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 aksika/abtars --skill system-healthgit clone --depth 1 https://github.com/aksika/abtarsWrote 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/aksika/abtars/system-health)<a href="https://agentmods.dev/skills/aksika/abtars/system-health"><img src="https://agentmods.dev/badge/skills/aksika/abtars/system-health/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/aksika/abtars/system-health"><img src="https://agentmods.dev/badge/skills/aksika/abtars/system-health.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 Data Exfiltration · line 42 Code scans file system directories looking for sensitive files. This could be reconnaissance for credential theft.Fix: Remove unnecessary filesystem scanning. If file access is needed, use explicit, scoped paths. Avoid reading ~/.ssh, ~/.aws, or credential directories.
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 | $0.00015 | $0.00748 |
| Opus 5.5 | $0.00006 | $0.00299 |
| Sonnet 5.5 | $0.00003 | $0.00150 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
system-health 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 7d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Health Check
Run a comprehensive health check and report only real issues — not known/accepted deviations.
Step 0 — Read known deviations
read docs/system-notes.md
Parse this file first. Any item described here is expected and must NOT be reported as an issue. Mention them briefly as "known & accepted" if relevant.
Step 1 — Doctor
~/.abtars/scripts/doctor.sh
Step 2 — Heartbeat
cat ~/.abtars/memory/.heartbeat # epoch ms — if >10 min old, stalled
Step 3 — Cron / Tasks
# Use /tasks command or:
cat ~/.abtars/memory/cron-state.json
Step 4 — Sleep cycle
ls -lt ~/.abtars/memory/sleep/ | head -5 # last audit — if >2 days, broken
ls -la ~/.abtars/memory/sleep/*.lock 2>/dev/null # stale locks
Step 5 — Memory DB
sqlite3 ~/.abtars/memory/memory.db "SELECT 'messages', COUNT(*) FROM messages UNION ALL SELECT 'durables', COUNT(*) FROM long_term_memories;"
sqlite3 ~/.abtars/memory/memory.db "SELECT datetime(timestamp/1000, 'unixepoch', 'localtime'), substr(content,1,60) FROM messages ORDER BY timestamp DESC LIMIT 3;"
Step 6 — Consolidation
ls -lt ~/.abtars/memory/daily/ 2>/dev/null | head -3 # if >3 days, broken
ls -lt ~/.abtars/memory/weekly/ 2>/dev/null | head -3
Step 7 — Bridge logs
grep -i "error\|fail\|crash\|WARN" ~/.abtars/logs/bridge.log 2>/dev/null | tail -20
Step 8 — Backup
ls -lt ~/.backup-abtars/ 2>/dev/null | head -5 # should be <2 days old
Step 9 — Processes
ps aux | grep -E "abtars|kiro-cli|ollama" | grep -v grep
Step 10 — System resources
top -l 1 -n 0 | head -10
df -h /
uptime
Step 11 — Model availability
# Check configured models from transport.json
python3 ~/.abtars/scripts/scout-ollama.py
Reporting
Produce a concise report:
- Status: one-line overall (✓ healthy / ⚠️ degraded / 🔴 down)
- Services: gateway, channels (Telegram/Discord) — up/down each
- Issues: only NEW/UNEXPECTED problems (not in system-notes.md)
- Known & accepted: one-line summary like "3 known deviations acknowledged, all expected"
- Resources: disk, memory, uptime — only flag if concerning (disk >85%, etc.)
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.
- 7d ago Changed 769d2bfb300a
- 1mo ago First seen · 105 lines · 15 tokens per session scan A 308b380550db
system-health is a skill published in the GitHub repository aksika/abtars (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 15 tokens to every session and 748 once invoked, about $0.0001 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 2026-08-31.
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performance-profiling
CPU, memory, network bottleneck analysis — systematic performance investigation.
power-bi-performance-troubleshooting
Systematic Power BI performance troubleshooting prompt for identifying, diagnosing, and resolving performance issues in Power BI models, reports, and queries.
refactor
Systematic code refactoring to improve maintainability without changing behavior. Use when asked to "refactor this", "clean up this code", "improve code quality", "simplify this", "reduce complexity", or "eliminate code smells". Always preserves existing behavior.
extension-audit-methodology
Systematic 5-dimension audit framework for VS Code extensions — debug hygiene, dead code, performance, menu validation, dependency cleanup.
refactoring-patterns
Safe transformations — same behavior, better structure.