Pp Parallel

Parallel — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the parallel-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 parallel --cli-only
   

2. Verify: parallel-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/ai/parallel/cmd/parallel-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.

Search, extract, deep research, FindAll, monitors, and Account balance/apps/keys in one Go binary. Session stitch, research recall, and monitor digests compound every run into offline memory. Product API key auth stays separate from Account OAuth so headline search never requires a dashboard login.

When to Use This CLI

Use this CLI for agent-native Parallel web research loops, offline recall of past searches/runs, FindAll-to-enrichment pipelines, monitor digests, and dual-auth Account admin. Prefer official parallel-cli when you need its YAML enrich planner or DuckDB/BigQuery deploy integrations.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI as a general web browser or scraper outside Parallel APIs
• Do not use Account balance add for large unattended spends without reviewing idempotency keys
• Do not prefer this over Parallel Search MCP for one-off free anonymous search experiments

Unique Capabilities

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

Local state that compounds

• session stitch — Bind search, extract, and task/findall runs into one local session chain for agent resume.

  Use when an agent needs to resume a multi-step Parallel research loop without re-fetching.

  parallel-pp-cli session stitch --search-id search_demo --json --agent
  

• monitors digest — Mechanical per-monitor event counts and top titles since a duration window.

  Use for Monday triage of which monitors fired or went quiet.

  parallel-pp-cli monitors digest --since 7d --json --agent
  

• research recall — FTS across local searches, extracts, and task summaries with typed hit IDs.

  Use before paying for a live search when prior local research may already answer.

  parallel-pp-cli research recall --query "Anthropic funding" --json --agent --select hits.source,hits.id,hits.title
  

Spend control

• tasks guard — Refuse Task creates when prepaid balance is below a threshold.

  Use before expensive Task Groups when you must avoid surprise credit burn.

  parallel-pp-cli tasks guard --min-balance 500 --dry-run --json --agent
  

• balance burn — Diff local balance snapshots against local run volume over a window.

  Use when explaining weekly credit burn without opening the dashboard.

  parallel-pp-cli balance burn --since 7d --json --agent
  

Research pipelines

• findall promote — Turn FindAll candidates into a Task Group enrichment job.

  Use when entity discovery should immediately become batch enrichment.

  parallel-pp-cli findall promote --findall-id findall_demo --limit 10 --json --agent
  

• tasks lineage — Print the offline previous_interaction_id follow-up chain for a run.

  Use when debugging multi-turn deep research context chains offline.

  parallel-pp-cli tasks lineage trun_demo --json --agent
  

Command Reference

chat — Manage chat

• parallel-pp-cli chat — Chat completions. This endpoint can be used to get realtime chat completions.

extract — Extract returns excerpts or full content from one or more URLs. Inputs are a list of URLs and an optional search objective and keyword queries. The returned excerpts or full content is formatted as markdown and suitable for LLM consumption.

• Result: excerpts or full content from the URL formatted as markdown

• parallel-pp-cli extract — Extracts relevant content from specific web URLs.

findall — The FindAll API discovers and evaluates entities that match complex criteria from natural language objectives. Submit a high-level goal and the service automatically generates structured match conditions, discovers relevant candidates, and evaluates each against the criteria. Returns comprehensive results with detailed reasoning, citations, and confidence scores for each match decision. Streaming events and webhooks are supported.

• parallel-pp-cli findall cancel-run — Cancel a FindAll run.
• parallel-pp-cli findall enrich-run — Add an enrichment to a FindAll run.
• parallel-pp-cli findall entity-search — Return ranked entities matching a natural language objective.
• parallel-pp-cli findall extend-run — Extend a FindAll run by adding additional matches to the current match limit.
• parallel-pp-cli findall get-events — Stream events from a FindAll run.
• parallel-pp-cli findall get-result — Retrieve the FindAll run result at the time of the request.
• parallel-pp-cli findall get-schema — Get FindAll Run Schema
• parallel-pp-cli findall ingest-run — Transforms a natural language search objective into a structured FindAll spec.
• parallel-pp-cli findall runs-v1 — Starts a FindAll run. This endpoint immediately returns a FindAll run object with status set to 'queued'.
• parallel-pp-cli findall runs-v1-get — Retrieve FindAll Run Status

monitors — The Monitor API watches the web for material changes on a fixed frequency. Each monitor runs once on creation and then on its configured schedule, emitting events when meaningful changes are detected.

