Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tikalk/adlc-team-skills --skill team-levelupgit clone --depth 1 https://github.com/tikalk/adlc-team-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tikalk/adlc-team-skills/team-levelup)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/team-levelup"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/team-levelup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/team-levelup"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/team-levelup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.01854 |
| Opus 5.5 | $0.00023 | $0.00742 |
| Sonnet 5.5 | $0.00011 | $0.00371 |
| Haiku 4.5 | $0.00006 | $0.00185 |
Grade A, and why
team-levelup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.jsonmapssession_end→ this skill - Manual invocation:
/team-levelupafter completing work - Before closing a branch: Extract team-wide learnings
When NOT to use
- Brownfield discovery: Use
/team-initto 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:
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.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago Changed · +4 lines d713ab1715db
- 13d ago Changed · +10 lines df757e79eaee
- 15d ago First seen · 195 lines · 57 tokens per session scan A cbf3b21f149a
team-levelup is a skill published in the GitHub repository tikalk/adlc-team-skills (141 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 1,854 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-01.
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state
Use when the user says 'update state', 'project state', 'where was I', or at session start to load current context.
project-butler
A project-memory workflow that keeps a code project’s rules, current structure, documents, logs, tasks, and handoff notes organized.
register-project
Register an existing project in memory. Use when starting work on a new repo or when a project needs its own context file. Triggers on: 'register project', 'add project to memory', 'track this project', 'create project file'.
pm-context-system
Use this skill proactively during any session — observe the conversation, and the moment a decision is made or context worth keeping surfaces, propose a one-line log entry rather than interviewing the user up front. Use this skill when the user makes an explicit request too — wanting Claude to remember their work…
vibe-coding-survival
A practical guide for managing projects built with AI that writes code, often called vibe coding. It covers keeping decisions recorded, limiting the work in progress, saving usable versions, and checking that changes really work.
trajectory
Internal dynos-work skill. Sequence memory manager. Stores compact task traces and retrieves similar prior tasks to inform discovery and design review. Runs only when the user explicitly types /dynos-work:trajectory; never auto-triggered from conversation.