git:20260629.d6dee95 to git:20260727.3288eb6

- 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.

- 1. **Resolve config** — follow the Config Resolution Protocol in `llm-wiki/SKILL.md` (inline `@name` override → 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:

- ```markdown

- # 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."