Pp Averusa

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AVer USA — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the averusa-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:

1. Install via the Printing Press installer. It defaults binaries to $HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:

   npx -y @mvanhorn/printing-press-library install averusa --cli-only

2. Verify: averusa-pp-cli --version
3. Ensure the reported install directory is on $PATH for the agent/runtime that will invoke this skill.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.6 or newer). This installs into $GOPATH/bin (default $HOME/go/bin), so add that directory to $PATH instead:

go install github.com/mvanhorn/printing-press-library/library/devices/averusa/cmd/averusa-pp-cli@latest

If --version reports "command not found" after install, the runtime cannot see the binary directory on $PATH. Do not proceed with skill commands until verification succeeds.

averusa.com hides its manuals, spec sheets, and white papers behind a Salesforce portal maze and per-model PDFs. averusa-pp-cli syncs the whole catalog into a local database, then answers the questions integrators actually ask: which model fits (compare), what are its specs (specs), what docs exist per model (coverage, docs pack), what changed since last sync (whats-new), and which PDF links are dead (doctor).

When to Use This CLI

Use averusa-pp-cli whenever an agent or integrator needs an AVer USA user manual, spec sheet/datasheet, or white paper — browsing the type-filtered catalog, comparing models for a bid, assembling an offline job bag, or auditing which PDF links are still alive. Its local store answers cross-document questions ('which model has a white paper and which is discontinued?') that the Salesforce portal cannot.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI to control AVer PTZ or conference cameras — that is device control over VISCA/IP, which belongs to Bitfocus Companion or vendor device tools.
• Do not use it to download or install firmware — firmware binaries live on AVer's external download manager, not the portal's fileField, and firmware installation is a device operation.

Unique Capabilities

These capabilities aren't available in any other tool for this API.

Local catalog that compounds

• compare — Side-by-side spec fields for two or more AVer models from their datasheets, ready for bid comparisons and RFP tables. Spec fields come from datasheet PDFs extracted by harvest --with-specs.

  Use this instead of opening N datasheet PDFs to answer 'which model fits?' for a bid.

  averusa-pp-cli compare CAM570 CAM550 --agent

• specs — One model's full spec fields as clean text or JSON for spec-compliance tables and agent pipelines. Fields come from datasheet PDFs extracted by harvest --with-specs.

  Use this to fill an RFP spec-compliance row without opening the PDF.

  averusa-pp-cli specs CAM570 --json --agent

• whats-new — List documents and products added or updated since the last sync, filterable by age.

  Use this to track new manuals or firmware docs across a fleet without re-checking the website.

  averusa-pp-cli whats-new --since 30d --json

• coverage — Per-model doc-type availability matrix for a category, flagging which models are missing manuals, spec sheets, or white papers.

  Use this before a recommendation or commissioning checklist to catch missing compliance docs.

  averusa-pp-cli coverage conference-camera

Reachability mitigation

• docs audit — HEAD-checks every document URL in the catalog and flags 404s and soft-404 shells, caching last-checked status locally.

  Use this before pushing docs to a shared drive to catch dead or mislinked PDFs while they are still cheap to fix.

  averusa-pp-cli docs audit

Offline job-site workflow

• docs pack — Batch-download every document for a model into one offline folder with stable <model>-<type> names, with a --dry-run preview. Downloads need entityIds, which harvest resolves at sync time.

  Use this to pre-stage a job bag of manuals before driving to a site with no signal.

  averusa-pp-cli docs pack CAM570 --out ./job-570 --dry-run

Service-specific intelligence

• products status — Flag which models AVer lists as discontinued, filterable by category.

  Use this before specing a model into a school bid so a discontinued unit never ships in the quote.

  averusa-pp-cli products status --category conference-camera --json

Command Reference

harvest — Build the local corpus (run this first)

• averusa-pp-cli harvest — walk the support-portal article sitemap (737 articles), fetch each article's SSR page, resolve its Salesforce entityId, and scrape the product catalog + discontinued lists into the local corpus
• averusa-pp-cli harvest --only docs --limit 20 — narrow to a docs subset
• averusa-pp-cli harvest --only products --with-specs — also extract spec fields from datasheet PDFs (requires pdftotext)

docs — AVer USA support-portal knowledge articles and attached files

• averusa-pp-cli docs search — Type-filtered full-text search over the harvested catalog (--type user-manual|spec-sheet|white-paper|...)
• averusa-pp-cli docs list — List the harvested catalog, filterable by --type/--model (live sitemap fallback when unharvested)
• averusa-pp-cli docs get — Fetch a knowledge article as clean text (crawler-UA SSR)
• averusa-pp-cli docs download — Download an article's attached file (PDF)
• averusa-pp-cli docs audit — HEAD-check every document URL; flag 404s and soft-404 shells
• averusa-pp-cli docs pack — Batch-download every document for a model into one offline folder

products — AVer USA product catalog and datasheets

• averusa-pp-cli products list — List the harvested catalog, filterable by --category
• averusa-pp-cli products get — Fetch a product page as clean text (spec links, downloads)
• averusa-pp-cli products docs — List every document harvested for a model, plus its datasheet
• averusa-pp-cli products status — Flag models AVer lists as discontinued, filterable by category

novel commands — compare (side-by-side datasheet specs), specs (one model's spec fields), whats-new (docs/products updated since last harvest), coverage (per-model doc-type matrix)

Finding the right command

When you know what you want to do but not which command does it, ask the CLI directly:

averusa-pp-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match — fall back to --help or use a narrower query.