• event_stream monitors track a search query and emit an event for each new material change.
• snapshot monitors track a specific task run's output and emit an event when the output changes.

Results can be polled via the events endpoint or delivered via webhooks.

• parallel-pp-cli monitors create — Create a monitor. Monitors run on a fixed frequency to detect material changes in web content.
• parallel-pp-cli monitors list — List monitors ordered by creation time, newest first. Monitors are sorted by created_at descending.
• parallel-pp-cli monitors retrieve — Retrieve a monitor. Retrieves a specific monitor by monitor_id.

service — Service utility endpoints

• parallel-pp-cli service account-add-balance — Charge the organization's default payment method and add the amount to the prepaid credit balance.
• parallel-pp-cli service account-create-app — Create a new app for the authenticated organization
• parallel-pp-cli service account-create-key — Create a new API key for an app
• parallel-pp-cli service account-delete-app — Delete an app from the authenticated organization
• parallel-pp-cli service account-delete-key — Delete an API key from an app
• parallel-pp-cli service account-get-balance — Get the authenticated organization's prepaid credit balance
• parallel-pp-cli service account-list-apps — List all apps for the authenticated organization

tasks — The Task API executes web research and extraction tasks. Clients submit a natural-language objective with an optional input schema; the service plans retrieval, fetches relevant URLs, and returns outputs that conform to a provided or inferred JSON schema. Supports deep research style queries and can return rich structured JSON outputs. Processors trade-off between cost, latency, and quality. Each processor supports calibrated confidences.

• Output metadata: citations, excerpts, reasoning, and confidence per field

Task Groups enable batch execution of many independent Task runs with group-level monitoring and failure handling.

• Submit hundreds or thousands of Tasks as a single group

• Observe group progress and receive results as they complete

• Real-time updates via Server-Sent Events (SSE)

• Add tasks to an existing group while it is running

• Group-level retry and error aggregation

• parallel-pp-cli tasks runs-events-get — Streams events for a task run. Returns a stream of events showing progress updates and state changes for the task run.

• parallel-pp-cli tasks runs-events-get-runs — Streams events for a task run. Returns a stream of events showing progress updates and state changes for the task run.

• parallel-pp-cli tasks runs-get — Retrieves run status by run_id. The run result is available from the /result endpoint.

• parallel-pp-cli tasks runs-input-get — Retrieves the input of a run by run_id.

• parallel-pp-cli tasks runs-post — Initiates a task run. Returns immediately with a run object in status 'queued'.

• parallel-pp-cli tasks runs-result-get — Retrieves a run result by run_id, blocking until the run is completed.

• parallel-pp-cli tasks sessions-events-get — Streams events from a TaskGroup: status updates and run completions.

• parallel-pp-cli tasks taskgroups-get — Retrieves aggregated status across runs in a TaskGroup.

• parallel-pp-cli tasks taskgroups-post — Initiates a TaskGroup to group and track multiple runs.

• parallel-pp-cli tasks taskgroups-runs-get — Retrieves task runs in a TaskGroup and optionally their inputs and outputs.

• parallel-pp-cli tasks taskgroups-runs-id-get — Retrieves run status by run_id.

• parallel-pp-cli tasks taskgroups-runs-post — Initiates multiple task runs within a TaskGroup.

websearch — Manage websearch

• parallel-pp-cli websearch — Searches the web. The legacy Search API reference (/v1beta/search endpoint) is available [here](https://docs.parallel.

Finding the right command

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

parallel-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

Doctor before spend

parallel-pp-cli doctor --dry-run

Confirm auth wiring without calling paid endpoints

Recall then decide

parallel-pp-cli research recall "Anthropic" --json --agent --select hits.source,hits.id,hits.title

Check local memory before paying for live Search

Monitor weekly digest

parallel-pp-cli monitors digest --since 7d --json --agent

Mechanical triage of monitor events

Promote FindAll to enrichment

parallel-pp-cli findall promote --findall-id findall_demo --limit 5 --json --agent

Entity discovery into Task Group

Balance burn check

parallel-pp-cli balance burn --since 7d --json --agent

Explain weekly credit burn from local snapshots

Auth Setup

Product commands use PARALLEL_API_KEY via the x-api-key header. Account commands (balance, apps, keys) need an OAuth device-flow Bearer JWT (see docs.parallel.ai/integrations/account-api); a Product API key alone cannot call Account endpoints. Never commit API key values.

