# Pp Parallel > Agent-native Parallel web research with a local SQLite memory no other Parallel CLI keeps. Trigger phrases: `search the web with Parallel`, `Parallel deep research`, `FindAll companies with Parallel`, `check Parallel balance`, `use parallel`, `run parallel-pp-cli`. - Skill: `mvanhorn-printing-press-libra/pp-parallel` (Agent Skill, multi-file: 309 files) - Install (CLI): `npx skillmds add mvanhorn-printing-press-libra/pp-parallel` - Raw SKILL.md: https://api.skillmd.com/api/skills/mvanhorn-printing-press-libra/pp-parallel/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: Apache-2.0 - Author: mvanhorn (https://skillmd.com/u/mvanhorn-printing-press-libra) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/mvanhorn-printing-press-libra/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 `$HOME/.local/bin` on macOS/Linux and `%LOCALAPPDATA%\Programs\PrintingPress\bin` on Windows: ```bash npx -y @mvanhorn/printing-press-library install parallel --cli-only ``` 2. Verify: `parallel-pp-cli --version` 3. Ensure the reported install directory is on `$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 `$PATH` instead: ```bash go install github.com/mvanhorn/printing-press-library/library/ai/parallel/cmd/parallel-pp-cli@latest ``` If `--version` reports "command not found" after install, the runtime cannot see the binary directory on `$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._ ```bash 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._ ```bash 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._ ```bash 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._ ```bash 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._ ```bash 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._ ```bash 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._ ```bash 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: ```bash parallel-pp-cli which "" ``` `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 ```bash parallel-pp-cli doctor --dry-run ``` Confirm auth wiring without calling paid endpoints ### Recall then decide ```bash 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 ```bash parallel-pp-cli monitors digest --since 7d --json --agent ``` Mechanical triage of monitor events ### Promote FindAll to enrichment ```bash parallel-pp-cli findall promote --findall-id findall_demo --limit 5 --json --agent ``` Entity discovery into Task Group ### Balance burn check ```bash 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: ```bash 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: ```json { "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."}, "results": } ``` 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 ` for one invocation, or set `PARALLEL_HOME=` 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: ```json { "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: ```bash parallel-pp-cli recall "" --agent ``` The response envelope: ```json { "query": "...", "normalized": "", "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": ["", "parallel-pp-cli learnings confirm 12"] } ], "playbook": { "query_family": "...", "playbook": { "steps": [ { "cmd": "", "purpose": "..." } ], "entity_slots": ["$ENTITY"], "expected_tool_calls": 3 }, "slots_resolved": { "$ENTITY": { "token": "", "canonical": "" } }, "notes": "" }, "notes": "" } ``` 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 ` only after the trial verified the behavior. Reject a wrong candidate with `learnings reject `. -> 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 ` prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. `learnings reject ` 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:` (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: ```bash parallel-pp-cli teach --query "" --resource-type --resource --resource # (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: ```bash # Common case: record both the resource learning AND the playbook in one call. parallel-pp-cli teach \ --query "" \ --resource \ --playbook-file ~/playbooks/.json \ --playbook-notes-file ~/playbooks/-notes.md # (append shell `&` to background it) # Alternate: playbook-only (no resource to record alongside). parallel-pp-cli teach-playbook \ --query "" \ --playbook-file ~/playbooks/.json \ --notes-file ~/playbooks/-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`. ```bash parallel-pp-cli playbook amend \ --query "" \ --add-note "" # (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]: `). 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 `. 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: | Sink | Effect | |------|--------| | `stdout` | Default; write to stdout only | | `file:` | Atomically write output to `` (tmp + rename) | | `webhook:` | POST the output body to the URL (`application/json` or `application/x-ndjson` when `--compact`) | 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 | Code | Meaning | |------|---------| | 0 | Success | | 2 | Usage error (wrong arguments) | | 3 | Resource not found | | 4 | Authentication required | | 5 | API error (upstream issue) | | 7 | Rate limited (wait and retry) | | 10 | Config error | ## 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: ```bash go install github.com/mvanhorn/printing-press-library/library/ai/parallel/cmd/parallel-pp-mcp@latest ``` 2. Register with Claude Code: ```bash 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: ```bash parallel-pp-cli [subcommand] [args] --agent ``` 4. If ambiguous, drill into subcommand help: `parallel-pp-cli --help`.