Team Boot logo

Team Boot

Organization
tikalk
team-boot

Use when a session starts or resumes after compaction (auto via the session_start and session_compact event hooks) and the team AI directives context — constitution, CDR index, Class Boots catalog, skills registry — is not yet injected; also fires the session-end friction trigger for CDR/ADR/PDR capture with pending-decision safety nets.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill nameteam-boot
Stars
144
Forks
2
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tikalk on GitHub. Read the source before you install it.

Installation

Install the Team Boot AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/tikalk/adlc-team-skills/tree/main/skills/team/team-boot
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/team/team-boot .claude/skills/team-boot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Team Boot in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Team Boot on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Team Boot is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

team-boot

Overview

Assembles team AI directives context and injects it into the system prompt at session start. The CDR index lists all available team context modules with descriptors — read full module files on demand when a task matches.

team-boot injects only the always-relevant layer: constitution titles, the compact CDR index, the Class Boots catalog, the skills registry, and MCP servers. The four record classes (ADR/PDR/ChDR/CDR deep-dive) plus technology selection load on demand through the class boots below — each pairs its index injection with decision capture.

Discovery is native: the injected CDR index is matched by the LLM against the current task on its own, per prompt, with no skill invocation. /team-discover is a separate, user-invoked command for explicit structured re-discovery (e.g., starting a complex feature) and is not part of the bootstrap loop.

Fast path: if the team AI directives context (constitution, CDR index, Class Boots catalog) is already in your system prompt or first user message, the event hook already ran — do nothing.

Class Boots

BootInjectsInvoke WhenCapture Via
architect-bootADR index (docs/adlc/memory/adr/ + legacy .adlc/memory/adr/)architecture work; tech-stack/pattern choicesuggest /architect-specify
product-bootPDR index (docs/adlc/memory/pdr/ + legacy .adlc/memory/pdr/)product/feature scope, personas, monetizationsuggest /product-specify
change-bootChDR index (docs/adlc/memory/chdr/chdr.md + legacy .adlc/memory/chdr.md)change-history rationale, reverts, issue-linked commitssuggest /change-specify (mining: /change-init)
team-levelupCDR module bodies (team-ai-directives)session end; CDR descriptor match; reusable team patternsuggest /team-levelup
tech-radar-bootTikal Tech Radar contextchoosing/evaluating technologyradar context + suggest /architect-specify

Invoke a class boot when a task or decision matches its row — invoke it at the START of the matching task, before planning the todo list and before implementation, so the class context informs planning. Never defer to session end. When no local memory index exists in the current working directory and the directory sits inside a workspace (detected via a .gitmodules marker in an ancestor), the boot reads the workspace root's docs/adlc/memory/ index instead. Each boot emits its class context section and its own searched line (_Searched N <class> records, K matched._), and carries the full detection and capture guidance for its class. 0 rows matched → the boot emits its section heading + the searched line only — no table.

Event hook (automatic)

The session_start and session_compact event hooks (declared in .events.json, wired by adlc-cli) run scripts/bash/boot.sh (POSIX) or scripts/powershell/boot.ps1 (Windows), which reads .adlc/init-options.json, assembles the context block (constitution, CDR.md index table, .skills.json), and outputs it to stdout. The plugin caches the result and pushes it into the system prompt on every step (idempotent — same cached content, no accumulation). The session_compact declaration makes post-compaction re-injection part of the contract: when a harness summarizes history, the generated plugin re-runs the handler and the dedup guard prevents double-injection. Agents whose adapters don't map session_compact yet skip it — adlc-cli owns those mappings.

Manual fallback (agents without event support)

  1. Read .adlc/init-options.json from the current working directory. Do NOT walk up parent directories. Do NOT use glob, find, or any file-search tool to locate it.
  2. If unconfigured (missing, null, or path doesn't exist): invoke the team-setup skill.
  3. If the factory-setup skill is installed in this session and no .adlc/issues-provider.yml resolves in the repo root: ask the user whether to run /factory-setup now (provider pick is questions-driven, same as team-setup modes). On decline, skip for this session — there is deliberately no persistent opt-out marker.
  4. If configured: assemble the context and follow the Class Boots catalog above. Full walkthrough in references/manual-fallback.md.

Decision Capture

Detect decisions as they emerge and suggest the matching capture skill to the user — never write drafts or touch .adlc/drafts/. The capture skill writes the draft when the user runs it; the clarify skill gates it afterwards.

