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Printing Press CLI for Table Reservation Goat. One reservation CLI for OpenTable, Tock, and Resy — search all three networks at once, watch for cancellations
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Every Scrape Creators endpoint across 28 platforms, plus a local store with offline transcript search, cross-platform joins, and ad-library diffing no other Scrape Creators tool ships. Trigger phrases: `find which platforms a creator is on`, `compare these creators' engagement`, `monitor a brand's ads`, `search creator transcripts for a keyword`, `track a hashtag across platforms`, `use scrape creators`, `run scrape-creators`.
Every Scrape Creators endpoint across 28 platforms, plus a local store with offline transcript search, cross-platform joins, and ad-library diffing no other Scrape Creators tool ships. Trigger phrases: `find which platforms a creator is on`, `compare these creators' engagement`, `monitor a brand's ads`, `search creator transcripts for a keyword`, `track a hashtag across platforms`, `use scrape creators`, `run scrape-creators`.
Printing Press CLI for Spotify. You can use Spotify's Web API to discover music and podcasts, manage your Spotify library, control audio playback
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SOC 직업 분류 기준
| name | pp-table-reservation-goat |
| description | Printing Press CLI for Table Reservation Goat. One reservation CLI for OpenTable, Tock, and Resy — search all three networks at once, watch for cancellations |
| author | Pejman Pour-Moezzi |
| license | Apache-2.0 |
| argument-hint | <command> [args] | install cli|mcp |
| allowed-tools | Read Bash |
| metadata | {"openclaw":{"requires":{"bins":["table-reservation-goat-pp-cli"]},"install":[{"kind":"go","bins":["table-reservation-goat-pp-cli"],"module":"github.com/mvanhorn/printing-press-library/library/food-and-dining/table-reservation-goat/cmd/table-reservation-goat-pp-cli"}]}} |
This skill drives the table-reservation-goat-pp-cli binary. If it is missing, install it first:
$HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:
npx -y @mvanhorn/printing-press-library install table-reservation-goat --cli-only
table-reservation-goat-pp-cli --version$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 1.26.5 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/table-reservation-goat/cmd/table-reservation-goat-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.
One reservation CLI for OpenTable, Tock, and Resy — search all three networks at once, watch for cancellations, book + cancel end-to-end, and track changes from a local store agents can query.
availability — Check open reservation slots across OpenTable, Tock, and Resy
table-reservation-goat-pp-cli availability check — Check open slots for a restaurant on a specific date and party sizetable-reservation-goat-pp-cli availability multi-day — Multi-day availability for a single restaurant — Mon-Sun matrixexperiences — List prepaid and tasting-menu experiences (Tock-style)
me — Read your authenticated user profile from both networks
reservations — List, book, modify, and cancel reservations (requires auth login)
OpenTable attach booking is enabled only when TABLE_RESERVATION_GOAT_OT_CHROME_DEBUG_URL is explicitly configured and the attached profile is already signed in. Use TRG_ALLOW_BOOK=prepare to drive through an enabled final confirmation control without clicking it; only TRG_ALLOW_BOOK=1 may place the reservation. Typed failures are attach_unreachable, not_signed_in, selector_drift, form_validation, slot_taken, and incomplete_confirmation; page_state diagnostics are redacted and never include URL query tokens or arbitrary account labels.
Tock booking prefers the signed-in Chrome session at TABLE_RESERVATION_GOAT_TOCK_CHROME_DEBUG_URL (default http://localhost:9222) and keeps the stealth-headless session fallback. It tries legacy slot buttons first, then the exact-time /search row and modern time-combobox/experience-card controls. Agent and --no-input runs never prompt for CVC: provide a 3- or 4-digit TRG_TOCK_CVC only when needed. Typed failures are cvc_required and selector_drift; selector diagnostics contain only a query-free path, booleans, time labels, and allowlisted control categories.
restaurants — Search and inspect restaurants across OpenTable, Tock, and Resy
table-reservation-goat-pp-cli restaurants get — Get a restaurant's full detail — hours, address, cuisine, price band, photos, accoladestable-reservation-goat-pp-cli restaurants list — List restaurants across OpenTable, Tock, and Resy; filter by location, cuisine, price band, accolades, and party sizewishlist — Read your saved/wishlisted restaurants from both networks
When you know what you want to do but not which command does it, ask the CLI directly:
table-reservation-goat-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.
