LivePilot — Private Corpus Builder

Use this skill when the user wants to create a private corpus-atlas from their own filesystem content. The result becomes queryable through the same atlas_* and extension_atlas_* MCP tools that already index Ableton's factory packs.

You are NOT scanning anything blindly. Every step needs the user to confirm a path, a scanner type, and (optionally) opt in to AI annotation.

When to invoke

Trigger words: "build my corpus", "scan my projects", "index my racks", "make atlas of my library", "private atlas", "know my own sound", "my production knowledge", "scan my plugin presets".

This skill is the right entry point for the FIRST time a user wants to build their corpus. For subsequent runs (incremental scans, adding sources) the user can just call corpus_* tools directly — the skill is about ergonomics for newcomers.

Workflow (6 steps)

Step 1 — Confirm intent + state

Read docs/USER_CORPUS_GUIDE.md for the architecture (don't paste it back to the user — assume they want results, not docs). Tell the user:

"I'll set up a private corpus at ~/.livepilot/atlas-overlays/user/, scan whatever folders you point me at, and make the result queryable via the same atlas tools. Mechanical scan first (fast, deterministic). AI annotation is optional and runs as a separate step."

Then call:

corpus_status()

If sources: [], this is a fresh setup — call corpus_setup_wizard() (step 1.5 below) to detect candidates. If sources already exist, ask whether to add new ones, rescan existing, or both.

Step 1.5 — First-run wizard (NEW — recommended for new users)

corpus_setup_wizard()

This surveys the user's filesystem for sensible scan targets and returns a candidate packet — no scanning happens yet. Walk the user through each candidate one at a time:

"I found a User Library folder with 1,234 .adg rack presets — should I index it? (default: yes)"

>

"I see 431 .amxd Max devices in MAX MONTY/m4l_2024 — index them too?"

>

"Also a bundle of plugin presets at ~/Library/Audio/Presets — these tend to be noisy (5,000+ files often). Skip or include?"

For each YES, call corpus_add_source(...) with the candidate's suggested_id, type, path. Then ask the separate plugin-detection question:

"Want me to also detect installed VST3/AU/VST2 plugins via corpus_detect_plugins? Independent of file scanning — different layer."

If yes, call corpus_detect_plugins() after the file scan completes.

Personal .als project folders are NEVER auto-suggested — privacy-sensitive. The user must point at any specific session they want indexed.

Step 2 — Initialize

corpus_init()

Idempotent. Creates the output dir + a default manifest if absent.

Step 3 — Discover what to scan

Ask the user:

"Which folders should I index? Common places (you can pick any combo): - Live projects: typically ~/Music/<your-folder> or wherever you save .als files - Rack library: ~/Music/Ableton/User Library/Presets - Max devices: ~/Documents/Max <N>/Max for Live Devices - Plugin presets: ~/Library/Audio/Presets (macOS) or %APPDATA%\VST3 Presets (Windows) Tell me a path + what type of content it has, and I'll add it."

Run corpus_list_scanners() to show the user what types are supported on this install.

For each path the user names:

corpus_add_source(
    source_id="<short-id-they-pick>",
    type="<als | adg | amxd | plugin-preset>",
    path="<absolute path>",
    recursive=True,
)

Recommendations:

• Use short, lowercase, hyphenated source IDs: my-projects, my-racks,

m4l-devices. They become part of every entity_id and namespace.

• For very large libraries (>2000 files), suggest scanning a subset first

to validate before doing the full sweep.

• Ask about exclude patterns for backup folders, version-control checkouts,

and .als-archive files.

Step 4 — Run the scan

corpus_scan()    # all sources
# OR
corpus_scan(source_id="my-projects")   # one source

Show the user the per-source progress + error counts. If any source had errors, surface the first 3 with paths so they can see whether to ignore or fix them. Common error causes:

• Live 11 corruption sentinel files
• Password-protected projects
• Symlink loops
• Filesystem permissions

If a path doesn't exist, the manifest still keeps the source — the user can fix the path with corpus_remove_source then re-add.