Recipes

Field kit before you drive out

averusa-pp-cli docs pack CAM570 --out ./job-570

One command assembles the whole offline doc bag for a model with stable names — no portal clicking at the jobsite.

Bid spec table

averusa-pp-cli specs CAM550 --json --agent

Structured spec fields straight into an RFP compliance table or agent pipeline.

Which model fits?

averusa-pp-cli compare CAM570 CAM550 --agent

Side-by-side datasheet specs so a recommendation never relies on opening two PDFs.

Audit the shared drive

averusa-pp-cli docs audit

Catches dead and mislinked PDFs before installers do — the CAM570 page currently mislinks cam520pro3-datasheet.pdf.

White papers for an eval

averusa-pp-cli docs search "white paper" --type white-paper --json --select title,doc_type,model

Evaluation material filtered to white papers, narrowed with --select so agents don't parse the full payload.

Auth Setup

No authentication required.

Run averusa-pp-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color --yes.

• Pipeable — JSON on stdout, errors on stderr

• Filterable — --select keeps a subset of fields, e.g. --select title,doc_type,model. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

  averusa-pp-cli docs list --agent --select title,doc_type,model

• Previewable — --dry-run shows the request without sending

• Offline-friendly — sync/search commands can use the local SQLite store when available

• Non-interactive — never prompts, every input is a flag

• Read-only — do not use this CLI for create, update, delete, publish, comment, upvote, invite, order, send, or other mutating requests

Response envelope

Commands that read from the local store or the API wrap output in a provenance envelope:

{
  "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."},
  "results": <data>
}

Parse .results for data and .meta.source to know whether it's live or local. A human-readable N results (live) summary is printed to stderr only when stdout is a terminal AND no machine-format flag (--json, --csv, --compact, --quiet, --plain, --select) is set — piped/agent consumers and explicit-format runs get pure JSON on stdout.

Paths and state

Agents should treat the CLI's path resolver as part of the runtime contract:

• Use --home <dir> for one invocation, or set AVERUSA_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: AVERUSA_CONFIG_DIR, AVERUSA_DATA_DIR, AVERUSA_STATE_DIR, AVERUSA_CACHE_DIR.

• Resolution order is per-kind env var, --home, AVERUSA_HOME, XDG (XDG_CONFIG_HOME, XDG_DATA_HOME, XDG_STATE_HOME, XDG_CACHE_HOME), then platform defaults.

• config contains settings like config.toml and profiles. data contains the corpus database (data.db) and harvested state. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.

• This CLI has no authentication: every AVer surface it reads is public, so no credential storage exists or is needed.

• Run averusa-pp-cli doctor --fail-on warn to surface path warnings. agent-context exposes a schema v4 paths block for agents that need the resolved dirs.

• For MCP, pass relocation through the MCP host config. The MCP binary does not inherit CLI flags:

  {
    "mcpServers": {
      "averusa": {
        "command": "averusa-pp-mcp",
        "env": {
          "AVERUSA_HOME": "/srv/averusa"
        }
      }
    }
  }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use AVERUSA_HOME or per-kind vars as durable fleet levers, and use --home only for a single invocation. Relocation is not reversible by unsetting env vars; move files manually before clearing AVERUSA_HOME, or doctor will not find credentials left under the former root.

Automatic learning

This CLI ships a self-capturing learning loop. The CLI does its own bookkeeping: every invocation is journaled locally, a failed flag followed by a corrected retry auto-derives a flag_alias candidate, and a teach on a query family without a playbook auto-synthesizes a playbook_candidate from the session's journal. Your job is judgment only: recall first, act on surfaced candidates, teach the final answer, playbook amend when you observe a correction. You never record failures by hand.

