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 alexeyshishin/as-skill --skill obsidian-ingestgit clone --depth 1 https://github.com/alexeyshishin/as-skillWrote 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/alexeyshishin/as-skill/obsidian-ingest)<a href="https://agentmods.dev/skills/alexeyshishin/as-skill/obsidian-ingest"><img src="https://agentmods.dev/badge/skills/alexeyshishin/as-skill/obsidian-ingest/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/alexeyshishin/as-skill/obsidian-ingest"><img src="https://agentmods.dev/badge/skills/alexeyshishin/as-skill/obsidian-ingest.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 | $0.00141 | $0.01747 |
| Opus 5 | $0.00071 | $0.00873 |
| Sonnet 5 | $0.00028 | $0.00349 |
| Haiku 4.5 | $0.00014 | $0.00175 |
Grade A, and why
obsidian-ingest 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 11d 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
obsidian-ingest — Knowledge ingest into the Obsidian vault
This skill is the ingest pipeline coordinator. Your job: take a single markdown file, understand what knowledge is hidden in it, decide how best to represent it in the base, and create the necessary files. You do the formatting, frontmatter, and link enrichment yourself — that's an integral part of ingest.
Before starting, read the vault rules:
AGENTS.md— general principles for working with the base.claude/rules/note-types-frontmatter.md— frontmatter templates and fields.claude/rules/knowledge-structures.md— atomic notes, MOCs, syntheses.claude/rules/content-style.md— language, style, wikilinks.claude/rules/tags.md— tag taxonomy
Step 1. Read and understand the source
Read the input file in full. Determine:
- Content type: article / web clip, transcript, meeting notes, draft note, arbitrary markdown
- Main topic: one phrase describing what this text is about
- Domain: which tags it belongs to (sre, kubernetes, observability, career, concept, etc.)
- Confidence level:
mediumif there's a single source,highif you see the idea corroborated multiple times
Step 2. Extract entities (knowledge extraction)
Types:
- Concepts and models — abstract ideas, frameworks, thinking patterns
- Insights — non-trivial conclusions not obvious from the title
- Facts — specific statements, data, figures
- Practices — "how-to"s, processes, techniques
Atomicity criteria — knowledge-structures.md (the "When to split a section into its own note" section). Typical result: 3–8 entities from a single source.
Step 3. Come up with note names
Style (claim-based, not topic-based) and forbidden characters — file-naming.md.
Before naming — run rg -l "<keyword>" across the vault. If a similar note already exists, the plan includes updating the existing one instead of creating a new one.
Step 4. Draft the ingest plan
Plan → confirmation → action protocol — workflows.md. Plan format:
What ships with it
1 file 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.
- 11d ago First seen · 209 lines · 141 tokens per session scan A 55114928fe48
obsidian-ingest is a skill published in the GitHub repository alexeyshishin/as-skill (4 stars, last pushed 22d ago), licensed MIT. It adds 141 tokens to every session and 1,747 once invoked, about $0.0007 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
knowledge-ops
Use when an Obsidian knowledge system needs classification, deduplication, retrieval, synchronization, debt queues, or governed Agent/Wiki promotion.
context-manager
Use when a long-running agent task needs context budgeting, checkpointing, compaction, retrieval, or capability-based model routing.
session-learn
Use when a completed work session should yield durable concepts, corrections, decisions, reusable patterns, and a traceable next action.
session-manager
A fallback skill for recording development sessions and restoring project context from an Obsidian vault, a folder of linked notes. It handles explicit resume, handoff, status, and end-of-session requests when automatic capture is unavailable.
cds-stats
Show project memory statistics: session count, observation counts by type, entity count, top entities, and project planning status.
cds-search
Search past session observations using the sessions.search MCP tool. Falls back to CLI if the MCP server is not available.