agentleFS
thunderphonethunderphone/skills/use-with-pipecat/SKILL.md

use-with-pipecat

Skill0 starsChanged 57 days agoMITView on GitHub

Connect Pipecat to the ThunderPhone Realtime endpoint. Use when someone asks for pipecat-thunderphone, ThunderPhoneRealtimeLLMService, an OpenAI Realtime-compatible Pipecat service, package installation, saved agent_id configuration, or Pipecat voice-session troubleshooting.

  • Reads credentials
  • Installs packages
  • Sends data out

The file

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 5 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Install the documented adapter: Use pip install pipecat-thunderphone in a clean environment.

    line 13
  2. Distinctive:

    Select agent mode: Prefer a saved deployed ThunderPhone agent for shared prompt, voice, tools, knowledge, and testing.

    line 16
  3. Very specific:

    Create the service: Import ThunderPhoneRealtimeLLMService from the installed adapter and pass the API key plus the documented saved …

    line 21
  4. Distinctive:

    Verify end to end: Test connection, two-way audio, interruptions, tool calls, language switching, teardown, and error recovery.

    line 64
  5. Distinctive:

    Follow use with Pipecat and the Realtime API.

    line 79
  6. Distinctive:

    Keep the API key server-side and redact event payloads before logging.

    line 80
  7. Distinctive:

    Do not claim latency or audio quality without a real audio run.

    line 81

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Use ThunderPhone with Pipecat

  1. Install the documented adapter. Use pip install pipecat-thunderphone in a clean environment. Confirm the resolved pipecat-ai version is 1.8 or newer; do not mix incompatible transport examples from older releases.
  2. Select agent mode. Prefer a saved deployed ThunderPhone agent for shared prompt, voice, tools, knowledge, and testing. Use inline session config only when the application must own those fields.
  3. Store the credential as THUNDERPHONE_API_KEY. Never embed it in client JavaScript, recordings, or pipeline logs.
  4. Create the service. Import ThunderPhoneRealtimeLLMService from the installed adapter and pass the API key plus the documented saved agent_id or inline session options. Wire the service between the application's chosen input/output transports as shown in the Pipecat guide.
  5. Use the OpenAI Realtime protocol at wss://api.thunderphone.com/v1/realtime. Let the adapter manage event and audio framing; do not combine REST call fields with realtime session fields.
  6. Inspect server-side sessions during backend diagnosis with POST /v1/realtime/sessions only when the integration requires explicit session creation.
{
  "agent_id": 12
}
curl --fail-with-body -X POST https://api.thunderphone.com/v1/realtime/sessions \
  -H "Authorization: Bearer $THUNDERPHONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-ThunderPhone-Client: skills/use-with-pipecat@1.0" \
  --data-binary @realtime-session.json
import json, os, requests
with open("realtime-session.json", encoding="utf-8") as f: payload = json.load(f)
r = requests.post("https://api.thunderphone.com/v1/realtime/sessions", json=payload,
    headers={"Authorization": f"Bearer {os.environ['THUNDERPHONE_API_KEY']}",
             "X-ThunderPhone-Client": "skills/use-with-pipecat@1.0"}, timeout=30)
r.raise_for_status(); print(r.json())
import { readFile } from "node:fs/promises";
const r = await fetch("https://api.thunderphone.com/v1/realtime/sessions", { method: "POST",
  headers: { Authorization: `Bearer ${process.env.THUNDERPHONE_API_KEY}`,
    "Content-Type": "application/json", "X-ThunderPhone-Client": "skills/use-with-pipecat@1.0" },
  body: await readFile("realtime-session.json", "utf8") });
if (!r.ok) throw new Error(await r.text()); console.log(await r.json());
  1. Verify end to end. Test connection, two-way audio, interruptions, tool calls, language switching, teardown, and error recovery. Correlate client events with ThunderPhone call/session records.

Errors

SymptomCauseFix
Import failsAdapter or compatible Pipecat version is missingInstall pipecat-thunderphone and verify the environment's dependency versions.
WebSocket returns unauthorizedKey is missing/invalid or auth is sent in the wrong placeLet the adapter use THUNDERPHONE_API_KEY; do not expose it to browser code.
Audio connects but behavior is staleSaved agent draft was not deployed or wrong agent ID is usedRead the agent, deploy the intended revision, and reconnect.
Duplicate audio or eventsTwo services/transports own the same streamKeep one audio path and one lifecycle owner per session.

Source and safety rules

  • Follow use with Pipecat and the Realtime API.
  • Keep the API key server-side and redact event payloads before logging.
  • Do not claim latency or audio quality without a real audio run.
  • Pin compatible packages for production and retest before upgrades.

See references/pipecat-checklist.md.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.