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 djangonavarro220/agentic-life-os --skill travel-planninggit clone --depth 1 https://github.com/djangonavarro220/agentic-life-osWrote 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/djangonavarro220/agentic-life-os/travel-planning)<a href="https://agentmods.dev/skills/djangonavarro220/agentic-life-os/travel-planning"><img src="https://agentmods.dev/badge/skills/djangonavarro220/agentic-life-os/travel-planning/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/djangonavarro220/agentic-life-os/travel-planning"><img src="https://agentmods.dev/badge/skills/djangonavarro220/agentic-life-os/travel-planning.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 | $0.00022 | $0.01023 |
| Opus 5 | $0.00011 | $0.00511 |
| Sonnet 5 | $0.00004 | $0.00205 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
travel-planning 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 12d 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.
This is a copy
78% identical to decision-journal — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
travel-planning
Mandatory integrity contract
Before acting, load and follow ../../references/data-integrity.md. Its provenance, missing-value, correction-history, defensive-write, backup, and recovery rules are mandatory for this subskill.
Coordinate travel planning without turning the itinerary into a giant prompt blob. Keep pointers to reservations, documents, weather, packing, transport, risks, and next actions.
Trigger
Use when the user asks to:
- plan a trip, itinerary, packing list, or travel checklist
- review reservations, documents, transport, weather, or schedule risks
- prepare day-by-day options or fallback plans
- capture travel follow-ups before or after a trip
- decide what belongs in calendar, tasks, notes, mail, or documents
Operating model
- Resolve the active runtime and private data directory.
- Read
$LIFEOS_DATA_DIR/config.jsonwhen available. - If source ownership for this domain is unknown, inspect runtime-owned systems read-only and propose a source decision before writing anything.
- Load only
runtimes/<active-runtime>.mdif runtime-specific commands, storage pointers, delivery, scheduling, or integration behavior are needed. - Do not load other runtime adapters for the same task.
- Prefer pointers, access notes, review dates, and short operational state over duplicated raw data.
- Ask before changing external systems, creating reminders, contacting people, importing data, broad migrations, or destructive cleanup.
- Record useful Life OS coordination state under
$LIFEOS_DATA_DIR/travel-planning/data.jsonwhen the user/runtime policy allows it.
Source decision
Store the source choice in $LIFEOS_DATA_DIR/travel-planning/data.json when useful:
{
"source_decisions": {
"travel_records": {
"owner": "runtime|external|life-os",
"runtime": "<active-runtime>",
"source": "<tool-or-system>",
"access": "<short pointer or retrieval instruction>"
}
}
}
This JSON shape belongs in $LIFEOS_DATA_DIR/<skill-name>/data.json, not global config.json. Use Life OS as the source of truth only for Life-OS-specific preferences, technical state, or notes explicitly created inside Life OS. Do not silently create a second private database when the runtime or an external system already owns the real data.
What ships with it
3 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.
- 12d ago First seen · 133 lines · 22 tokens per session scan A a834db8b8313
travel-planning is a skill published in the GitHub repository djangonavarro220/agentic-life-os (11 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,023 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to decision-journal, differing in 26 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…