name

pp-plane

description

Every Plane workspace as a typed, offline-syncable CLI — plus issue relations and module membership the REST API hides.

author

Anton Sidorov aka anticodeguy

license

Apache-2.0

argument-hint

<command> [args] | install cli|mcp

allowed-tools

Read Bash

metadata

openclaw

requires

install

bins

plane-pp-cli

kind

bins

module

plane-pp-cli

github.com/mvanhorn/printing-press-library/library/project-management/plane/cmd/plane-pp-cli

Plane — Printing Press CLI

Prerequisites: Install the CLI

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

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 plane --cli-only
```
Verify: plane-pp-cli --version
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/project-management/plane/cmd/plane-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.

The Plane REST API

Visit our quick start guide and full API documentation at developers.plane.so.

Unique Capabilities

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

relations — List, create, and delete work-item relations (blocking, blocked_by, duplicate, relates_to, temporal).
module — Module-membership helpers: cache enrichment (sync) and assign-on-create. Plane's issue API never returns module membership, so sync enriches the local cache at its tail.
attach-file — Attach a local file to a work item (presign, upload, mark uploaded) in one step; the generated commands expose only the presign and mark steps. Example: plane-pp-cli attach-file <issue_id> <project_id> ./report.pdf

Command Reference

assets — File Upload & Presigned URLs

Generate presigned URLs for direct file uploads to cloud storage. Handle user avatars, cover images, and generic project assets with secure upload workflows.

Key Features:

Generate presigned URLs for S3 uploads
Support for user avatars and cover images
Generic asset upload for projects
File validation and size limits

Use Cases: User profile images, project file uploads, secure direct-to-cloud uploads.

plane-pp-cli assets create-generic-upload — Generate presigned URL for generic asset upload
plane-pp-cli assets create-user-upload — Generate presigned URL for user asset upload
plane-pp-cli assets delete-user — Delete user asset. Delete a user profile asset (avatar or cover image) and remove its reference from the user profile.
plane-pp-cli assets get-generic — Get presigned URL for asset download
plane-pp-cli assets update-generic — Update generic asset after upload completion
plane-pp-cli assets update-user — Mark user asset as uploaded

invitations — Manage invitations

plane-pp-cli invitations workspaces-create — Create a workspace invite
plane-pp-cli invitations workspaces-destroy — Delete a workspace invite
plane-pp-cli invitations workspaces-list — List all workspace invites for a workspace
plane-pp-cli invitations workspaces-partial-update — Update a workspace invite
plane-pp-cli invitations workspaces-retrieve — Get a workspace invite by ID

issues — Manage issues

plane-pp-cli issues get-workspace-work-item — Retrieve a specific work item using workspace slug, project identifier, and issue identifier.
plane-pp-cli issues search-work-items — Perform semantic search across issue names, sequence IDs, and project identifiers.

members — Team Member Management

Manage team members, roles, and permissions within projects and workspaces. Control access levels and track member participation.

Key Features:

Invite and manage team members
Assign roles and permissions
Control project and workspace access
Track member activity and participation

Use Cases: Team setup, access control, role management, collaboration.

plane-pp-cli members — Retrieve all users who are members of the specified workspace.

projects — Project Management

Create and manage projects to organize your development work. Configure project settings, manage team access, and control project visibility.

Key Features:

Create, update, and delete projects
Configure project settings and preferences
Manage team access and permissions
Control project visibility and sharing

Use Cases: Project setup, team collaboration, access control, project configuration.

plane-pp-cli projects create — Create a new project in the workspace with default states and member assignments.
plane-pp-cli projects delete — Permanently remove a project and all its associated data from the workspace.
plane-pp-cli projects list — Retrieve all projects in a workspace or get details of a specific project.
plane-pp-cli projects retrieve — Retrieve details of a specific project.
plane-pp-cli projects update — Partially update an existing project's properties like name, description, or settings.

stickies — Manage stickies

plane-pp-cli stickies create-sticky — Create a new sticky in the workspace
plane-pp-cli stickies delete-sticky — Delete a sticky by its ID
plane-pp-cli stickies list — List all stickies in the workspace
plane-pp-cli stickies retrieve-sticky — Retrieve a sticky by its ID
plane-pp-cli stickies update-sticky — Update a sticky by its ID

users — Current User Information

Get information about the currently authenticated user including profile details and account settings.

Key Features:

Retrieve current user profile
Access user account information
View user preferences and settings
Get authentication context

Use Cases: Profile display, user context, account information, authentication status.

plane-pp-cli users — Retrieve the authenticated user's profile information including basic details.

work-items — Work Items & Tasks

Create and manage work items like tasks, bugs, features, and user stories. The core entities for tracking work in your projects.

Key Features:

Create, update, and manage work items
Assign to team members and set priorities
Track progress through workflow states
Set due dates, estimates, and relationships

Use Cases: Bug tracking, task management, feature development, sprint planning.

plane-pp-cli work-items get-workspace-2 — Retrieve a specific work item using workspace slug, project identifier, and issue identifier.
plane-pp-cli work-items search-2 — Perform semantic search across issue names, sequence IDs, and project identifiers.

Finding the right command

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

plane-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.

Auth Setup

Run plane-pp-cli auth setup to print the URL and steps for getting a key (add --launch to open the URL). Then set:

export PLANE_API_KEY_AUTHENTICATION="<your-key>"

To persist credentials, use plane-pp-cli auth set-token <token>. Stored secrets live in credentials.toml under the data dir, not in config.toml.

Run plane-pp-cli doctor to verify setup.

Workspace targeting

Plane's REST API is workspace-scoped: every request goes to …/api/v1/workspaces/<slug>/…. The public API cannot enumerate a user's workspaces from an API key, so the slug is user-supplied — take it from the browser URL (app.plane.so/<slug>/). Keep base_url templated as https://<host>/api/v1/workspaces/{slug} (literal {slug}); do not bake a concrete slug into it (that pins the CLI to one workspace and is flagged by doctor).

The active workspace is chosen by precedence: --workspace <slug> flag > PLANE_SLUG env > default_workspace (config).

Hitting an unexpected 403/empty result? Run echo $PLANE_SLUG first. A process-wide PLANE_SLUG exported in your shell silently overrides default_workspace, so commands target that workspace and items in any other one come back as 403 "You do not have permission" — a wrong-workspace symptom masquerading as a permissions problem, not a key issue. Either unset it, set it per-project (e.g. PLANE_SLUG=<slug> in the project's env), or pass --workspace <slug> explicitly (the flag always wins). plane-pp-cli workspaces current shows the active slug and where it was resolved from.

# One-time onboarding: probe + enroll your slug(s), write a templated base_url
plane-pp-cli init --host https://api.plane.so acme bravo --default acme

# Or manage the local registry directly
plane-pp-cli workspaces add acme bravo   # access-probes each before saving
plane-pp-cli workspaces use acme         # probe + set as the default
plane-pp-cli workspaces list             # show enrolled workspaces (the API can't list them for you)
plane-pp-cli workspaces current          # show active slug + where it was resolved from

# Target a specific workspace for one command (overrides env + default)
plane-pp-cli members --workspace bravo --agent --select display_name

Enrollment is local-only (a [[workspaces]] registry in config.toml) because the API can't enumerate workspaces by key. A bad slug fails loudly (the probe rejects it with a non-zero exit) rather than silently returning the wrong workspace.

Via the MCP server: the plane_execute tool takes an optional top-level workspace argument — the MCP twin of --workspace, same top precedence over PLANE_SLUG/default_workspace. Pass it to target one call at a specific workspace; omit it to use the configured default. (Without it the server resolves the slug from PLANE_SLUG/default_workspace at load time, so the same 403-as-wrong-workspace trap above applies to MCP calls.)

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:
```
plane-pp-cli projects 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 PLANE_HOME=<dir> to relocate all four path kinds under one root.
Use per-kind env vars only when a specific kind must diverge: PLANE_CONFIG_DIR, PLANE_DATA_DIR, PLANE_STATE_DIR, PLANE_CACHE_DIR.
Resolution order is per-kind env var, --home, PLANE_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 plane-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": {
    "plane": {
      "command": "plane-pp-mcp",
      "env": {
        "PLANE_HOME": "/srv/plane"
      }
    }
  }
}