Detection Triggers

PatternTypeCapture viaClarify via
Tech stack choice, pattern selection, "we chose X over Y"decision/architect-specify/architect-clarify
Feature scope, persona, monetizationproduct/product-specify/product-clarify
Reusable team rule, "we always do X"pattern/team-levelup/team-levelup
Revert/hotfix rationale, issue-linked commit, git command w/ human-authored message, authored PR title/body, CHANGELOG editincident/change-specify/change-clarify
Workaround adopted, "X for now because Y"workaround/change-specify/change-clarify
Operational constraint, "only works because Z"constraint/architect-specify/architect-clarify
Change abandoned, "simplifying X but Y blocks it"abandoned/change-specify/change-clarify
Eval criterion discoveredeval/evals-specify/evals-clarify

Proportionality gate and trust model in references/decision-capture.md: match documentation depth to how non-obvious the decision is, and synthesize project knowledge — never transcribe instructions.

Team Context & Decisions (every response)

Every response carries ONE merged section — grounding context rows and owed decision rows in the same 6-column table:

markdown
## Team Context & Decisions

| ID | Name | Type | Rel | Status | Clarify |
|--|--|--|--|--|--|
| CDR-YYYY-NNN | <name> | <type> | <relevance> | in use | — |
| — | <decision> | <ADR/PDR/CDR/ChDR/Eval> | <trigger> | pending | <clarify skill> |

_Scope: N CDRs · A ADRs · P PDRs · C ChDRs · E evals · M skills — J rows shown · Unrecorded: N pending · Unclarified: M captured drafts._

Status in use + Clarify — = grounding context (accepted records only). Status pending/captured/clarified/handed off + Clarify skill = owed decisions. No draft row may carry Status in use — drafts appear only as pending decision rows. J = all rows shown.

  • Detect: match session decisions against triggers above.
  • Classify: assign record type (ADR/PDR/CDR/ChDR).
  • Suggest: name the capture skill in one line — _Suggested capture: <decision name> → run <capture skill>._ Never write drafts or touch .adlc/drafts/; the capture skill writes the draft when the user runs it.
  • Track: update the table row (ID = draft ID or —, Status = pending/captured/clarified/handed off, Clarify = matching clarify skill).
  • Surface: mirror each detected decision as a task-list todo (suggest capture → matching clarify skill at session end). After code-modifying tasks, add a trailing todo to sweep Team Context & Decisions until Unrecorded: 0 pending · Unclarified: 0 drafts (a draft leaves Unclarified only via its clarify skill or an explicit user handoff to a named clarify or execute skill).
  • Session-end: before closing, deliver the clarify prompt naming each uncaptured decision (ID + capture skill) and each captured draft (ID + clarify skill); if the user defers clarify, mark those rows handed off.

Capture goes through the class capture skills (/architect-specify, /product-specify, /change-specify, /change-init for mined history, /team-levelup, /evals-specify) — the boot suggests, the user runs. The clarify skills (/architect-clarify, /product-clarify, /change-clarify, /evals-clarify) review whatever drafts exist at session end.

Failure Handling

  • Missing index + missing records → emit the section heading + _Scope: 0 CDRs · 0 ADRs · 0 PDRs · 0 ChDRs · 0 evals · 0 skills — 0 rows shown._ only — no table — and continue the user's task; never block.
  • Unparseable index rows → skip malformed rows, note the skip count.

Verification

  • Team Context & Decisions emitted with _Scope: N CDRs · A ADRs · P PDRs · C ChDRs · E evals · M skills — J rows shown · Unrecorded: N pending · Unclarified: M captured drafts._ (J = all rows shown; Status=in use rows are accepted records only, decision rows carry a clarify skill).
  • Class boots fired at task start (before todo planning), never deferred; workspace-root fallback engaged when CWD has no local memory.
  • Detected decisions added as table rows with the matching clarify skill.
  • No drafts written by the boot; each detected decision carries a _Suggested capture: … line naming its capture skill.
  • Detected decisions mirrored as task-list todos; trailing ledger-sweep todo added after code-modifying tasks; sweep closed only at Unrecorded: 0 pending · Unclarified: 0 drafts with the session-end clarify prompt delivered.

Unconfigured projects

Invoke team-setup to configure team AI directives for this project.

If factory skills are installed but no provider config resolves, ask whether to run factory-setup.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Team Boot AI skill do?

Use when a session starts or resumes after compaction (auto via the session_start and session_compact event hooks) and the team AI directives context — constitution, CDR index, Class Boots catalog, skills registry — is not yet injected; also fires the session-end friction trigger for CDR/ADR/PDR capture with pending-decision safety nets.

Why use Team Boot on TypingMind?

Because you install it once and use it with any model. Team Boot is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Team Boot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/team/team-boot. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Team Boot?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Team Boot?

As many as you like. As long as a model supports skills, you can use Team Boot with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Team Boot AI skill free?

Yes. It is published on GitHub by tikalk under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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