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 mikonos/zettelkasten-agent-skills --skill network-maintenancegit clone --depth 1 https://github.com/mikonos/zettelkasten-agent-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/mikonos/zettelkasten-agent-skills/network-maintenance)<a href="https://agentmods.dev/skills/mikonos/zettelkasten-agent-skills/network-maintenance"><img src="https://agentmods.dev/badge/skills/mikonos/zettelkasten-agent-skills/network-maintenance/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/mikonos/zettelkasten-agent-skills/network-maintenance"><img src="https://agentmods.dev/badge/skills/mikonos/zettelkasten-agent-skills/network-maintenance.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 | $0.00036 | $0.00445 |
| Opus 5 | $0.00018 | $0.00222 |
| Sonnet 5 | $0.00007 | $0.00089 |
| Haiku 4.5 | $0.00004 | $0.00044 |
Grade A, and why
network-maintenance 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.
What it actually says
Network Maintenance
Goal
Keep the knowledge graph healthy: reduce orphans, increase meaningful connectivity, and create bridges between clusters.
Quick start
- Define scope: directories + time window (e.g. last 30/90 days).
- Diagnose first, prescribe second: deliver only the top 3–5 structural issues and a task list.
Workflow
Step 1: Scan
- Count notes / links
- Identify hubs and clusters
- Identify outliers (orphans, weak links)
Step 2: Diagnose
Example thresholds:
- Orphan rate: <5% excellent / 5–10% good / 10–20% ok / >20% bad
- Avg degree: >3 excellent / 2–3 good / 1–2 ok / <1 bad
Step 3: Identify issues
- Orphans (0 links)
- Weak links (<2 links)
- One-way links
- Cluster isolation (no bridges)
- Tag/label inconsistency (if you use tags)
Step 4: Prescribe
Prioritize:
- Bridge work (cluster-to-cluster)
- Orphan rescue (high value notes first)
- Index creation / consolidation
Output template
## 🔧 Network Maintenance Report
### Snapshot
- **Total notes**: [N]
- **Total links**: [N]
- **Orphans**: [N] ([%])
### Health score: [0-100]
### 🚨 Top issues (prioritized)
1. **[Issue type]**: [what] → [why it matters] → [fix]
### 📋 Task list
- [ ] [Task 1]
- [ ] [Task 2]
Quality checks
- Metrics → diagnosis → tasks align 1:1
- Tasks are concrete (which notes, what links)
- Priorities are explicit
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 · 65 lines · 36 tokens per session scan A 9e5ff487e9a9
network-maintenance is a skill published in the GitHub repository mikonos/zettelkasten-agent-skills (2 stars, last pushed 7mo ago), licensed MIT. It adds 36 tokens to every session and 445 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 2026-08-31.
Other skills, from other repositories
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.