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

plane-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>", "plane-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 `plane-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; plane-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 plane-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:

plane-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.
plane-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).
plane-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.

plane-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

plane-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.
PLANE_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:

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

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless PLANE_FEEDBACK_ENDPOINT is set AND either --send is passed or PLANE_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:

Sink	Effect
`stdout`	Default; write to stdout only
`file:<path>`	Atomically write output to `<path>` (tmp + rename)
`webhook:<url>`	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.

plane-pp-cli profile save briefing --json
plane-pp-cli --profile briefing projects list
plane-pp-cli profile list --json
plane-pp-cli profile show briefing
plane-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:

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

MCP Server Installation

Install the MCP server:

go install github.com/mvanhorn/printing-press-library/library/project-management/plane/cmd/plane-pp-mcp@latest

claude mcp add plane-pp-mcp -- plane-pp-mcp

Verify: claude mcp list

Direct Use

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

Execute with the --agent flag:

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

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

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File metadata and controls

Plane — Printing Press CLI

Prerequisites: Install the CLI

Unique Capabilities

Command Reference

Finding the right command

Auth Setup

Workspace targeting

Agent Mode

Response envelope

Paths and state

Automatic learning

Step 1: recall before any discovery

Step 2: decision tree

Step 3: always read warnings

Step 4: teach & after finalizing your response - always

Step 5: playbooks - optional flags, automatic synthesis

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

PII discipline for amend notes

Measuring the loop

Disabling learning

Agent Feedback

Output Delivery

Named Profiles

Exit Codes

Argument Parsing

MCP Server Installation

Direct Use

Step 1: `recall` before any discovery

Step 3: always read `warnings`

Step 4: `teach &` after finalizing your response - always

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