pp-forkable

Forkable — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the forkable-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 forkable --cli-only
```
Verify: forkable-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/food-and-dining/forkable/cmd/forkable-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.

Forkable exposes no public API. This CLI reverse-engineers the my-account app's GraphQL surface into a Go binary with clean read commands and agent-native output. On top of the raw reads it adds longitudinal views the product never shows — served-meal history, preference-vs-served drift, spend trends, allowance utilization, venue rotation, and a week-ahead digest — all fetched live from Forkable. It also exposes the my-account app's own meal-management mutations as meal set, meal set-all, meal confirm, meal skip, and reorder; these are dry-run by default and only place, confirm, or skip real orders when you pass --confirm.

When to Use This CLI

Use this CLI when an agent or script needs to query a Forkable office-lunch program from the terminal: what meals were served, how much was spent, which venues rotate, and whether served meals match stated dietary preferences. It is the only programmatic surface for Forkable and the only way to get longitudinal history the web app does not aggregate. It can also set, confirm, and skip upcoming meal deliveries on the user's behalf — those commands are dry-run by default and require --confirm to apply. All commands fetch live from Forkable (no local sync step).

Anti-triggers

Do not use this CLI for:

Do not place, change, or cancel meal orders without explicit user intent — the meal set, meal set-all, meal confirm, meal skip, and reorder commands mutate the real account and require --confirm; run them dry-run first and confirm the printed mutation before adding --confirm.
Do not use this CLI to add or remove club members or manage billing — use the Forkable web app.
Do not use this CLI for real-time delivery tracking notifications — those come via Forkable's email/Slack/SMS.

Unique Capabilities

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

Local history that compounds

served-history — See every meal actually served to you over time, with date, venue, price, and dietary level.

Reach for this when an agent needs a longitudinal view of what a person has eaten, not just the current delivery.
```
forkable-pp-cli served-history --since 90d --agent
```
preference-drift — Flag served meals that violate your stated dislikes or dietary restrictions, or miss your likes.

Use this to audit whether auto-selection is actually honoring dietary preferences over time.
```
forkable-pp-cli preference-drift --since 60d --agent
```
venue-rotation — Rank venues by how often they've served you and how recently.

Use this to spot venue fatigue or under-used favorites across the whole synced window.
```
forkable-pp-cli venue-rotation --since 120d --agent
```

Making the opaque legible

why-picked — Explain why a delivery's meal was auto-selected by ranking candidate items and their scores.

Pick this to explain a single day's auto-selected meal; use preference-drift for aggregate conformance.
```
forkable-pp-cli why-picked --delivery 1219480 --agent
```

Finance and allowances

spend-trend — Bucket lunch spend into per-week or per-month totals with CSV export.

Reach for this when finance needs a time series of lunch cost, not a single delivery receipt.
```
forkable-pp-cli spend-trend --since 6mo --by month --csv
```
allowance-burn — Show granted-vs-consumed allowance utilization per club, including multi-club comparison.

Use this to see which teams are over- or under-using their lunch budget.
```
forkable-pp-cli allowance-burn --by club --csv
```

Agent-native plumbing

upcoming-digest — One agent-shaped line per upcoming day: date, venue, auto-selected item, price, allowance headroom.

Pick this for a quick 'what's coming this week' summary an agent can read in one shot.
```
forkable-pp-cli upcoming-digest --agent
```

Command Reference

account —

forkable-pp-cli account — Show the authenticated Forkable user: profile, roles, dietary preferences (likes/dislikes/restrictions), companies

buffet_addresses —

forkable-pp-cli buffet-addresses — List your buffet delivery addresses (street, city, postal code, coordinates, club).

clubs —

forkable-pp-cli clubs — List meal clubs (teams/offices) you belong to or manage, with delivery address, delivery days, allowances

csrf —

forkable-pp-cli csrf — Fetch a CSRF token. Read-only.

deliveries —

forkable-pp-cli deliveries in-progress-ids — List IDs of deliveries currently in progress.
forkable-pp-cli deliveries list — List your meal deliveries from a given date forward, including per-delivery orders, chosen menu items, receipts

meal_scores —

forkable-pp-cli meal-scores — Show meal auto-selection scores (menuId, itemId, score) for a delivery and user across candidate menus.

menus —

forkable-pp-cli menus — Get menu(s) with venue, sections, items, prices, dietary levels, ratings, and modifiers.

notifications —

forkable-pp-cli notifications — List account notifications shown in the my-account app (title, description, links, publish window).

venue_usage —

forkable-pp-cli venue-usage — Get per-venue usage keyed by venue id over a date range. Requires venue ids and from/to dates inlined in the query.

Stale default dates on deliveries list and venue-usage. These two raw GraphQL passthrough commands ship a default --query with an example date baked directly into the query text. Running either with no flags can silently return an empty or misleading result (e.g. deliveries list with no overrides returns {"count":0} even when real deliveries exist) rather than an error. Always override the date argument with a real date — today's date for forward-looking reads, or a far-past date (e.g. 2000-01-01, as served-history/allowance-burn already do internally) for full-history reads — before trusting a zero/empty result from either command. Other passthrough reads (menus, meal-scores) instead ship placeholder ids that need replacing — see each command's own entry above.

