Agent skill

Pp Texas Roadhouse

by mvanhorn in mvanhorn/printing-press-library

Printing Press CLI for Texas Roadhouse. An agent skill from mvanhorn/printing-press-library.

Apache-2.0Auto-check: notes

Install Pp Texas Roadhouse

skills CLI
$ npx skills add mvanhorn/printing-press-library --skill pp-texas-roadhouse -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mvanhorn/printing-press-library pp-texas-roadhouse --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-skills/pp-texas-roadhouse .claude/skills/pp-texas-roadhouse && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pp-texas-roadhouse
GitHub stars
2.1k
Token cost
~7.2k tokens
SKILL.md length
2,972 words
Files
1
Skills in repo
506
Repo updated
First seen
Licence
Apache-2.0

At a glance

Printing Press CLI for Texas Roadhouse. An agent skill from mvanhorn/printing-press-library.

  • Works in 6 steps: recall before any discovery → decision tree → always read warnings → …
  • Phrases: join the waitlist
  • SKILL.md covers Prerequisites: Install the CLI, HTTP Transport, Discovery Signals and Command Reference, plus 12 more sections
  • Calls claude, npx and go

What it does

Pp Texas Roadhouse is an agent skill from mvanhorn/printing-press-library. Printing Press CLI for Texas Roadhouse. Waitlist: find a nearby store, read the quote, join and leave the list. Trigger phrases: join the waitlist, Texas Roadhouse wait, roadhouse nearby.

Its SKILL.md is about 7.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Model Context Protocol. The repository describes itself as: Official library of CLIs generated by the CLI Printing Press. Endorsed, tested, and community-contributed. The licence is Apache-2.0.

When your agent uses it

  • Phrases: join the waitlist
  • Texas Roadhouse wait
  • Roadhouse nearby

Example prompts

  • “/pp-texas-roadhouse”

Requirements

  • Node.js
  • Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. recall before any discovery
  2. decision tree
  3. always read warnings
  4. teach & after finalizing your response - always
  5. playbooks - optional flags, automatic synthesis
  6. playbook amend & when your debug response identifies a correction

What it can do on your machine

Read from SKILL.md and the folder at commit d9a1696. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • claude
    • npx
    • go

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pp Texas Roadhouse loads about 7.2k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 2,972 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~7.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mvanhorn/printing-press-library at commit d9a1696, republished under its Apache-2.0 licence (© mvanhorn). 2,972 words, ~7,172 tokens.

Download SKILL.mdSave it as .claude/skills/pp-texas-roadhouse/SKILL.md (or your agent's skills folder).
name
pp-texas-roadhouse
description
Printing Press CLI for Texas Roadhouse. Waitlist: find a nearby store, read the quote, join and leave the list. Trigger phrases: `join the waitlist`, `Texas Roadhouse wait`, `roadhouse nearby`.
allowed-tools
Read, Bash
author
Thomas McCormick
license
Apache-2.0
argument-hint
<command> [args] | install cli|mcp
<!-- GENERATED FILE — DO NOT EDIT.
     This file is a verbatim mirror of library/food-and-dining/texas-roadhouse/SKILL.md,
     regenerated post-merge by tools/generate-skills/. Hand-edits here are
     silently overwritten on the next regen. Edit the library/ source instead.
     See the repository agent guide, section "Generated artifacts: registry.json, cli-skills/". -->

Texas Roadhouse - call ahead seating

Prerequisites: Install the CLI

This skill drives the texas-roadhouse-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 texas-roadhouse --cli-only
  2. Verify: texas-roadhouse-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/food-and-dining/texas-roadhouse/cmd/texas-roadhouse-pp-cli@6695fc0c66c9f1a5670c049428bd79752e231751

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.

Waitlist CLI for Texas Roadhouse. Find a nearby store, read the quote, join and leave the list.

Unofficial integration: This skill is not affiliated with or endorsed by Texas Roadhouse. It uses browser-observed, undocumented endpoints that may change without notice. A waitlist submission sends the guest's name, email address, phone number, and party details to Texas Roadhouse. Do not put that identity on argv flags. Agents pass stdin JSON, --guest-file, or TEXAS_ROADHOUSE_GUEST_* env vars. --dry-run redacts guest fields. Hidden name/email/phone flags are a private --yes path only. Live submit / checkin / cancel require --yes.

MCP privacy boundary: the MCP server intentionally does not expose waitlist submit, because MCP hosts can retain tool-call arguments in transcripts or logs. Use the CLI's stdin JSON, --guest-file, environment, or interactive-prompt flow for a guest join.

Last endpoint verification: 2026-08-29. A Cloudflare challenge or HTTP 403 can mean the browser-facing endpoint changed or rejected the Chrome-compatible transport; retrying a mutation is unsafe until --dry-run and a fresh read request succeed.

HTTP Transport

This CLI uses Chrome-compatible HTTP transport for browser-facing endpoints. It does not require a resident browser process for normal API calls.

Discovery Signals

This CLI was generated with browser-observed traffic context.

