/operating-model-bootstrap
Bootstrap or harden a project's operating model for mixed human and AI delivery teams. Use when starting a repository, adopting an agent harness, defining authority and risk tiers, coordinating parallel agents safely, binding evidence and review to an exact candidate, or
$ npx -y skills add jpantsjoha/ai-native-developer-experience --skill operating-model-bootstrap --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/operating-model-bootstrap
Context preview
The summary Claude sees to decide when to auto-load this skill.
Bootstrap or harden a project's operating model for mixed human and AI delivery teams. Use when starting a repository, adopting an agent harness, defining authority and risk tiers, coordinating parallel agents safely, binding evidence and review to an exact candidate, or
SKILL.md
operating-model-bootstrap.SKILL.mdname: operating-model-bootstrap
description: Bootstrap or harden a project's operating model for mixed human and AI delivery teams. Use when starting a repository, adopting an agent harness, defining authority and risk tiers, coordinating parallel agents safely, binding evidence and review to an exact candidate, or correcting fragmented rules that prevent coherent, consistent, complete delivery.
Operating Model Bootstrap
When to use
- Starting a new repository or team project that will use AI agents or agent-harness skills
- Adopting this harness into an existing project for the first time
- Defining or repairing authority, risk tiers, lane isolation, or evidence-binding contracts
- Coordinating parallel human and agent lanes safely (worktrees, ownership, integration)
- Binding review to an exact candidate commit before R2/R3 work
- Correcting fragmented or contradictory rules across AGENTS.md, CLAUDE.md, and GEMINI.md
---
Establish a portable operating kernel and one tracked project profile. Treat humans, agents, skills, tools, and gates as one delivery system with explicit authority, ownership, evidence, and completion semantics. The day-one seed lets a new team begin system design and delivery planning while explicitly owned project unknowns are resolved.
Project awareness — seven areas to establish at init
The bootstrap must establish or explicitly own each of the following. Required areas must be answered before the profile leaves `seed` state. Optional areas have a recommended default; record `not yet established — owner: role; required before: trigger` for any unknown rather than leaving it blank or fabricating a value.
| # | Area | Status | Default if not declared | |---|---|---|---| | 1 | Product vision, objectives, actors, scope, out-of-scope, SOW / acceptance record | **Required** | None — must be declared | | 2 | Team roster: PO, SMEs, integration owner, reviewer roles, escalation path | **Required** | None — must be declared | | 3 | Technical stack: language, framework, architecture, repos, interfaces, runtime | **Required** | None — must be declared | | 4 | Tooling: package manager, linter, type checker, unit / integration / e2e test commands | Optional — recommended defaults | Linting + validation + unit tests minimum | | 5 | Cloud, hosting, data classification, residency, compliance, approved-vendor constraints | Optional — advised | Deduced from stack; invoke `governance-guardrail` to confirm policy alignment | | 6 | Automation: CI/CD, branch policy, release process, deployment owner, rollback | Optional — default provided | GitHub SemVer + tag-based releases; invoke `release-manager` to document and confirm | | 7 | Delivery controls: source of truth, issue/PR conventions, DoD, escalation triggers | Optional — default provided | GitHub stack; this repository's issues, ADRs, and architecture docs are the source of truth |
Backfill from an existing repo
When adopting into a repo that already has code, do not present a blank seven-area form. Run the read-only inspection pass to pre-fill what the repository already reveals:
python3 .agents/skills/operating-model-bootstrap/scripts/inspect_repo.py .
It reads manifests, tool configs, CI, `CODEOWNERS`, and docs, and prints **inferred** findings — each a machine guess with an evidence pointer and the exact `inferred — source: <evidence>; confirm: <role>` marker to paste into the profile. Rules:
- Transcribe each finding into the profile as its `inferred` marker; **never** silently
promote it to a verified fact.
- Confirm inferred fields **one at a time** with the named human; a confirmed field
becomes a plain verified value. The profile cannot go `active` while any `inferred` field remains (the validator enforces this).
- The pass is read-only and **never infers authority** — `CODEOWNERS` handles are roster
*candidates* only; role and accountability stay `unknown` until a human assigns them.
