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-clarifygit 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-clarify)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/levelup-clarify"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/levelup-clarify/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-clarify"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/levelup-clarify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Memory Poisoning · line 17 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 73 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00043 | $0.02998 |
| Opus 5 | $0.00022 | $0.01499 |
| Sonnet 5 | $0.00009 | $0.00600 |
| Haiku 4.5 | $0.00004 | $0.00300 |
Grade A, and why
levelup-clarify 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 11d 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 — 413 lines — stays where its author put it; the contents beside it link to each section on GitHub.
levelup-clarify
What this skill does
Review pending CDRs (status Discovered or Proposed) and decide their fate: Accepted, Rejected, or Deferred.
This is the quality gate for all contributions to team-ai-directives:
- Validate that patterns are team-wide (not project-specific)
- Check for duplicates against existing team-ai-directives
- Ensure CDRs have clear context, decision, and evidence
- Update CDR statuses in
{REPO_ROOT}/.adlc/drafts/cdr/CDR-{NNN}.md - Regenerate
{REPO_ROOT}/.adlc/drafts/cdr/cdr.mdindex
This is an interactive command. Present exactly one CDR per interaction and wait for user input.
When to use
- After
/levelup-init: Validate brownfield discoveries - After
/levelup-specify: Review proposed feature learnings - After
/team-repairfound conflicts: Resolve conflict CDRs created by repair - Periodic review: Clean up stale pending CDRs
When NOT to use
- No pending CDRs: If no CDRs have status Discovered/Proposed, there is nothing to clarify
- Direct editing: Do not use this skill to bypass the review workflow
- Routine health checks: Use
/team-repairfor team AI directives maintenance
Process
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Examples of User Input:
"CDR-001 CDR-003"— Focus on specific CDRs"rules"— Clarify only rule-type CDRs"all"— Clarify all pending CDRs- Empty input: Clarify all CDRs with status "Discovered" or "Proposed"
Flags
--all: Clarify all pending CDRs (same as empty input)--type TYPE: Filter by context type (rules, personas, examples, skills, constitution, evals)--limit N: Limit to N clarifications per session (default: 5)--no-evals-gate: Disable the evals regression gate (default: gate is ON)
Role & Context
You are acting as a Context Validator reviewing discovered patterns. Your role involves:
- Validating that patterns are still relevant
- Clarifying scope (team-wide vs project-specific)
- Checking against existing team-ai-directives for overlap
- Refining CDR content through targeted questions
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.
- 11d ago First seen · 413 lines · 43 tokens per session scan A e2b0adad9565
levelup-clarify is a skill published in the GitHub repository tikalk/adlc-team-skills (133 stars, last pushed 3d ago), licensed MIT. It adds 43 tokens to every session and 2,998 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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