Team Levelup logo

Team Levelup

Organization
tikalk
team-levelup

Use when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives. Auto-triggers on session_end event. Also invoked from team-boot's Class Boots catalog for CDR descriptor matches.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill nameteam-levelup
Stars
144
Forks
2
Bundled files
8
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.

  • 8 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 Levelup 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-levelup
  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-levelup .claude/skills/team-levelup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Team Levelup 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 Levelup 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 Levelup 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-levelup

What this skill does

Session-end CDR lifecycle — extracts reusable patterns from the completed session, scores them by confidence, presents for batch review, and publishes accepted CDRs as a draft PR to team-ai-directives.

Replaces the former levelup-specify, levelup-clarify, and levelup-publish skills with a single streamlined workflow.

Searched N CDRs, K matched. 0 rows matched → emit the section heading + the searched line only — no table. A 0-row table header collapses into unrendered single-line markdown; never emit one.

When no local context-module records exist in the current working directory and the directory sits inside a workspace (detected via a .gitmodules marker in an ancestor), read the workspace root's records instead.

When to use

Invoke at the START of a matching task — before planning the todo list and before implementation — so the CDR context informs planning. Never defer to session end.

  • Session end (automatic): .events.json maps session_end → this skill
  • Manual invocation: /team-levelup after completing work
  • Before closing a branch: Extract team-wide learnings

When NOT to use

  • Brownfield discovery: Use /team-init to scan existing code
  • ADR/PDR/ChDR capture: Use the respective clarify skills (architect-clarify, product-clarify, change-clarify)

Storage

CDR drafts live in the adlc orphan branch of team-ai-directives:

team-ai-directives (adlc branch, orphan)
├── drafts/cdr/
│   ├── CDR-001.md        # Pending CDR
│   └── cdr.md            # Draft index
└── reports/
    ├── sessions/<user>/<YYYY-MM>.md
    ├── projects/<project>.json
    └── confidence-scores.json

Main branch has NO drafts directory — only accepted CDRs in context_modules/.

Process

Phase 0: Environment Setup

Run the setup script:

bash
scripts/bash/team-levelup.sh --setup

Parse JSON for REPO_ROOT, TEAM_AI_DIRECTIVES, NEXT_CDR, ADLC_BRANCH_EXISTS.

If TEAM_AI_DIRECTIVES is not configured, exit with message to run /team-setup.

Phase 1: Extract CDRs from Session

Review the current session to identify reusable patterns:

  1. What did the user ask for?
  2. What did the agent do? (file changes, key decisions, approach)
  3. What reusable patterns emerged?
  4. What files were created/modified? (git diff --stat, git log --oneline -10)

For each pattern, create a CDR draft using the shared template at skills/team/templates/cdr-draft-template.md.

Write session trace to adlc branch: reports/sessions/<user>/<YYYY-MM>.md.

Phase 2: Score Confidence

For each extracted CDR, calculate confidence:

Base scores by type:

  • Rule: 0.60
  • Persona: 0.50
  • Example: 0.50
  • Constitution: 0.70

Bonuses:

  • +0.20 if paired eval exists
  • +0.10 if evidence includes file paths/commits
  • +0.10 if multiple projects reference same pattern

Usage multiplier (from reports/confidence-scores.json):

  • success_rate > 0.8: ×1.2
  • success_rate 0.5-0.8: ×1.0
  • success_rate < 0.5: ×0.8

Thresholds:

  • ≥ 0.8: HIGH — batch review, auto-accept after review
  • 0.5-0.79: MEDIUM — batch review required
  • < 0.5: LOW — keep as draft

Phase 3: Batch Review

Present CDRs one at a time (same as former team-learn logic):

markdown
## CDR-{ID}: {Title}

**Context Type**: {type}
**Confidence**: {score} ({HIGH/MEDIUM/LOW})
**Current Status**: {status}

### Current Content
...

### Choose Action

| Option | Action |
|---|---|
| A | Accept — Approve for implementation |
| B | Reject — Decline with reason |
| C | Defer — Skip for now, keep pending |
| D | Accept all remaining — Accept this and all pending CDRs |
| P | Promote to check (mechanical rules only) |

Reply with your choice (A/B/C/D/P).

Wait for user input before proceeding. Update CDR file after each decision.

Absent Context

If team-boot injected no team context this session (no Team Context & Decisions section in the first user message — unconfigured project or hook failure): say so in one line, maintain rows in Team Context & Decisions with a 0 CDRs matched (no team context injected — run /team-setup) source line, and continue the task on directly-read CDR modules. Never treat a missing injection as an empty record set. Recovery: run /team-diagnose.

Phase 4: Publish

For accepted CDRs, create a draft PR to team-ai-directives main branch:

  1. Switch to adlc branch worktree
  2. Read accepted CDRs from drafts/cdr/
  3. Transform to OKF v0.2 format with confidence frontmatter
  4. Write to context_modules/ in main branch
  5. Create branch, commit, push, open draft PR

Phase 5: Write Usage Data

Write to adlc branch:

  • reports/sessions/<user>/<YYYY-MM>.md — privacy-scrubbed session summary
  • reports/projects/<project>.json — CDR match/apply counts
  • Update reports/confidence-scores.json

Phase 6: Notify

Write report to .adlc/team-levelup-report.md (local, not committed):

markdown
## Team-Levelup Report

**Date**: {date}
**CDRs Extracted**: N
**CDRs Accepted**: N
**CDRs Rejected**: N
**CDRs Deferred**: N
**PR**: {URL or "none — no accepted CDRs"}

### Next Steps
1. Review PR (if created)
2. Run `/team-repair --update-confidence` after merge

Conflict Resolution

When two projects draft the same pattern:

  1. First draft creates CDR-001 with project: project-a
  2. Second draft detects existing CDR with matching descriptor → merges:
    • Adds project-b to project: field
    • Appends evidence section
    • Increments confidence (multi-project validation)

Team Context & Decisions

team-levelup integrates with Team Context & Decisions maintained by team-boot. CDR-class decisions detected during the session are surfaced as capture suggestions (run /team-levelup to write the draft — the boot never writes it) and tracked as table rows in the same section. Mirror each CDR-class decision as a task-list todo (suggest capture → /team-levelup 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). At session end, 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. Do not fabricate table rows — only record decisions that actually emerged from the session.

Verification

  • CDRs written to adlc branch drafts/cdr/
  • Session summary written to adlc branch reports/sessions/
  • Usage counts written to adlc branch reports/projects/
  • Accepted CDRs published as draft PR to main branch
  • team-levelup-report.md written locally
  • No drafts in main branch

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 Levelup AI skill do?

Use when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives. Auto-triggers on session_end event. Also invoked from team-boot's Class Boots catalog for CDR descriptor matches.

Why use Team Levelup on TypingMind?

Because you install it once and use it with any model. Team Levelup 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 Levelup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/team/team-levelup. 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 Levelup?

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 Levelup?

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

Is the Team Levelup 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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