Agent skill

Team Levelup

by tikalk in tikalk/adlc-team-skills

A skill your agent uses when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives.

MITAuto-check passedDevelopment

Install Team Levelup

skills CLI
$ npx skills add tikalk/adlc-team-skills --skill team-levelup -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tikalk/adlc-team-skills team-levelup --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tikalk/adlc-team-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/team/team-levelup .claude/skills/team-levelup && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
team-levelup
GitHub stars
141
Token cost
~1.8k tokens
SKILL.md length
746 words
Files
5 (incl. scripts)
Skills in repo
44
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives.

  • Works in 7 steps: Environment Setup → Extract CDRs from Session → Score Confidence → …
  • A session ends to extract CDRs
  • SKILL.md covers What this skill does, When to use, Storage and Process, plus 4 more sections
  • Runs Shell scripts from its folder; calls git

What it does

Team Levelup is an agent skill from tikalk/adlc-team-skills. Use when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives. Auto-triggers on sessionend event. Also invoked from team-boot's Class Boots catalog for CDR descriptor matches.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `scripts/helpers.sh`, `scripts/score-confidence.sh` and `scripts/scrub-session.sh`).

It sits in Development. The repository describes itself as: Agent skills for the Agentic SDLC: team lifecycle (team-boot, team-learn, team-init, team-repair), software factory, evals, CDR lifecycle with confidence scoring, and… The licence is MIT.

When your agent uses it

  • A session ends to extract CDRs
  • Score confidence
  • Publish accepted CDRs to team-ai-directives
  • Sessionend event

Example prompts

  • “/team-levelup”

Requirements

  • A Bash shell

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Environment Setup
  2. Extract CDRs from Session
  3. Score Confidence
  4. Batch Review
  5. Publish
  6. Write Usage Data
  7. Notify

What it can do on your machine

Read from SKILL.md and the folder at commit 2dbed36. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Team Levelup loads about 1.8k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 746 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tikalk/adlc-team-skills at commit 2dbed36, republished under its MIT licence (© tikalk). 746 words, ~1,770 tokens.

Download SKILL.mdSave it as .claude/skills/team-levelup/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
team-levelup
description
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.
disable-model-invocation
true
scripts.sh
scripts/team-levelup.sh
scripts.ps
scripts/team-levelup.ps1

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/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.

Show full SKILL.md (278 more words)Show less
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 captured as drafts and tracked as table rows in the same section. Mirror each CDR-class decision as a task-list todo (draft → /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 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

© tikalk, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts) in skills/team/team-levelup of tikalk/adlc-team-skills.

  • SKILL.md
  • scripts/helpers.sh
  • scripts/score-confidence.sh
  • scripts/scrub-session.sh
  • scripts/team-levelup.sh

Open the folder on GitHubat commit 2dbed36

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Categories

Questions about Team Levelup

What does Team Levelup do?

A skill your agent uses when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives. Team Levelup is an agent skill from tikalk/adlc-team-skills. Use when a session ends to extract CDRs, score confidence, batch review, and publish accepted CDRs to team-ai-directives.

When should I use Team Levelup?

Team Levelup fits situations like: A session ends to extract CDRs; score confidence; publish accepted CDRs to team-ai-directives; sessionend event.

How do I install Team Levelup in Claude Code?

Run `npx skills add tikalk/adlc-team-skills --skill team-levelup -a claude-code`. Or copy the skill folder (skills/team/team-levelup in tikalk/adlc-team-skills) into .claude/skills/team-levelup in your project. Claude Code loads it when a task matches its description.

How do I install Team Levelup in Codex?

Run `npx skills add tikalk/adlc-team-skills --skill team-levelup -a codex`. Or copy the skill folder (skills/team/team-levelup in tikalk/adlc-team-skills) into .agents/skills/team-levelup in your project. Codex loads it when a task matches its description.

Can I use Team Levelup in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tikalk/adlc-team-skills --skill team-levelup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/team-levelup, .gemini/skills/team-levelup, .github/skills/team-levelup and .opencode/skills/team-levelup in your project.

What does Team Levelup need to run?

Going by SKILL.md and its folder, Team Levelup needs a shell for the scripts in its folder and the command-line tools its instructions call (git). Our summary lists: A Bash shell.

Does Team Levelup access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Team Levelup safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Team Levelup use?

Team Levelup is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Team Levelup use?

About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Team Levelup?

Skills that share tags, products or a category with Team Levelup: Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars) and Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Team Levelup?

tikalk (a GitHub organization) maintains it in tikalk/adlc-team-skills, which has 141 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on October 6, 2026.

Source: tikalk/adlc-team-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.