  • Capture coverage: 7 API entries from 7 total network entries
  • Protocols: rest_json (75% confidence)
  • Candidate command ideas: get_mapbox.places — Derived from observed GET /api/mapbox/geocoding/v5/mapbox.places/{id} traffic.; get_quote — Derived from observed GET /api/texasroadhouse/waitlist/{waitlist_id}/quote traffic.; get_settings — Derived from observed GET /api/texasroadhouse/waitlist/{waitlist_id}/settings traffic.; get_test — Derived from observed GET /api/texasroadhouse/waitlist/{waitlist_id}/test traffic.; list_near — Derived from observed GET /api/stores/near traffic.

Command Reference

mapbox — Operations on mapbox.places

  • texas-roadhouse-pp-cli mapbox <id> — GET /api/mapbox/geocoding/v5/mapbox.places/{id}

stores — Operations on near

  • texas-roadhouse-pp-cli stores — GET /api/stores/near

texasroadhouse — Operations on test

  • texas-roadhouse-pp-cli texasroadhouse cancel — Cancel a waitlist request. Guests can also text REMOVE to leave; CLI leave is this cancel command. Live cancel requires --yes; --dry-run previews without POSTing.
  • texas-roadhouse-pp-cli texasroadhouse checkin — Mark the party HERE after the guest texts HERE once everyone has arrived (text REMOVE to leave). Not a host-stand visit. Live check-in requires --yes; --dry-run previews without POSTing.
  • texas-roadhouse-pp-cli texasroadhouse get-quote — GET /api/texasroadhouse/waitlist/{waitlist_id}/quote
  • texas-roadhouse-pp-cli texasroadhouse get-settings — GET /api/texasroadhouse/waitlist/{waitlist_id}/settings
  • texas-roadhouse-pp-cli texasroadhouse get-status — GET waitlist request status. Query clientid=texasroadhouse.
  • texas-roadhouse-pp-cli texasroadhouse get-test — GET /api/texasroadhouse/waitlist/{waitlist_id}/test
  • texas-roadhouse-pp-cli texasroadhouse submit — Join a store waitlist. Guest PII comes from stdin JSON, --guest-file, or TEXAS_ROADHOUSE_GUEST_* env vars, not argv flags. Live join requires --yes; --dry-run previews a redacted body without POSTing.
Finding the right command

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

bash
texas-roadhouse-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

No authentication required.

Run texas-roadhouse-pp-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color.

  • 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
    texas-roadhouse-pp-cli mapbox 65804 --agent --select bbox,center,context
  • Previewable — --dry-run shows the request without sending

  • Offline-friendly — sync/search commands can use the local SQLite store when available

  • Non-interactive — --agent never prompts; guest name/email/phone belong on stdin JSON, --guest-file, or env vars, not argv flags

  • Explicit confirmation — --agent does not imply --yes; pass --yes separately only after the target, arguments, and side effects are clear

  • Explicit retries — use --idempotent only when an already-existing create 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": <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 TEXAS_ROADHOUSE_HOME=<dir> to relocate all four path kinds under one root.

  • Use per-kind env vars only when a specific kind must diverge: TEXAS_ROADHOUSE_CONFIG_DIR, TEXAS_ROADHOUSE_DATA_DIR, TEXAS_ROADHOUSE_STATE_DIR, TEXAS_ROADHOUSE_CACHE_DIR.

  • Resolution order is per-kind env var, --home, TEXAS_ROADHOUSE_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 texas-roadhouse-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": {
        "texas-roadhouse": {
          "command": "texas-roadhouse-pp-mcp",
          "env": {
            "TEXAS_ROADHOUSE_HOME": "/srv/texas-roadhouse"
          }
        }
      }
    }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use TEXAS_ROADHOUSE_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 TEXAS_ROADHOUSE_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
texas-roadhouse-pp-cli recall "<user's question>" --agent

The response envelope:

json
{
  "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>", "texas-roadhouse-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 `texas-roadhouse-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; texas-roadhouse-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 texas-roadhouse-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
texas-roadhouse-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.

Show full SKILL.md (1,309 more words)Show less
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.
texas-roadhouse-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).
texas-roadhouse-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.

bash
texas-roadhouse-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

texas-roadhouse-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.
  • TEXAS_ROADHOUSE_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:

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

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

SinkEffect
stdoutDefault; 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.

texas-roadhouse-pp-cli profile save briefing --json
texas-roadhouse-pp-cli --profile briefing mapbox 65804
texas-roadhouse-pp-cli profile list --json
texas-roadhouse-pp-cli profile show briefing
texas-roadhouse-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

CodeMeaning
0Success
2Usage error (wrong arguments)
3Resource not found
5API error (upstream issue)
7Rate limited (wait and retry)
10Config error

Waitlist join flow (present, then confirm)

This is the human choreography. The CLI lists stores. The agent presents them. The CLI never picks a store.

When a human wants on a Texas Roadhouse waitlist:

  1. recall the question first (get on the Texas Roadhouse waitlist).

  2. Geocode the place they named (mapbox). If they named no place, ask for a city or use their last known area. Do not invent coordinates.

  3. stores --lat … --long … --limit 4 --radius 80. Use each store's extref as waitlist_id, not the internal id.