Step 5 — Verify queryability

After scanning, the new sidecars are on disk but the running MCP server hasn't loaded them yet (overlays are read at boot). Tell the user:

"Restart the MCP server to load the new sidecars into the overlay index. After restart you can query via: - extension_atlas_search(query='...', namespace='user.<source_id>') - extension_atlas_get(...) - atlas_* tools that respect overlay namespaces"

If they're inside Claude Code, the MCP server is already restarted on each session start, so the next session sees the new corpus.

Demonstrate one search after restart:

extension_atlas_search(query="pad", namespace="user.<their-source-id>")

If matches return: green light. If no matches: check that wrappers were written (look at ~/.livepilot/atlas-overlays/user/<output_subdir>/ for *.yaml files), check the wrapper's tags, and verify the namespace is user.<source_id> not user/<source_id>.

Step 6 — Optional: AI annotation

If the user wants the corpus to have free-form descriptions (sonic fingerprint, reach-for, key-techniques — like the factory pack identity YAMLs), do an AI annotation pass.

Per the feedback rule on subagents, this MUST use parallel sonnet subagents. Pattern:

1. Read the source's _parses/*.json directory.
2. Group sidecars into batches of ~20 entries.
3. For each batch, dispatch ONE sonnet agent with:

• The batch's JSON sidecars
• The artist + genre vocabulary references from

livepilot/skills/livepilot-core/references/

• The annotation YAML schema (see USER_CORPUS_GUIDE.md §"AI annotation")

1. Each agent writes one annotated/<slug>.yaml per sidecar in its batch.
2. Synthesize a summary in main: total annotated, failures, sample preview.

Brief template for the annotation subagent: "You are writing producer- oriented annotation YAMLs for these N sidecars. Read each sidecar's device list + scale + BPM. Read the artist/genre vocabularies for register matching. Write one YAML per sidecar with sonic_fingerprint (3-5 sentences), reach_for / avoid bullet lists, genre_affinity tags, producer_anchors when the device combo or aesthetic register clearly maps to a known producer."

Cap subagents at 3 in flight at once to avoid context bloat in main.

Anti-patterns (don't do these)

1. Don't auto-scan without confirmation. The user's filesystem is

private; ask before every new path.

1. Don't recurse into protected folders. Watch for paths like

~/Library/Application Support/com.ableton.live/Drm or anything in /System/.... Filter via exclude_globs.

1. Don't apply AI annotation to ALL sources by default. It's expensive

in tokens and the user may only care about annotations for their .als projects, not their .adg rack library.

1. Don't suggest deleting the manifest to "start over" — corpus_remove_source

preserves sidecars on disk so they can be reused. Manifest delete is destructive only to the source-list, not to indexed data.

1. Don't try to write to the bundled atlas tree. Always write under

~/.livepilot/atlas-overlays/user/. The factory atlas at packs/ is read-only from this skill's perspective.

Quick reference — corpus tools (14 total)

For querying the built corpus, use the existing atlas_* and extension_atlas_* tools with namespace="user.<source_id>".

Plugin knowledge engine — Phase 2-4 agent flow

When the user asks: "learn about my installed plugins", "index my VST library", "build knowledge of all my plugins" — this is the Plugin Knowledge Engine. See docs/PLUGIN_KNOWLEDGE_ENGINE.md for full architecture.

Step P1 — Detect (deterministic)

corpus_detect_plugins()

Walks OS-standard plugin folders, parses each bundle's identity metadata (VST3 moduleinfo.json / AU Info.plist / VST2 CcnK / AAX manifest), writes ~/.livepilot/atlas-overlays/user/plugins/_inventory.json. On macOS expect 30-200 plugins typical; runtime <2 seconds.

Show the user the count + format breakdown. Ask if they want to proceed with all detected plugins or filter to a subset (by vendor, format, size).

Step P2 — Local manuals (deterministic)

For each plugin (or the priority subset):

corpus_discover_manuals(plugin_id="<id>")

Globs the standard manual locations + extracts text from the top candidate via pypdf / bs4 / pdfplumber. Section splitter detects chapter headings.

Many modern plugins won't have local manuals (vendors point to web docs). That's expected — Step P3 picks up the slack.

