The smallest tool that closes a real gap.
LESSON 02What is an agent made of once you strip away the IDE? Assembled deliberately, with a cost meter.
LESSON 03Who watches every PR while you sleep? Review on every PR, not just when you have time.
LESSON 04Where does the team get shared skills? One version of truth instead of drifting copies.
LESSON 05How do you delegate work at a distance? Boundaries before launch, control returns in review.
An agent starts earning for the team only once it runs without you.
"Out in the Belt nobody waits for a shipment from the inner planets. You take what is at hand and seal the breach before the air runs out."
- a Belter mechanic, at the airlockBelters do not wait for a shipment from the inner planets - they take what is at hand and seal the breach before the air runs out. Internal team tools grow out of the same logic: wherever SaaS carries platform-grade responsibility - auth, billing, compliance - you stay with SaaS. You build only around the team's local gap that no off-the-shelf product knows: data visualization instead of another round of questions to the analysts.
Internal team tools follow one simple rule: wherever SaaS carries platform-grade responsibility - auth, billing, compliance - you stay with SaaS. You build only around the team's local gap that no off-the-shelf product knows.
Input: a real gap in the team's flow. Output: the smallest tool that closes it - and a build vs buy decision made deliberately, not out of momentum. Mechanism: before the first line of code, you ask one question - who takes the responsibility when the tool fails; if the platform does, you buy; if the gap is yours and yours alone, you build the smallest thing that works.
whenever someone says "let's build ourselves a tool" - build only around a local gap, buy wherever SaaS carries platform-grade responsibility.
Before the team builds a tool out of momentum, the gap goes on paper: the PRD defines the problem, the users and the boundaries - and only on that basis do you decide whether to build at all.
Input: shape-notes.md from the discovery stage (or raw notes). Output: context/foundation/prd.md conforming to a locked schema - 10 sections for a greenfield project, 11 for a change in an existing system.
A 0-4 heuristic scores whether the notes are shaped: a checkpoint block, requirements in FR-NNN format, Given / When / Then scenarios, an explicit business rule. Below the threshold the skill warns outright: what is missing and what it costs the document. Generation happens in memory, with schema validation before anything lands on disk.
For a change in an existing system: a review of the current state, problem and motivation, personas, success criteria with "what must not break" guardrails, stories, functional and non-functional requirements, Non-Goals and Open Questions.
The skill is a document generator, not a discovery facilitator: it never invents domain decisions, business rules or success criteria. Every gap goes verbatim into Open Questions - a human resolves it, not the generator.
"You do not get a ready-made soldier. You pick him, assemble him from what he has, and measure him in every battle - trust comes after results."
- a training army commander, before the first battleEnder does not get a ready-made army - he gets recruits and builds a team out of them: he picks, positions and measures in every battle before he trusts anyone. The first team agent is built the same way: you go one level down, below the IDE, to the SDK primitives - model, tools, loop, context - and assemble from them a new team member whose behavior you understand, because you put it together yourself.
The first team agent is built one level down, below the IDE: from SDK primitives - model, tools, loop, context - you assemble a new team member whose behavior you understand, because you put it together yourself.
Input: the primitives of an agent SDK. Output: an agent assembled deliberately, with a cost meter and metrics from day one. Mechanism: the project's first fork - ready-made SDK vs assemble-it-yourself - settles how much control you need; and you measure costs from the start, because an agent without a meter is a team member whose salary nobody knows.
before the team trusts the agent - from primitives, deliberately, with a cost meter from day one.
"We probed the ocean for years before we understood that it was probing us. Every study leaves a trace in the studied - and in the one who studies."
- station log, Solaris research stationThe ocean of Solaris cannot be studied from the outside - it responds to observation and sends back to the researchers what they themselves brought in: the observer changes the observed system. Code review in the AI era runs on the same physics: an agent as a pipeline step, triggered without you on every PR - including the one submitted at three in the morning - is observation that shapes both the code and the team, because an author writes differently knowing the review will always come.
Code review in the AI era is an agent as a pipeline step, triggered without you on every PR - including the one submitted at three in the morning, including the one you would not have had the energy to read.
Input: a PR in the team's repository. Output: a CI pipeline in which the agent leaves a comment on every PR - review stops depending on your calendar. Mechanism: /10x-impl-review-ci on GitHub Actions scores changes by severity × impact, and triage separates findings worth fixing from noise - a reviewer who reports everything is just as useless as one who reports nothing.
the module's thesis - an agent earns its keep only when it works without you: review on every PR, not when you happen to have the energy.
Review on every PR with no human in the loop: an agent in CI compares the diff against the plan the PR declares it implements, and leaves a durable audit trail - a report, inline comments, a verdict.
Input: a PR plus a plan - found by the context/changes/<id>/plan.md convention or a "Plan:" line in the PR description. Output: a reviews/impl-review.md report committed to the PR branch, inline comments on the changed lines and a summary comment with a table of dimensions.