Init artefacts
The initializer script seeds structure, not decisions. Running `bootstrap` (or the `init` command, which runs `bootstrap` first) creates these records with template content to be grounded from verified project evidence:
- `docs/operating-model/PROJECT-OPERATING-PROFILE.md` — the project contract, holding the
seven-area answers above.
- `docs/VISION.md` — product vision, objectives, actors, scope, and out-of-scope (area 1).
- `docs/ROADMAP.md` — the big-picture scope of work: ordered outcome gates, seeded
unpopulated. The team adds dates as the Product Owner confirms them.
- `docs/STATUS.md` — the derived situation-report structure, seeded unpopulated.
- `docs/operating-model/DELIVERY-WORKFLOW.md`, `CHANGELOG.md`, and the
`AGENTS.md` / `CLAUDE.md` / `GEMINI.md` adapters.
The script seeds structure; the agent records the decisions. As part of the bootstrap workflow, capture the choices made during init as durable, referenceable authority:
- **Baseline ADR** — author the project's first architecture decision record at its ADR
convention (`ADR/ADR-0000-baseline-structure-DRAFT.md`, approved to `-approved.md`; see `the-architect`), capturing every choice confirmed or deferred during init, with the owner and resolving trigger for each explicit unknown. This is the authority record for the starting structure.
- **First status entry** — add one entry to `docs/STATUS.md` recording the initialization
event and the open unknowns. This records what happened, not invented progress; roadmap outcomes stay unpopulated until the Product Owner validates them.
Do not fabricate answers to populate any of these files. An honest `unknown — owner: X; required before: Y` is better than a plausible-sounding invention that will mislead every agent that later reads the profile.
Portability boundary
The operating model is model-, vendor-, and IDE-agnostic. Thin surface adapters only make the same contract discoverable and invocable. They may not change authority, risk,
Read more
name: operating-model-bootstrap description: Bootstrap or harden a project's operating model for mixed human and AI delivery teams. Use when starting a repository, adopting an agent harness, defining authority and risk tiers, coordinating parallel agents safely, binding evidence and review to an exact candidate, or correcting fragmented rules that prevent coherent, consistent, complete delivery.
Operating Model Bootstrap
When to use
- Starting a new repository or team project that will use AI agents or agent-harness skills
- Adopting this harness into an existing project for the first time
- Defining or repairing authority, risk tiers, lane isolation, or evidence-binding contracts
- Coordinating parallel human and agent lanes safely (worktrees, ownership, integration)
- Binding review to an exact candidate commit before R2/R3 work
- Correcting fragmented or contradictory rules across AGENTS.md, CLAUDE.md, and GEMINI.md
---
Establish a portable operating kernel and one tracked project profile. Treat humans, agents, skills, tools, and gates as one delivery system with explicit authority, ownership, evidence, and completion semantics. The day-one seed lets a new team begin system design and delivery planning while explicitly owned project unknowns are resolved.
Project awareness — seven areas to establish at init
The bootstrap must establish or explicitly own each of the following. Required areas must be answered before the profile leaves `seed` state. Optional areas have a recommended default; record `not yet established — owner: role; required before: trigger` for any unknown rather than leaving it blank or fabricating a value.
| # | Area | Status | Default if not declared | |---|---|---|---| | 1 | Product vision, objectives, actors, scope, out-of-scope, SOW / acceptance record | **Required** | None — must be declared | | 2 | Team roster: PO, SMEs, integration owner, reviewer roles, escalation path | **Required** | None — must be declared | | 3 | Technical stack: language, framework, architecture, repos, interfaces, runtime | **Required** | None — must be declared | | 4 | Tooling: package manager, linter, type checker, unit / integration / e2e test commands | Optional — recommended defaults | Linting + validation + unit tests minimum | | 5 | Cloud, hosting, data classification, residency, compliance, approved-vendor constraints | Optional — advised | Deduced from stack; invoke `governance-guardrail` to confirm policy alignment | | 6 | Automation: CI/CD, branch policy, release process, deployment owner, rollback | Optional — default provided | GitHub SemVer + tag-based releases; invoke `release-manager` to document and confirm | | 7 | Delivery controls: source of truth, issue/PR conventions, DoD, escalation triggers | Optional — default provided | GitHub stack; this repository's issues, ADRs, and architecture docs are the source of truth |
Backfill from an existing repo
When adopting into a repo that already has code, do not present a blank seven-area form. Run the read-only inspection pass to pre-fill what the repository already reveals:
python3 .agents/skills/operating-model-bootstrap/scripts/inspect_repo.py .
