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Skills/ar9av/obsidian-wiki/wiki-context-pack
wiki-context-pack logo

wiki-context-pack

ar9av/obsidian-wiki
820 installs2K stars
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Installation

npx skills add https://github.com/ar9av/obsidian-wiki --skill wiki-context-pack

Summary

>

SKILL.md

Wiki Context Pack — Bounded Token Retrieval

You are producing a focused, token-bounded context pack from the wiki. Unlike wiki-query (which answers a question), this skill packages the most relevant wiki knowledge into a single markdown block that a downstream agent, skill, or user can consume directly.

Before You Start

  1. Resolve config — follow the Config Resolution Protocol in llm-wiki/SKILL.md (walk up CWD for .env → ~/.obsidian-wiki/config → prompt setup). This gives OBSIDIAN_VAULT_PATH and any QMD variables.
  2. Read $OBSIDIAN_VAULT_PATH/hot.md if it exists — gives instant context on recent activity.
  3. Read $OBSIDIAN_VAULT_PATH/index.md — the full page inventory.

Invocation Forms

/wiki-context-pack "transformer attention mechanism" --budget 16000
/wiki-context-pack "my-project architecture decisions" --budget 8000
/wiki-context-pack --recent --budget 4000   # recent activity pack from hot.md
/wiki-context-pack "authentication patterns"          # default budget: 8000 tokens

Parse the user's invocation to extract:

  • topic — the query string (required unless --recent)
  • --budget N — token budget in tokens (default: 8000; max: 100000)
  • --recent — pack the most recently updated/ingested pages instead of a topic query

Algorithm

Step 1: Relevance Pass (cheap)

Without opening page bodies:

  1. Scan index.md and frontmatter for topic match. Score each page:
  • +5 exact title or alias match
  • +3 tag match
  • +2 summary: field contains the query term
  • +1 index.md entry description contains the query term
  1. For --recent mode: sort pages by updated: frontmatter descending. Take top 20 as candidates.
  1. For topic mode: collect the top 20 candidates by score. If QMD is configured (QMD_WIKI_COLLECTION set), run a semantic pass and merge with the frontmatter score (QMD rank adds +4 to the page's score).

Step 2: Tier-Aware Selection

Within the candidate set, sort by relevance score, then apply tier ordering within each score bucket (see llm-wiki/SKILL.md, Importance Tiering section):

  1. All core-tier matches first
  2. Then supporting
  3. Then peripheral (only if budget allows)

Maintain this ordering when filling the budget in Step 3.

Step 3: Compression

For each selected page (in tier/relevance order), compute its compressed representation — not a full read, but a structured distillation:

  1. Required: title, tier:, tags:, summary: (from frontmatter — cheap, no body read needed)
  2. If budget allows: add the page body, but stripped of:
  • Frontmatter block (already captured above)
  • The ## Sources section (keep source names in a one-liner instead)
  • Duplicate wikilinks that are already mentioned in included pages
  • Boilerplate headers with no content following them
  1. Dedup overlapping content — if two selected pages share a paragraph (or near-identical claim), keep it only in the more relevant page. Mark the removal: _(content also in [[other-page]])_.

Estimate tokens for each page representation as len(text_chars) / 4.

Step 4: Budget Enforcement

Fill the pack greedily in tier/relevance order until the budget is exhausted:

  1. Always include the frontmatter summary block for every selected page, even if the body doesn't fit.
  2. If a page body doesn't fit in full, include a compressed excerpt: the first non-header paragraph plus the "Key Ideas" section (if present).
  3. Drop peripheral-tier pages first when trimming.
  4. Keep a running token count. Stop adding pages when the next page would exceed the budget.
  5. Track how many pages were dropped and note it in the header.

Step 5: Render Output

Emit a single markdown block:

# Context Pack: <topic>
# Generated: <ISO timestamp>
# Budget: <budget> tokens | Actual: <actual> tokens | Pages: <N included> / <M candidates>
# Methodology: 4 chars/token estimate

---

## [[<category/page-name>]] (<tier>, ~<tokens> tokens)
tags: #tag1 #tag2
summary: <summary field text>

<compressed body or excerpt>

---

## [[<next-page>]] (<tier>, ~<tokens> tokens)
...

If --recent mode, the header reads:

# Context Pack: Recent Activity (last N pages)

Empty result: If no pages scored above 0 and --recent produced no results, output:

# Context Pack: <topic>
No relevant pages found. Consider running /wiki-ingest to add sources about this topic.

Step 6: Log

Append to $OBSIDIAN_VAULT_PATH/log.md:

- [TIMESTAMP] CONTEXT_PACK topic="<topic>" budget=<N> actual_tokens=<M> pages_included=<K> pages_dropped=<D>

Use Cases

  • Feed into /wiki-research — pass the pack as context to avoid re-discovering known facts
  • Pass to /wiki-synthesize — scoped input for a specific synthesis task
  • Provide to external agents via MCP or clipboard — bounded, structured, citation-ready
  • Checkpoint context before a long multi-step task — know what the wiki already knows before starting

Notes

  • The 4 chars/token heuristic matches wiki-status's token footprint estimate — consistent across skills
  • The pack is a snapshot; it is not written to the vault. Re-run to refresh.
  • For very large budgets (> 50K tokens), warn the user: "This pack is large. Consider narrowing your topic or using wiki-query for a targeted answer instead."

Score

0–100
57/ 100

Grade

C

Popularity17/30

820 installs — growing adoption. Source repo has 2,235 GitHub stars.

Completeness19/30

Documented: full SKILL.md body, one-line install. Missing: description, category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Wiki Context Pack skill score badge previewScore badge

Markdown

[![Wiki Context Pack skill](https://www.remoteopenclaw.com/skills/ar9av/obsidian-wiki/wiki-context-pack/badges/score.svg)](https://www.remoteopenclaw.com/skills/ar9av/obsidian-wiki/wiki-context-pack)

HTML

<a href="https://www.remoteopenclaw.com/skills/ar9av/obsidian-wiki/wiki-context-pack"><img src="https://www.remoteopenclaw.com/skills/ar9av/obsidian-wiki/wiki-context-pack/badges/score.svg" alt="Wiki Context Pack skill"/></a>

Wiki Context Pack FAQ

How do I install the Wiki Context Pack skill?

Run “npx skills add https://github.com/ar9av/obsidian-wiki --skill wiki-context-pack” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Wiki Context Pack skill do?

> The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Wiki Context Pack skill free?

Yes. Wiki Context Pack is a free, open-source skill published from ar9av/obsidian-wiki. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Wiki Context Pack work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Wiki Context Pack works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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