Diff from the merge-base → three subagents in parallel: drift against the plan (MATCH / DRIFT / MISSING / EXTRA), safety and conformance with neighboring patterns, test coverage → verification commands from the plan → scores across seven dimensions → an APPROVED / NEEDS ATTENTION / REJECTED verdict.
Severity (CRITICAL / WARNING / OBSERVATION), impact, dimension, file:line location, detail and a proposed fix with explicit strength, cost and confidence. A limit of 10 findings - related problems get consolidated, and sorting by severity separates findings worth fixing from noise.
The skill asks nothing and edits no code: it reads, analyzes, reports. A missing plan in a PR means a neutral comment and an exit, not a failure. A REJECTED verdict blocks the check only in the workflow - with an override label after a human decision.
"Empires fall, copies drift. The knowledge that survives is the one with a single source, an edition number and a route to every planet."
- curator of the Encyclopedia, preface to an editionThe Encyclopedia Galactica was created so that knowledge would survive the fall of an empire: a single source, an edition number, a route to every planet. A team's shared skills need exactly that, because AI artifacts are code - skills, commands and rules get one source of truth, versions and a distribution mechanism, instead of drifting copies pasted between machines.
AI artifacts are code: skills, commands and rules get one source of truth, versions and a distribution mechanism - instead of drifting copies pasted between machines.
Input: skills and rules scattered across the team's computers. Output: a package registry - a repo with a package definition, released versions and auth, from which everyone installs the same truth. Mechanism: install / update / uninstall like in a package manager, delivering to multiple tools - you pick the distribution channel by the audience meant to use it, not by how flashy the demo looks.
the module's second thesis - AI artifacts are code: one source of truth, versioned and distributed, instead of copies drifting apart.
Register a name or schema that is load-bearing: changing it forces a coordinated update across its consumers. The registry makes future plan reviews flag contract-breaking changes automatically.
Input: a freshly created or newly recognized contract - a schema field, an API endpoint, a frontmatter format. Output: an H2 entry in docs/reference/contract-surfaces.md, which /10x-plan-review greps against the text of every future plan.
The surface name (the H2 heading is the literal grep target - fully qualified instead of a bare identifier), the canonical file:line definition, owners and consumers, and a breaking-change checklist ready to paste into a migration plan.
A four-question interview - the user writes the content, the skill suggests nothing upfront → an echo of the entry and confirmation → self-bootstrap of the file on first use → append at the end of the registry.
The file is append-only - revisions are made by a human, by hand, and that friction is deliberate. One entry per invocation. The entry bar: if a rename breaks something only in a local module, it is not a contract and no entry is created.
"You do not lead a pack by the hand. Orders and boundaries come before the action - then you operate in the dark, and your report goes back to the commander."
- pack commander, briefing before the dropThe Howlers do not wait for the commander to approve their every move - orders and boundaries are set before the action, then you trust the pack and read the reports that come back. Async and remote agents are the same doctrine: delegation with deferred control - the sandbox and the network and secret boundaries are set before the agent launches, because nobody renegotiates the rules mid-action.
Async and remote agents are delegation with deferred control: the sandbox and the network and secret boundaries are set before the agent launches - and control returns in reports and review.
Input: a task that does not require your presence. Output: an agent working remotely in a sandbox with explicitly drawn boundaries - and a report that comes back for review. Mechanism: responsibility does not disappear with delegation, it changes when it happens - it returns in monitoring and review; you control the outcome, not every move.
when delegating - boundaries before launch, control in review; responsibility returns, it does not disappear.
Execute an approved plan with nobody at the keyboard: the autonomous counterpart of /10x-implement for /goal sessions and headless runs. Trust is replaced by explicit policies, not by the agent's good will.
The goal condition is written down before launch: file scope, no weakening of tests, a turn limit. A no-interaction policy - nobody is watching, so every decision has a rule set in advance, and doubts are resolved by the conservative interpretation: fewer files, fewer behavior changes, closer to the letter of the plan.
Each phase's implementation is written by a delegated subagent, but the quality gates run in the main context: plan criteria → a deliberate-break check (the test must go red on deliberately broken code - otherwise it protects nothing) → the full test suite → commits only on green, one per phase.
A divergence between the plan and reality is classified: Minor (a moved file, a changed name) - adapted and reported in one line; Structural (a missing dependency, a different architecture) - a STOP block with resume instructions. When in doubt: Structural.
The goal evaluator reads only the transcript: every gate verdict, commit SHA and adaptation must be narrated in the response - a gate that passed silently does not exist. At most 2 repair attempts per gate, then STOP; manual steps return to the human as a closing checklist.
The badge is closed by one of two projects - both grow straight out of this module: the observer-reviewer from lesson 03 or the encyclopedia from lesson 04.
Teamwork closes the third and final badge of the campaign. An agent that earns for the team is the finale of a road that started on the launch ramp.
This is the last module of the campaign - you have the full set: your own code under control, someone else's without fear, and an agent that works for the team. The best next step is to roll out one thing from here at work, still this week.
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