Knowledge Prime

Knowledge Primer

Surface project-relevant Basic Memory knowledge as a concise context brief. Identifies which dependencies and tools are documented, loads key gotchas and patterns, and highlights gaps worth filling.

Flags

• --deep — expand observation loading from 800 to 2000 tokens and include [pattern], [feature], [usage] categories alongside the default critical categories. Also expands graph traversal from top 6 to top 12 notes.

Edge Cases

• No manifest files found — report "No package or tool manifest files detected in the current directory" and skip steps 2–5. Suggest running in a project root.
• Empty BM directories — treat as 0 documented. Don't error.
• Very large dependency lists (100+) — cap at top 50 by frequency/ alphabetical for the cross-reference. Note total count in the brief.
• --deep on a small graph (<10 notes) — treat as compact mode. Report "Graph has fewer than 10 notes — showing all relevant notes."

Workflow

1. Identify project stack

Detect manifest files in the current working directory:

Use Read to check for root-level manifest files — do not use Glob, which recurses into node_modules/ and similar directories. Use Glob only for wildcard paths like .github/workflows/*.yml. For detected package manifests, use Read to extract dependency names:

• package.json → dependencies + devDependencies keys
• Cargo.toml → [dependencies] + [dev-dependencies] tables
• go.mod → require block
• composer.json → require + require-dev keys
• pyproject.toml → [project.dependencies] or dependencies key
• requirements.txt → package names (before ==/>=/etc.)
• Gemfile → gem '...' lines

For tool manifests, extract tool names:

• Brewfile → brew "...", cask "...", vscode "..." lines
• .github/workflows/*.yml → uses: lines (extract owner/repo)
• Dockerfile → FROM lines (extract image names)
• .vscode/extensions.json → recommendations array

2. Query Basic Memory

For each detected ecosystem, list documented notes:

list_directory(dir_name="npm", depth=1)
list_directory(dir_name="crates", depth=1)
list_directory(dir_name="brew", depth=1)
# ... (only for ecosystems detected in step 1)

Each list_directory call costs ~50 tokens — only query ecosystems that have manifest files in the project.

Cross-reference: for each dependency from step 1, check if a corresponding note title exists in the list_directory results. Build two lists:

• Documented — deps with a BM note
• Undocumented — deps without a BM note

3. Score relevance

Assign relevance scores to documented notes using three passes:

Pass 1 — Dependency match (score: 3): Notes whose title matches a direct project dependency.

Pass 2 — Graph expansion (score: 2): For top-scoring notes from pass 1, expand via:

build_context(url="<ecosystem-dir>/<note-title>", depth=1, max_related=5)

Use each note's own resolved path from pass 1 (e.g. npm/npm-fastify, crates/crate-tokio, brew/brew-ripgrep) — never hardcode to a single ecosystem; a Cargo-only or Brewfile-only project will have pass 1 top-scoring on crate or brew notes, not npm. Related notes that appear get score 2 (transitive relevance).

Pass 3 — Beads/activity boost (score: 1): Fetch recent activity now (needed for scoring before sorting):

recent_activity(timeframe="7d", output_format="json")

Set an explicit page_size and paginate by incrementing page until a page returns fewer items than page_size (or an empty result) — recent_activity has no has_more field, unlike search_notes — to ensure complete 7-day activity window. The response is a flat top-level result array (singular key, not nested under results); collect the set of active permalinks mentally, deduplicating as you go (recent_activity was observed returning duplicate rows for the same entity in live testing).

If the project has .beads/ (check via Glob(pattern=".beads/*")) or recent_activity shows notes updated in the last 7 days, those notes get +1 boost.

Sort all scored notes descending. Take top 6 (or top 12 with --deep).

4. Load observations

For each top-scored note, load critical observations:

read_note(identifier="<note-title>", include_frontmatter=true)

Extract only observations tagged with critical categories:

• [gotcha] — known pitfalls
• [limitation] — constraints to be aware of
• [breaking] — breaking changes With --deep, also include [pattern], [feature], and [usage] categories.

Token budget: 800 tokens total (2000 with --deep). If observations exceed the budget, prioritize [gotcha] > [breaking] > [limitation] > [pattern].

4b. Observation sweep (supplementary)

Search for critical observations beyond the top-scored notes:

search_notes(query="gotcha breaking limitation", search_type="text", entity_types=["observation"], page_size=10)

Note: BM's search treats space-separated terms and OR identically (hybrid search, not strict FTS5 boolean). The query above is equivalent to "gotcha OR breaking OR limitation" — use the simpler form.

Post-filter: Keep only observations whose content starts with [gotcha], [breaking], or [limitation]. Discard others — the text query matches these words anywhere, including prose mentions that are not category tags.

Deduplication: Build a set of note titles loaded in Step 4. Discard observations from notes already in that set — Step 4 extracted their critical observations in full context. Keep only observations from new notes.

Token budget: 200 tokens (400 with --deep), separate from Step 4's 800/2000 budget. Prioritize [gotcha] > [breaking] > [limitation].

Output: Swept observations appear in a separate ### Other warnings section in the brief (Step 6), after ### Key gotchas. Include the parent note title as attribution. Max 3 entries to protect the "scannable in 30 seconds" goal. Omit the section entirely if no new observations survive filtering.

If search_notes fails or returns an error, skip this step and proceed to Step 5. Note "Observation sweep skipped (BM search unavailable)" in the brief header.

10 results is sufficient — do not paginate even if has_more is true.

5. Cross-reference recent activity

Using the recent_activity results fetched in Step 3, note which of the top-scored notes were recently updated — these are most likely to be relevant to current work. Include in the brief output.

6. Synthesize brief

Produce a structured context brief:

## Project Knowledge Brief

### Stack detected
- npm: 45 deps (38 documented, 7 undocumented)
- brew: 12 tools (10 documented, 2 undocumented)

### Key gotchas
- **npm-fastify** — [gotcha] reply.send() after reply.redirect() causes hang
- **npm-pino** — [limitation] redaction doesn't work on nested arrays

### Other warnings
- **npm-helmet** — [gotcha] CSP defaults are permissive — tighten for production

### Recent activity
- 3 notes updated in last 7 days: npm-fastify, npm-pino, brew-ripgrep

### Gaps worth filling
- Top undocumented dep: `undici`
- Run `/knowledge-gaps` for full coverage analysis
- Run `/intel undici` to document the top gap

7. Suggest next steps

Based on the brief:

• If undocumented deps exist in the detected stack: suggest /intel <pkg> for the top one
• If no manifest files found: suggest running in a project directory
• If knowledge graph is empty for all detected ecosystems: suggest /knowledge-gaps first, then batch /intel
• If all deps are documented: note good coverage, suggest checking for staleness with the knowledge-gardener agent