meal management (writes) — place real orders and spend; dry-run by default, pass --confirm to apply.

forkable-pp-cli meal set <deliveryId> --item <id> --menu <id> [--modifier <modifierId>:<optionId>] [--replace-piece <uuid>] [--note <text>] [--confirm] — Override the auto-picked meal for one delivery day (replacePiece). --replace-piece takes the current piece's UUID (from deliveries list).
forkable-pp-cli meal set-all --deliveries <id,id> --item <id> --menu <id> [--modifier <modifierId>:<optionId>] [--confirm] — Apply one meal item across several delivery days (replaceAllPieces).
forkable-pp-cli meal confirm <deliveryId> [--unconfirm] [--confirm] — Confirm (or --unconfirm) a delivery day (confirmDelivery).
forkable-pp-cli meal skip <deliveryId> [--confirm] — Skip / cancel one or more delivery days (removeDelivery); --deliveries <id,id> skips several.
forkable-pp-cli reorder <fromDate> --onto <deliveryId> [--replace-piece <uuid>] [--confirm] — Repeat the meal you had on a past date onto an upcoming delivery day.

--replace-piece is effectively always required for meal set and reorder. Forkable auto-selects a candidate meal for every delivery day before you ever touch it — in practice there is no delivery day with a genuinely empty slot, so omitting --replace-piece is a rare/theoretical path, not the default case. Dry-run mode does not validate this. A dry-run without --replace-piece renders a plausible-looking mutation preview and only fails at --confirm time, with a raw GraphQL error (oldPieceId ... Expected value to not be null). Before running --confirm, always look up the target delivery's current piece id via deliveries list (its orders[].pieces[].id) and pass it with --replace-piece. meal set-all has no --replace-piece flag — replaceAllPieces takes no old-piece id at all, so this caveat doesn't apply to it.

Required item options — --modifier. An item with a required option group (e.g. "Choose Protein", min: 1) is rejected by Forkable unless you send a selection. Pass --modifier <modifierId>:<optionId>[,<optionId>...] (repeatable) — the CLI builds the selectionsHash ({"16":[10]}) the replacePiece mutation needs. Find modifier and option ids under each item's modifiers in the menus output. Example: forkable-pp-cli meal set 12345 --item 678 --menu 90 --modifier 16:10 --replace-piece <uuid> --confirm.

Finding the right command

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

forkable-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

What have I eaten this quarter

forkable-pp-cli served-history --since 90d --agent --select date,venue,name,price

Longitudinal list of served meals, narrowed to the high-signal fields to keep agent context small.

Audit dietary conformance

forkable-pp-cli preference-drift --since 60d --json

Flags any served meal that conflicts with your stated dislikes or restrictions.

Monthly lunch spend for finance

forkable-pp-cli spend-trend --since 6mo --by month --csv

Per-month spend totals exported as CSV for a budget close.

Which teams are burning their allowance

forkable-pp-cli allowance-burn --by club --csv

Granted-vs-consumed allowance utilization per club, side by side.

This week's lunch at a glance

forkable-pp-cli upcoming-digest --agent

One compact line per upcoming day for a quick agent-readable briefing.

Auth Setup

Forkable authenticates with a browser session cookie plus a per-request CSRF token fetched from /api/v2/csrf_token. Log in to forkable.com in Chrome, then run 'forkable-pp-cli auth login --chrome' to import your session. There is no API key.

Run forkable-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:
```
forkable-pp-cli buffet-addresses --query example-value --agent --select id,name,status
```
Previewable — --dry-run shows the request without sending
Safe writes — read commands query Forkable; the write commands (meal set, meal set-all, meal confirm, meal skip, reorder) are dry-run by default and only mutate the account when invoked with --confirm
Live fetch — commands query Forkable directly over GraphQL; there is no local sync/cache step
Non-interactive — never prompts, every input is a flag

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 FORKABLE_HOME=<dir> to relocate all four path kinds under one root.
Use per-kind env vars only when a specific kind must diverge: FORKABLE_CONFIG_DIR, FORKABLE_DATA_DIR, FORKABLE_STATE_DIR, FORKABLE_CACHE_DIR.
Resolution order is per-kind env var, --home, FORKABLE_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 forkable-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": {
    "forkable": {
      "command": "forkable-pp-mcp",
      "env": {
        "FORKABLE_HOME": "/srv/forkable"
      }
    }
  }
}

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

forkable-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>", "forkable-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 `forkable-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; forkable-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. This CLI fetches live (no sync command); populate lookups by running the relevant read command (e.g. deliveries list, clubs list).
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:

forkable-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.
forkable-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).
forkable-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.

forkable-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

forkable-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.
FORKABLE_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:

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

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

forkable-pp-cli profile save briefing --json
forkable-pp-cli --profile briefing buffet-addresses --query example-value
forkable-pp-cli profile list --json
forkable-pp-cli profile show briefing
forkable-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 forkable-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/food-and-dining/forkable/cmd/forkable-pp-mcp@latest

claude mcp add forkable-pp-mcp -- forkable-pp-mcp

Verify: claude mcp list

Direct Use

Check if installed: which forkable-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:

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

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