Step 1: recall before any discovery

Before list/search/drill commands on a new user question, run:

averusa-pp-cli recall "<user's question>" --agent

The response envelope:

{
  "query": "...",
  "normalized": "<normalized form>",
  "query_entities": ["..."],
  "found": true | false,
  "match_score": 0.0,
  "results": [
    { "resource_id": "...", "resource_type": "...", "venue": "...",
      "confidence": 2, "entity_match": "exact|partial|unknown",
      "source": "taught|preseed|pattern", "warnings": ["..."] }
  ],
  "mismatches": [ /* only when --debug-mismatches */ ],
  "warnings": [ /* top-level */ ],
  "candidates": [
    { "id": 12, "class": "flag_alias | playbook_candidate",
      "summary": "...", "sightings": 3, "last_seen": "...",
      "rationale": "...",
      "next_action": ["<trial command>", "averusa-pp-cli learnings confirm 12"] }
  ],
  "playbook": {
    "query_family": "...",
    "playbook": {
      "steps": [ { "cmd": "<command with {slot} substitution>", "purpose": "..." } ],
      "entity_slots": ["$ENTITY"],
      "expected_tool_calls": 3
    },
    "slots_resolved": { "$ENTITY": { "token": "<live token>", "canonical": "<canonical>" } },
    "notes": "<workarounds + gotchas for this query family>"
  },
  "notes": "<duplicate surface for non-playbook callers>"
}

Empty-store short-circuit: if the store has no learnings, playbooks, or candidates yet (recall finds nothing and learnings list and learnings candidates are both empty), skip recall for the rest of this session instead of taxing every query; resume recall-first once something has been taught.

Step 2: decision tree

Read candidates, playbook, notes, results[0], and warnings in that order:

if Candidates present (warnings include "candidates_present"):
    -> candidates are try-then-confirm, never facts. Follow each candidate's
       two-step next_action verbatim: run the trial command first, then run
       `learnings confirm <id>` only after the trial verified the behavior.
       Reject a wrong candidate with `learnings reject <id>`.
    -> NEVER re-teach something recall surfaced as a candidate; confirm or
       reject that candidate instead of teaching a duplicate.
    -> candidates ride alongside playbooks and resource hits, not instead of
       them; continue with the branches below after acting on them.

if Playbook present:
    -> READ Playbook.notes verbatim FIRST (workarounds + gotchas the CLI surface doesn't expose)
    -> replay Playbook.steps in order, substituting Playbook.slots_resolved entries
       for the entity slot tokens. If a step's slot is unresolved, fall back to
       discovery for that step only.
    -> the Playbook's expected_tool_calls is a budget; if you find yourself running
       materially more, record the divergence via `averusa-pp-cli playbook amend`
       at end-of-session.

elif Notes present (no Playbook):
    -> read Notes verbatim before any discovery step; they carry known gotchas
       for this query family even when no structured choreography exists yet.

elif Found AND Results[0].EntityMatch == "exact" AND Results[0].Confidence >= 2:
    -> skip discovery; fetch live data for Results[*].ResourceID in parallel

elif Found AND Results[0].EntityMatch == "partial":
    -> candidate hint, NOT a hit; read the resource title to validate before trusting

elif (any row in Mismatches[] when --debug-mismatches was passed):
    -> treat as cold start; the stored learning is for a different entity
       (different canonical resolved from query_entities)

else:  // Found == false, no playbook, no notes
    -> cold start; run discovery normally; teach the answer afterward (Step 4).
       If the family has no playbook yet, that teach auto-synthesizes a
       playbook candidate from this session's journal - you do not need to
       record one by hand.

Playbook and Notes are orthogonal to the per-resource path. A recall response can carry both a Playbook AND a Results[] hit - use both: the Playbook tells you which choreography to run; the resource hits short-circuit specific steps. Default to skipping mismatches; pass --debug-mismatches only when investigating cold-start surprises.

Candidate judgment details: learnings confirm <id> prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. learnings reject <id> tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; averusa-pp-cli learnings candidates lists the full open set.

Graceful degradation: if learnings confirm is an unknown command, you are driving an older binary - ignore the candidates guidance and follow the rest of the protocol.

Step 3: always read warnings

• low_confidence: row exists at confidence<2. Treat as a hint, not a skip-discovery hit.
• resource_not_in_store: the local store doesn't have the resource the learning points at. The match validator couldn't classify entities — direct-fetch and re-evaluate.
• cross_alias_match (per-result): the row was taught under a different alias and matched the live query's canonical via entity_lookups (e.g., a "USA" teach satisfying a "United States" recall). Trust the resource_id.
• similar_shape_different_entity:<canonical> (top-level): a structurally matching row exists but its canonical entity differs from the live query's. Treated as cold start; the warning carries the conflicting canonical as a hint, but the row is NOT promoted into Results.
• ambiguous_alias (top-level): a single query entity resolved to multiple canonicals (e.g., "Cards" → Arizona Cardinals + St. Louis Cardinals). Surface the ambiguity from context before committing to a resource.
• candidates_present (top-level): the envelope carries a candidates section. Handle it via the candidates branch in Step 2 before anything else.
• lookup_refresh_available (top-level): an entity in the query has no lookup row yet, but synced data could provide one. Run averusa-pp-cli sync to refresh entity lookups.
• Top-level no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.