It reads manifests, tool configs, CI, `CODEOWNERS`, and docs, and prints **inferred** findings — each a machine guess with an evidence pointer and the exact `inferred — source: <evidence>; confirm: <role>` marker to paste into the profile. Rules:
- Transcribe each finding into the profile as its `inferred` marker; **never** silently
promote it to a verified fact.
- Confirm inferred fields **one at a time** with the named human; a confirmed field
becomes a plain verified value. The profile cannot go `active` while any `inferred` field remains (the validator enforces this).
- The pass is read-only and **never infers authority** — `CODEOWNERS` handles are roster
*candidates* only; role and accountability stay `unknown` until a human assigns them.
Init artefacts
The initializer script seeds structure, not decisions. Running `bootstrap` (or the `init` command, which runs `bootstrap` first) creates these records with template content to be grounded from verified project evidence:
- `docs/operating-model/PROJECT-OPERATING-PROFILE.md` — the project contract, holding the
seven-area answers above.
- `docs/VISION.md` — product vision, objectives, actors, scope, and out-of-scope (area 1).
- `docs/ROADMAP.md` — the big-picture scope of work: ordered outcome gates, seeded
unpopulated. The team adds dates as the Product Owner confirms them.
- `docs/STATUS.md` — the derived situation-report structure, seeded unpopulated.
- `docs/operating-model/DELIVERY-WORKFLOW.md`, `CHANGELOG.md`, and the
`AGENTS.md` / `CLAUDE.md` / `GEMINI.md` adapters.
The script seeds structure; the agent records the decisions. As part of the bootstrap workflow, capture the choices made during init as durable, referenceable authority:
- **Baseline ADR** — author the project's first architecture decision record at its ADR
convention (`ADR/ADR-0000-baseline-structure-DRAFT.md`, approved to `-approved.md`; see `the-architect`), capturing every choice confirmed or deferred during init, with the owner and resolving trigger for each explicit unknown. This is the authority record for the starting structure.
- **First status entry** — add one entry to `docs/STATUS.md` recording the initialization
event and the open unknowns. This records what happened, not invented progress; roadmap outcomes stay unpopulated until the Product Owner validates them.
Do not fabricate answers to populate any of these files. An honest `unknown — owner: X; required before: Y` is better than a plausible-sounding invention that will mislead every agent that later reads the profile.
Portability boundary
The operating model is model-, vendor-, and IDE-agnostic. Thin surface adapters only make the same contract discoverable and invocable. They may not change authority, risk,
A team-project AI harness bootstrap that gives humans and agents a shared operating contract from day one, moving AI leverage from an individual “IC superhero” advantage to a repeatable team capability on an equal playing field.
Other skills on join-the-team.
- /adk-expert
Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent systems built on ADK. Authoritative source: adk.dev.
Open skill - /adversarial-gate
JP's signature red-team pass — "how would I break this?" Argue against your own approach before proceeding. Trigger on any high-stakes decision, architecture choice, or before marking work complete.
Open skill - /alibaba-expert
Alibaba Cloud expert guardrails — RAM least-privilege, data boundaries, residency (mainland/international split), cost, and official-source validation. Trigger when designing or reviewing any Alibaba Cloud workload, especially agents or LLMs (Model Studio/Bailian, Qwen,
Open skill - /aws-expert
AWS expert guardrails — IAM least-privilege, data boundaries, cost controls, residency, and official-source validation. Trigger when designing or reviewing any AWS workload, especially agents, LLMs (Bedrock), or multi-account systems.
Open skill - /azure-expert
Azure expert guardrails — Entra ID least-privilege, policy-first governance, data boundaries, cost, residency, and official-source validation. Trigger when designing or reviewing any Azure workload, especially agents, LLMs (Foundry/OpenAI), or landing-zone systems.
Open skill - /cost-guardrail
LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.
Open skill