Search and availability require no account. OpenTable and Tock attach booking use the signed-in session in the attached Chrome profile; Tock keeps the saved-session stealth-headless fallback; Resy booking uses its saved API token.
Run table-reservation-goat-pp-cli doctor to verify setup.
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:
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
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.
Agents should treat the CLI's path resolver as part of the runtime contract:
Use --home <dir> for one invocation, or set TABLE_RESERVATION_GOAT_HOME=<dir> to relocate all four path kinds under one root.
Use per-kind env vars only when a specific kind must diverge: TABLE_RESERVATION_GOAT_CONFIG_DIR, TABLE_RESERVATION_GOAT_DATA_DIR, TABLE_RESERVATION_GOAT_STATE_DIR, TABLE_RESERVATION_GOAT_CACHE_DIR.
Resolution order is per-kind env var, --home, TABLE_RESERVATION_GOAT_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 table-reservation-goat-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": {
"table-reservation-goat": {
"command": "table-reservation-goat-pp-mcp",
"env": {
"TABLE_RESERVATION_GOAT_HOME": "/srv/table-reservation-goat"
}
}
}
}
Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use TABLE_RESERVATION_GOAT_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 TABLE_RESERVATION_GOAT_HOME, or doctor will not find credentials left under the former root.
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.
recall before any discoveryBefore list/search/drill commands on a new user question, run:
table-reservation-goat-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>", "table-reservation-goat-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.
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 `table-reservation-goat-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; table-reservation-goat-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.
warningslow_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 table-reservation-goat-pp-cli sync to refresh entity lookups.no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.teach & after finalizing your response - alwaysTeaching 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:
table-reservation-goat-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.
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.
table-reservation-goat-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).
table-reservation-goat-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.
playbook amend & when your debug response identifies a correctionIf 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.
table-reservation-goat-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:
{meta, results}, payload nested two levels deeper than the docs claim).What does NOT belong in notes:
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).
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:
If a correction is only meaningful with user-specific context, it belongs in a personal note, not in the playbook amend.
table-reservation-goat-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.
--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.TABLE_RESERVATION_GOAT_NO_LEARN=true in the environment globally disables the pipeline.When you (or the agent) notice something off about this CLI, record it:
table-reservation-goat-pp-cli feedback "the --since flag is inclusive but docs say exclusive"
table-reservation-goat-pp-cli feedback --stdin < notes.txt
table-reservation-goat-pp-cli feedback list --json --limit 10
Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless TABLE_RESERVATION_GOAT_FEEDBACK_ENDPOINT is set AND either --send is passed or TABLE_RESERVATION_GOAT_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.
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.
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.
table-reservation-goat-pp-cli profile save briefing --json
table-reservation-goat-pp-cli --profile briefing experiences list
table-reservation-goat-pp-cli profile list --json
table-reservation-goat-pp-cli profile show briefing
table-reservation-goat-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.
| Code | Meaning |
|---|---|
| 0 | Success |
| 2 | Usage error (wrong arguments) |
| 3 | Resource not found |
| 5 | API error (upstream issue) |
| 7 | Rate limited (wait and retry) |
| 10 | Config error |
Parse $ARGUMENTS:
help, or --help → show table-reservation-goat-pp-cli --help outputinstall → ends with mcp → MCP installation; otherwise → see Prerequisites above--agent)go install github.com/mvanhorn/printing-press-library/library/food-and-dining/table-reservation-goat/cmd/table-reservation-goat-pp-mcp@latest
claude mcp add table-reservation-goat-pp-mcp -- table-reservation-goat-pp-mcp
claude mcp listwhich table-reservation-goat-pp-cli
If not found, offer to install (see Prerequisites at the top of this skill).--agent flag:
table-reservation-goat-pp-cli <command> [subcommand] [args] --agent
table-reservation-goat-pp-cli <command> --help.