Run parallel-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. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

  parallel-pp-cli monitors list --agent --select id,name,status
  

• 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

• Explicit retries — use --idempotent only when an already-existing create should count as success, and use --ignore-missing only when a missing delete target should count as success

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 PARALLEL_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: PARALLEL_CONFIG_DIR, PARALLEL_DATA_DIR, PARALLEL_STATE_DIR, PARALLEL_CACHE_DIR.

• Resolution order is per-kind env var, --home, PARALLEL_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 credentials.toml, data.db, cookies, and auth sidecars. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.

• Stored secrets live in credentials.toml under the data dir. Existing legacy config.toml secrets are read for compatibility and leave config.toml on the first auth write.

• Run parallel-pp-cli doctor --fail-on warn to surface path and credential-location 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": {
      "parallel": {
        "command": "parallel-pp-mcp",
        "env": {
          "PARALLEL_HOME": "/srv/parallel"
        }
      }
    }
  }
  

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use PARALLEL_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 PARALLEL_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:

parallel-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>", "parallel-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 `parallel-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; parallel-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 parallel-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.

Step 4: teach & after finalizing your response - always

Teaching is unconditional. After resolving a query the store could not answer, background-teach the final resource mapping - no call-count threshold, no judging whether it was "worth" learning. The teach is the anchor of the loop: it triggers playbook synthesis for a family without a playbook, and same-referent phrasings fold into one family so near-duplicate teaches do not fragment the store. Fire it after assembling your user-facing response but BEFORE emitting it, with a shell & so the call returns immediately:

parallel-pp-cli teach --query "<user's question>" --resource-type <type> --resource <id1> --resource <id2>
# (append shell `&` to background it)

Silent on success. Errors only land in teach.log under the resolved state dir. Teach the most specific resource - if the user asked a broad question and you walked through parent records to find the specific answer, teach the leaf id, not the parent. The CLI uses seeded entity_lookups for cross-alias resolution at recall time, so a teach under one alias (e.g., "Niners") satisfies future queries under another alias (e.g., "49ers", "San Francisco") automatically.

PII rule: teach the structural question with identifiers stripped - never include names, emails, phone numbers, account ids, or other personal identifiers in taught queries or notes. The CLI scans teach queries for obvious email/phone shapes and warns, but does not block; strip before teaching rather than relying on the warning.

Step 5: playbooks - optional flags, automatic synthesis

You do not need to decide whether a session "deserves" a playbook: a teach on a family without one auto-synthesizes a playbook_candidate from the session's journal, and the next session judges it via confirm/reject. Attach explicit playbook flags only when you already hold choreography worth recording verbatim - workarounds the CLI didn't surface (silently-dropped flags, undocumented params, pagination tricks, payload gotchas). Prefer the integrated one-call form - record the resource learning and the playbook in the same teach invocation:

# Common case: record both the resource learning AND the playbook in one call.
parallel-pp-cli teach \
  --query "<user's question>" \
  --resource <id> \
  --playbook-file ~/playbooks/<shape>.json \
  --playbook-notes-file ~/playbooks/<shape>-notes.md
# (append shell `&` to background it)

# Alternate: playbook-only (no resource to record alongside).
parallel-pp-cli teach-playbook \
  --query "<user's question>" \
  --playbook-file ~/playbooks/<shape>.json \
  --notes-file ~/playbooks/<shape>-notes.md

Playbook files are JSON with steps, entity_slots, expected_tool_calls. Notes files are markdown carrying the gotchas verbatim. File-free callers (MCP-only agents) pass the same content inline: --playbook-json and --playbook-notes on the integrated teach form, --playbook-json and --notes on teach-playbook. On the integrated teach form, the playbook flags are optional - omit them entirely for a resource-only teach. On the standalone teach-playbook form, at least one of the playbook and notes flags must be set; both empty is rejected. Playbooks are keyed on the structural query family (entities stripped) so a recipe taught from one entity-shaped query applies to every other query of the same shape, with slots_resolved binding the live query's canonical at recall time.

When you DO find a playbook on a future recall, treat it as ground truth: replay the steps with slots_resolved substitutions, skip the discovery that the choreography already documents, and read notes before any step.

