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 realactivity/tula --skill lookoutgit clone --depth 1 https://github.com/realactivity/tulaWrote 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/realactivity/tula/lookout)<a href="https://agentmods.dev/skills/realactivity/tula/lookout"><img src="https://agentmods.dev/badge/skills/realactivity/tula/lookout/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/realactivity/tula/lookout"><img src="https://agentmods.dev/badge/skills/realactivity/tula/lookout.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 | $0.00106 | $0.01577 |
| Opus 5.5 | $0.00042 | $0.00631 |
| Sonnet 5.5 | $0.00021 | $0.00315 |
| Haiku 4.5 | $0.00011 | $0.00158 |
Grade A, and why
lookout 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 1mo 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lookout
Ambient health radar. Lookout reads where the user is right now, fuses environmental + place + neighborhood SDoH data with their FHIR record, and surfaces only the items that matter for them. A weather app says "AQI 142." Lookout says "ozone is high, you have asthma on file, want an indoor option?"
When to Use
✅ Use when:
- The user says "lookout for me" or any natural variant ("how's the air today?", "where can I work out right now?", "anything I should know today?", "what's [ZIP] like for me?")
- The configured daily briefing time fires
- The user asks about open-now resources (gym, pharmacy, urgent care, grocery, park) tied to their health
- The user asks about a neighborhood's SDoH overlay for travel or relocation
When NOT to Use
❌ Don't use when:
- The user wants a clinical interpretation of a PDF → use
med-pdf - The user wants to connect/refresh their patient portal → use
health-records - The user wants a drafted clinician message → use
epic-note - A request would diagnose, recommend treatment, or adjust a medication
- A request would produce billing, payor, prior-auth, EOB, or coding content (CPT/HCPCS/HCC). SDoH Z codes are the only codes used, and only as clinical context.
Setup
- Init the store.
python3 {baseDir}/scripts/init_db.py- Creates
~/.openclaw/workspace/.lookout-cache/lookout.dbfromschema.sql. - Seeds only tenant-neutral preferences (imperial, 07:30 briefing, 22:00–07:00 quiet hours). Re-running is safe.
- Creates
- Configure location. Onboarding or the user profile must create one
locationrow withis_default = 1and latitude/longitude before fetch runs. Location is user data; don't hard-code it in the skill. - Install Python dependency. Ensure
httpxis available to Python (python3 -m pip install httpxor the distro/package-manager equivalent). - Optional keys. Lookout runs end-to-end with no keys. For richer data:
AIRNOW_API_KEY— US AQI by lat/lon (free key from AirNow).GOOGLE_PLACES_API_KEY— open-now resources layer (paid).LOOKOUT_NWS_UA— descriptive User-Agent for api.weather.gov.
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.
- 1mo ago First seen · 96 lines · 106 tokens per session scan A 11ef5e6ec08b
lookout is a skill published in the GitHub repository realactivity/tula (46 stars, last pushed 3d ago), licensed Apache-2.0. It adds 106 tokens to every session and 1,577 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
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healthcare-expert
Expert-level healthcare systems, medical informatics, HIPAA compliance, and health data standards. Use when the user mentions medical, HIPAA, HL7, FHIR, or EHR, or when the task involves Healthcare IT, Standards and Protocols, Regulatory Compliance, or Security and Compliance.
system_status
Check system health -- disk usage, memory, running processes, uptime, battery.
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
branch_health
Quick health check -- test counts and file stats for AIPass branches.
genomics-epigenomics
Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first); working with single-cell ATAC (use scatac-preprocessing).