  4. texasroadhouse get-quote <extref> for those four.

  5. STOP. Present 3–4 stores with name, street address, city, distance, and wait. Wait for the human to name one store and a party size (1–6).

  6. Repeat consent. Dry-run submit --stdin only after that pick. Guest name, email, and phone go in stdin JSON, --guest-file, or TEXAS_ROADHOUSE_GUEST_* — not argv flags:

    bash
    printf '%s\n' '{"EmailAddress":"guest@example.test","FirstName":"Test","LastName":"User","PrimaryPhoneAreaCode":"555","PrimaryPhoneNumber":"555-0100","PartySize":2,"WaitMinutes":10}' \
      | texas-roadhouse-pp-cli texasroadhouse submit 218 --stdin --dry-run

    Live submit only after an explicit yes (--yes). VIP Club stays off. The guest later texts HERE once everyone has arrived (REMOVE to leave); checkin is that HERE mark, not a host-stand visit, and still needs --yes. CLI leave is texasroadhouse cancel.

How to present the options (every harness)

Default that works everywhere (OpenClaw, Claude, Telegram, a terminal, Grok Bot): a numbered list, one store per line, then "Reply with the number (or store name) and party size."

1. Store A — PII_ADDRESS_EXAMPLE — 18 mi — wait 0–10 (party 1–2)
2. Store B — PII_ADDRESS_EXAMPLE — 24 mi — wait 0–10

If the host has selectable option cards (widgets, Slack buttons, AskUserQuestion), use those with the same labels: store name, address, wait. Still wait for the pick. Do not auto-select the closest store.

ChatGPT, Codex, Claude, Hermes, OpenClaw, Telegram, Slack, Cursor, and a raw terminal all fit those two shapes. Claude AskUserQuestion and Slack buttons count as cards. ChatGPT, Codex, Hermes, OpenClaw, and Telegram default to the numbered list.

If they already named the store ("Branson"), skip the list and confirm address + quote + party size only.

Argument Parsing

Parse $ARGUMENTS:

  1. Empty, help, or --help → show texas-roadhouse-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

Install the MCP binary from this CLI's published public-library entry or pre-built release, then register it:

bash
claude mcp add texas-roadhouse-pp-mcp -- texas-roadhouse-pp-mcp

Verify: claude mcp list

Direct Use

  1. Check if installed: which texas-roadhouse-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 Command Reference above.
  3. Execute with the --agent flag:
    bash
    texas-roadhouse-pp-cli <command> [subcommand] [args] --agent
  4. If ambiguous, drill into subcommand help: texas-roadhouse-pp-cli <command> --help.

© mvanhorn, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in cli-skills/pp-texas-roadhouse of mvanhorn/printing-press-library.

Open the folder on GitHubat commit d9a1696

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Questions about Pp Texas Roadhouse

What does Pp Texas Roadhouse do?

Printing Press CLI for Texas Roadhouse. An agent skill from mvanhorn/printing-press-library. Pp Texas Roadhouse is an agent skill from mvanhorn/printing-press-library. Printing Press CLI for Texas Roadhouse.

When should I use Pp Texas Roadhouse?

Pp Texas Roadhouse fits situations like: phrases: join the waitlist; texas Roadhouse wait; roadhouse nearby.

How do I install Pp Texas Roadhouse in Claude Code?

Run `npx skills add mvanhorn/printing-press-library --skill pp-texas-roadhouse -a claude-code`. Or copy the skill folder (cli-skills/pp-texas-roadhouse in mvanhorn/printing-press-library) into .claude/skills/pp-texas-roadhouse in your project. Claude Code loads it when a task matches its description.

How do I install Pp Texas Roadhouse in Codex?

Run `npx skills add mvanhorn/printing-press-library --skill pp-texas-roadhouse -a codex`. Or copy the skill folder (cli-skills/pp-texas-roadhouse in mvanhorn/printing-press-library) into .agents/skills/pp-texas-roadhouse in your project. Codex loads it when a task matches its description.

Can I use Pp Texas Roadhouse in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mvanhorn/printing-press-library --skill pp-texas-roadhouse -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pp-texas-roadhouse, .gemini/skills/pp-texas-roadhouse, .github/skills/pp-texas-roadhouse and .opencode/skills/pp-texas-roadhouse in your project.

What does Pp Texas Roadhouse need to run?

Going by SKILL.md and its folder, Pp Texas Roadhouse needs the command-line tools its instructions call (claude, npx and go). Our summary lists: Node.js. Its frontmatter pre-approves these tools: Read, Bash.

Does Pp Texas Roadhouse access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Pp Texas Roadhouse safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Pp Texas Roadhouse use?

Pp Texas Roadhouse is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pp Texas Roadhouse use?

About 7.2k tokens (SKILL.md is roughly 29k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Pp Texas Roadhouse?

Skills that share tags, products or a category with Pp Texas Roadhouse: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pp Texas Roadhouse?

mvanhorn (a GitHub user) maintains it in mvanhorn/printing-press-library, which has 2,056 GitHub stars. The repository holds 506 skills in this directory. The repository was last updated on October 9, 2026.

Source: mvanhorn/printing-press-library on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.