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-specifygit 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-specify)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/levelup-specify"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/levelup-specify/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-specify"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/levelup-specify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Data Exfiltration · line 3 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00046 | $0.03377 |
| Opus 5 | $0.00023 | $0.01688 |
| Sonnet 5 | $0.00009 | $0.00675 |
| Haiku 4.5 | $0.00005 | $0.00338 |
Grade A, and why
levelup-specify 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 — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
levelup-specify
What this skill does
Extract Context Directive Records (CDRs) from the current session after completing work.
This is the primary command for capturing learnings from completed work:
- Review the current session directly (the agent remembers what it did)
- Identify reusable patterns: rules, personas, examples, skills, and constitution amendments
- For each directive CDR, also extract a paired eval CDR with pass/fail cases from the session
- Link CDRs to concrete implementation evidence (files, commits, tests)
- Write CDRs to
{REPO_ROOT}/.adlc/drafts/cdr/CDR-{NNN}.mdwith status Proposed - Auto-generate
{REPO_ROOT}/.adlc/drafts/cdr/cdr.mdindex
Key Difference from /levelup-init:
/levelup-init= Discovers patterns from existing codebase (brownfield)/levelup-specify(this skill) = Extracts patterns from the current session (greenfield)
This skill focuses on session-level learnings — what reusable knowledge emerged from the work just completed.
When to use
- After completing work: Capture reusable patterns from the session
- Contributing back to team AI directives: Turn session work into reusable directives
- Before closing a branch: Extract team-wide learnings
When NOT to use
- Brownfield projects: Use
/levelup-initto scan existing code - Mining git history / issue-linked changes: Use
/change-initto recover past decisions from commits + issue trackers - Before work is done: Run this after completing the implementation
- Routine team AI directives validation: Use
/team-repairfor health checks
Process
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Examples of User Input:
"Focus on error handling patterns"— Extract CDRs related to error handling"Document the testing approach"— Focus on testing patterns"CDR-001"— Enrich an existing CDR with more session evidence"--focus skills"— Only propose skill-type CDRs- Empty input: Extract all patterns from the session trace
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.
- 10d ago First seen · 402 lines · 46 tokens per session scan A 38fc81807203
levelup-specify is a skill published in the GitHub repository tikalk/adlc-team-skills (133 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 3,377 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.
Other skills, from other repositories
session-management
Context preservation, tiered summarization, resumability.
code-graph
AST-based code graph for fast symbol lookup, dependency analysis, and blast radius via codebase-memory-mcp MCP server.
mnemos
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies.
report
Writes the session final report to a file, then prints only the path and a one-line summary. Fires when the prompt contains "Report per memstack:report", and also when the prompt begins with a standing trigger configured through MEMSTACKREPORTONTASKPROMPTS or MEMSTACKREPORTTRIGGERS. Dormant otherwise.
compress
Use when the user says 'tokenstack', 'compression', 'token savings', 'proxy status', or asks about context window usage.
token-optimization
Use when the user says 'token optimization', 'save tokens', 'context window', 'reduce tokens', 'token stack', or 'TokenStack', or asks about extending context window capacity. Covers TokenStack, the built-in compression proxy that shrinks Claude Code tool output before it reaches the Anthropic API. Do NOT use for…