Step P2.5 — Canonicalize + cluster the inventory (NEW — efficiency step)

After corpus_detect_plugins returns the raw inventory (often 100-200 plugins on a populated Mac with iOS-port apps), call:

corpus_canonicalize_plugins()      # dedupes by vendor+name, prefers VST3
corpus_cluster_plugins()           # groups by vendor for efficient research

corpus_canonicalize_plugins collapses cross-format duplicates ("Valhalla DSP, LLC" + "Valhalladsp" → one record with formats_available: [AU, VST3]) and filters utility/system entries (Apple system AUs, Splice loaders) by default. On a real machine this typically reduces ~120 raw plugins → ~50-70 canonical.

corpus_cluster_plugins groups by canonical vendor + flags singletons. The result tells the agent which vendors to research as CLUSTERS (one shared WebSearch pass + N lean identity yamls) vs SINGLETONS (independent research). Typical clustering: Cem Olcay (9 MIDI tools = 1 shared pass), Moog (6 = 1 pass), chowdsp (4 = 1 pass) — vs flagship singletons like Drambo / Audulus 4 that warrant their own research.

Wave-dispatch pattern from real-world testing:

• ~5-8 cluster agents in parallel + 1-2 singleton-batch agents = ~7-10 total

parallel sonnet subagents.

• Each cluster agent is briefed with: plugin IDs from corpus_cluster_plugins

output, the existing reference identity (e.g., Valhalla Supermassive's), the shared-research strategy from the cluster manifest's research_strategy field.

• Singleton-batch agents handle 5-7 unrelated plugins each with independent

research per plugin.

• Token cost: ~80K per flagship singleton; ~30K-50K per cluster plugin (after

research-sharing amortization). 60 plugins ≈ 700K-1M total.

Step P3 — Web research (agent-driven, dispatch sonnet subagents)

For each plugin without a local manual (or to supplement an outdated one):

corpus_research_targets(plugin_id="<id>")

Returns a structured packet of search queries the agent fulfills via WebSearch + WebFetch. Per the standing rule on subagents, dispatch parallel sonnet agents — one per plugin OR one per query type — to:

1. Run WebSearch(query=<query>) for each query in research_targets
2. Pick the top relevant URL per result set (skip paywalls, marketing pages)
3. WebFetch(url=<url>, prompt="extract concrete sound design techniques + parameter recipes")
4. Cache the extracted text to <cache_root>/<target_type>/<n>.txt with the source URL stamped in the first line

Cap parallel subagents at 5 to avoid main-context bloat. Each subagent writes to disk + returns a one-paragraph summary.

Step P4 — AI synthesis (agent-driven, dispatch sonnet subagents)

After research is cached for a batch of plugins:

corpus_emit_synthesis_briefs(plugin_ids=[...])

Returns one synthesis_brief per plugin — a self-contained packet with all identity + manual + research data plus a YAML schema to fill. Dispatch one sonnet subagent per brief (or batches of 3 in parallel) using the Agent tool with subagent_type="general-purpose" and model="sonnet". Each subagent:

1. Reads brief["synthesis_inputs"]
2. Writes a YAML at brief["output_path"] matching brief["synthesis_schema"]
3. Returns a 200-word summary of what they wrote

Result: per-plugin ~/.livepilot/atlas-overlays/user/plugins/<plugin_id>/identity.yaml with sonic_fingerprint, reach_for, avoid, key_techniques, parameter_glossary, comparable_plugins, genre_affinity, producer_anchors.

Querying after the engine completes

The plugin identity YAMLs are picked up by the overlay loader on next MCP server boot. Then:

extension_atlas_search(query="warm pad analog", namespace="user.plugins")
extension_atlas_get(namespace="user.plugins", entity_id="u-he-diva")
atlas_chain_suggest(role="bass", genre="dub_techno")
  # may now return user-installed plugins ranked by reach_for / genre_affinity

The agent has knowledge of the user's sound, not just Ableton's defaults.

Universal extensibility

If the user has a content type the built-in scanners don't cover (Reaper .rpp, Bitwig .bwproject, custom JSON metadata files, etc.) tell them they can add a Scanner subclass to mcp_server/user_corpus/scanners/ — documented in docs/USER_CORPUS_GUIDE.md under "Scanner abstraction (write your own)". The 4 built-in scanners are the reference implementations; the pattern is plugin-style — drop in a file with @register_scanner, name a type_id, implement scan_one + derive_tags + derive_description. The runner picks it up.