Step 6: playbook amend & when your debug response identifies a correction

If your debug-protocol response identifies a concrete correction the notes or playbook should know — a workaround, an undocumented endpoint shape, a stale field name, observed schema drift, an empty-payload fallback — fire playbook amend BEFORE emitting your user-facing response. Same fire-and-forget posture as teach.

parallel-pp-cli playbook amend \
  --query "<exact recall query string>" \
  --add-note "<your concrete correction>"
# (append shell `&` to background it)

What counts as worth amending: a behavior you OBSERVED this session that future-you would benefit from knowing. Examples worth amending:

• A workaround for a CLI surface that silently drops or misorders a flag.
• An undocumented endpoint shape (response wrapped in {meta, results}, payload nested two levels deeper than the docs claim).
• Observed schema drift (a field renamed, an index that shifted between seasons, a category label that the API now returns lower-cased).

What does NOT belong in notes:

• The year-specific or entity-specific answer to the user's question. That's the response, not a learning.
• Per-team / per-athlete / per-row data the playbook already retrieves at runtime.
• Statements that paraphrase what the existing notes already say.

The amend command appends to the family's existing notes with a timestamped marker ([amend YYYY-MM-DDTHH:MMZ]: <text>). Multiple amends accumulate; the audit trail is visible. If no playbook exists yet for the family, amend creates a notes-only one (so cold-start corrections still land).

PII discipline for amend notes

playbook amend notes are designed to potentially flow upstream as shared knowledge in future versions of the Printing Press. Keep them clean of user-identifying content so the upstream-contribution path stays open without retroactive scrubbing:

• Do NOT embed paths to user filesystems, personal API keys or tokens, user email addresses, user GitHub handles, or specific query histories tied to a single user.
• Acceptable: endpoint shapes, undocumented field names, API gotchas, observed schema drift, workarounds for CLI surfaces, generalizable pagination or retry tactics.

If a correction is only meaningful with user-specific context, it belongs in a personal note, not in the playbook amend.

Measuring the loop

parallel-pp-cli learnings stats reports recall hit rate, teach-to-reuse, playbook resolution rate, and candidate confirm/reject counts from the local learn_events table. Rates are null until they have a denominator; everything stays on this machine. Use it to check whether the loop is earning its keep for this CLI.

Disabling learning

• --no-learn on a single command short-circuits both recall and the teach write path. Use for deterministic agent flows or tests that must not be affected by accumulated learnings.
• PARALLEL_NO_LEARN=true in the environment globally disables the pipeline.

Agent Feedback

When you (or the agent) notice something off about this CLI, record it:

parallel-pp-cli feedback "the --since flag is inclusive but docs say exclusive"
parallel-pp-cli feedback --stdin < notes.txt
parallel-pp-cli feedback list --json --limit 10

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless PARALLEL_FEEDBACK_ENDPOINT is set AND either --send is passed or PARALLEL_FEEDBACK_AUTO_SEND=true. Default behavior is local-only.

Write what surprised you, not a bug report. Short, specific, one line: that is the part that compounds.

Output Delivery

Every command accepts --deliver <sink>. The output goes to the named sink in addition to (or instead of) stdout, so agents can route command results without hand-piping. Three sinks are supported:

Unknown schemes are refused with a structured error naming the supported set. Webhook failures return non-zero and log the URL + HTTP status on stderr.

Named Profiles

A profile is a saved set of flag values, reused across invocations. Use it when a scheduled or recurring agent reuses the same saved flags while providing different input each run.

parallel-pp-cli profile save briefing --json
parallel-pp-cli --profile briefing monitors list
parallel-pp-cli profile list --json
parallel-pp-cli profile show briefing
parallel-pp-cli profile delete briefing --yes

Explicit flags always win over profile values; profile values win over defaults. agent-context lists all available profiles under available_profiles so introspecting agents discover them at runtime.

Exit Codes

Argument Parsing

Parse $ARGUMENTS:

1. Empty, help, or --help → show parallel-pp-cli --help output
2. Starts with install → ends with mcp → MCP installation; otherwise → see Prerequisites above
3. Anything else → Direct Use (execute as CLI command with --agent)

MCP Server Installation

1. Install the MCP server:

   go install github.com/mvanhorn/printing-press-library/library/ai/parallel/cmd/parallel-pp-mcp@latest
   

2. Register with Claude Code:

   claude mcp add parallel-pp-mcp -- parallel-pp-mcp
   

3. Verify: claude mcp list

Direct Use

1. Check if installed: which parallel-pp-cli If not found, offer to install (see Prerequisites at the top of this skill).
2. Match the user query to the best command from the Unique Capabilities and Command Reference above.
3. Execute with the --agent flag:

   parallel-pp-cli <command> [subcommand] [args] --agent
   

4. If ambiguous, drill into subcommand help: parallel-pp-cli <command> --help.