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 levelup-initgit 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/levelup-init)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/levelup-init"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/levelup-init/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/levelup-init"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/levelup-init.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 164 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00039 | $0.03116 |
| Opus 5 | $0.00019 | $0.01558 |
| Sonnet 5 | $0.00008 | $0.00623 |
| Haiku 4.5 | $0.00004 | $0.00312 |
Grade A, and why
levelup-init 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 9d 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
levelup-init
What this skill does
Reverse-engineer Context Directive Records (CDRs) from an existing codebase (brownfield) to document reusable patterns that could become contributions to team-ai-directives.
You act as a Context Archaeologist uncovering implicit team patterns from code:
- Scan the codebase for reusable rules, personas, examples, skill-worthy capabilities, and eval-worthy patterns
- Detect cross-sub-system patterns and inconsistencies
- For each directive CDR, also extract a paired eval CDR with pass/fail cases from code evidence
- Compare against existing team-ai-directives to avoid duplicates
- Write CDRs to
{REPO_ROOT}/.adlc/drafts/cdr/CDR-{NNN}.mdwith status Discovered - Auto-generate
{REPO_ROOT}/.adlc/drafts/cdr/cdr.mdindex
Key Difference from /levelup-specify:
/levelup-init(this skill) = Discovers what's already implemented in code/levelup-specify= Extracts patterns from a completed feature's spec/plan/tasks
This skill focuses on current state analysis — what IS reusable, not what SHOULD BE created.
When to use
- Brownfield projects: Existing code without team-wide directives
- Legacy modernization: Extract reusable patterns before refactoring
- Team onboarding: Turn implicit conventions into explicit directives
- Team AI Directives bootstrapping: Populate a new team-ai-directives repository
When NOT to use
- Greenfield projects: Use
/levelup-specifyafter implementing a feature - Mining git history / issue-linked changes: Use
/change-initto recover past decisions from commits + issue trackers - CDRs already exist: If
.adlc/drafts/cdr/has pending CDRs, use/levelup-clarifyto review - Routine team AI directives health checks: Use
/team-repairfor re-indexing and conflict scanning
Process
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Examples of User Input:
What ships with it
2 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.
- 9d ago First seen · 350 lines · 39 tokens per session scan A 4078c3aca4c3
levelup-init is a skill published in the GitHub repository tikalk/adlc-team-skills (133 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 3,116 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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