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 r007b34r/opencode-obsidian-knowledge-workflow --skill vault-health-feedbackgit clone --depth 1 https://github.com/r007b34r/opencode-obsidian-knowledge-workflowWrote 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/r007b34r/opencode-obsidian-knowledge-workflow/vault-health-feedback)<a href="https://agentmods.dev/skills/r007b34r/opencode-obsidian-knowledge-workflow/vault-health-feedback"><img src="https://agentmods.dev/badge/skills/r007b34r/opencode-obsidian-knowledge-workflow/vault-health-feedback/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/r007b34r/opencode-obsidian-knowledge-workflow/vault-health-feedback"><img src="https://agentmods.dev/badge/skills/r007b34r/opencode-obsidian-knowledge-workflow/vault-health-feedback.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 | $0.00048 | $0.01123 |
| Opus 5 | $0.00024 | $0.00562 |
| Sonnet 5 | $0.00010 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
vault-health-feedback 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 9d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vault-health-feedback
Constraints
- NEVER scan whole vault; sample strategically
- NEVER give more than 3 recommendations
- Check layers in order: workflow -> cognitive -> structural
- NEVER equate structural mess with system failure (check workflow first)
- NEVER repair, move, or restructure without explicit confirmation
Trigger Boundary
Use when: vault feels archival, knowledge not returning, system health diagnosis needed, highest-leverage repair unclear, user says "vault is dead" or "nothing comes back."
Do NOT use for:
- Inbox routing ->
inbox-triage - Recent-note relationships ->
connection-review - Week-level synthesis ->
weekly-synthesis - Direct restructuring before diagnosis
- Promoting material ->
note-promotion
Boundaries
- Owns: workflow health diagnosis, cognitive return assessment, structural friction detection
- Delegates to:
inbox-triage(root cause is inbox backlog),opencode-context-maintenance(root cause is context drift) - Never absorbs: inbox routing, relationship analysis, synthesis, promotion, direct restructuring
Continuations
| Condition | Next skill |
|---|---|
| Root cause is inbox backlog | inbox-triage |
| Root cause is context drift | opencode-context-maintenance |
| Vault paths inaccessible, possible config issue | opencode-context-maintenance |
| Vault is deeply archival, needs structural redesign | Escalate to human |
Companion Skill
All vault operations follow obsidian-mcp. Forbidden: obsidian_patch_note, obsidian_append_to_note. Verify every write through readback.
Procedure
Phase 1: Workflow Layer (always check first)
When assessing health metrics, read references/examples.md.
obsidian_list_notes path:"Inbox/"-> inbox freshness (items, age)obsidian_search_notes query:"type: synthesis"-> synthesis frequency- Check for recent triage/review/promotion activity
- Score: active / stale / dead
Phase 2: Cognitive Layer (only if workflow is active or stale)
- Sample 5-10 recent notes for reuse signals (links back, references)
- Check if themes recur across time windows
- Look for notes that were never revisited after creation
- Score: returning value / flat / declining
What ships with it
6 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.
- 9d ago First seen · 134 lines · 48 tokens per session scan A 4d54978facdc
vault-health-feedback is a skill published in the GitHub repository r007b34r/opencode-obsidian-knowledge-workflow (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 1,123 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.
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A lifecycle system for managing an Obsidian knowledge base, which is a folder of linked notes. It organizes raw material, AI-maintained wiki pages, and generated views into separate layers.
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llm-wiki
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or…
alive-morning
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pickup
Pick up work from repo-local handoff state. Use when starting a new Claude Code session, switching agents, returning to a project, or asking an AI agent to reconstruct context from prior work. Named pickup rather than resume to avoid shadowing Claude Code's built-in /resume conversation picker.