MultiPhysicsVault/.agents/skills/wiki-context-pack/SKILL.md

name, description

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description

wiki-context-pack

Produce a token-bounded context pack from the Obsidian wiki — a compact, structured slice of the most relevant pages for a topic or recent activity, designed for downstream consumption by another agent or skill. Use when the user says "/wiki-context-pack", "make a context pack", "give me a context slice for X", "pack the wiki for my agent", or "bounded context for Y". Different from wiki-query (which answers a question) — this produces reusable input material for a downstream task.

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

2. For --recent mode: sort pages by updated: frontmatter descending. Take top 20 as candidates.

3. 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
3. 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."