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Speak Summary

GitHubOfficialFirst committed by Sam-RoweDistributed by GitHub

  • Skill
  • Content generator
  • Automation and integrations
  • MIT
  • Low risk
  • Has scripts

From the author

Convert text, markdown, or a summary produced by another skill into a listenable MP3 using local CPU-only neural text-to-speech. Rewrites written prose for the ear before synthesising. Use when the user asks to "read this out", "turn this into audio", "make an MP3", "I want to listen to this", "podcast version", or wants a spoken digest for a commute or breakfast.
— the description by Sam-Rowe

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Speak Summary transforms written text, markdown, or summaries into listenable MP3 audio files. Designed as a terminal step in an automation chain, it processes text by first rewriting it for the ear to ensure natural flow and clarity, then synthesising it using local CPU-based neural text-to-speech. It is built to operate entirely offline, making it suitable for confidential content or headless environments.

What it does

  • Rewrites written prose into a script optimized for spoken delivery
  • Synthesizes text into mono MP3 audio files using local neural models
  • Supports voice cloning from provided audio samples
  • Handles text-to-speech processing entirely on the local CPU
  • Provides guidance on structuring content for audio consumption
  • Integrates with other skills to create automated audio briefings

When to use it

  • When you need to convert meeting minutes or daily summaries into audio
  • When you want to listen to a report or briefing during a commute
  • When you require an offline solution for generating spoken content
  • When you need a companion audio version of a written document

Advantages

  • Operates fully offline without external cloud speech services
  • Ensures privacy by keeping all data processing local
  • Works in headless environments or CI containers
  • Provides a structured approach to adapting text for the ear

Usage examples

“Turn this meeting summary into a podcast version for my commute.”
The agent rewrites the summary into a spoken script and generates an MP3 file ready for playback.
“I want to listen to this report, make an MP3.”
The agent processes the text, optimizes it for audio, and saves the resulting file to the designated directory.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/github/speak-summary/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip speak-summary.zip -d .agents/skills

Documentation by Sam-Rowe

Speak Summary

Turn written text into audio someone will actually want to listen to.

This skill is deliberately a terminal step in a chain. Another skill (or you) produces the text; this one makes it listenable. It pairs naturally with roundup, daily-prep, meeting-minutes, or any summarisation work.

Everything runs locally on CPU. No text is sent to a cloud speech service, which matters when the content is confidential, and it means the skill works in a headless cloud agent or CI container just as well as on a laptop.

Prerequisites

The synthesis engine is Kyutai pocket-tts, a small neural TTS model designed to run on CPUs.

The bundled script installs it automatically into a cached virtualenv on first use, so usually you need do nothing. To install it explicitly:

pip install pocket-tts          # any platform
brew install pocket-tts         # macOS, if preferred

pocket-tts requires Python >=3.10 and <3.15. The script searches for a compatible interpreter rather than assuming python3 is one — worth knowing if you are on a very new Python, where installation would otherwise fail.

You also need an encoder. ffmpeg is strongly preferred (brew install ffmpeg or apt-get install -y ffmpeg); on macOS the script falls back to the built-in afconvert and emits .m4a instead of .mp3.

The first run downloads the model (~1GB) from Hugging Face. After that it is fully offline and synthesises roughly 6x faster than real-time.

The important step: rewrite for the ear

Do not feed written text straight into the synthesiser. Prose that reads well on screen is tiring to listen to. Rewriting it first is what separates a useful audio digest from an unlistenable one.

Produce a spoken script that:

  • Opens with orientation. What this is, what it covers, roughly how long it runs.

  • Replaces bullets with connective prose. "First… The bigger one is… Finally…" — a listener has no visual structure to lean on, so carry it in the language.

  • Expands abbreviations on first use. "PR" becomes "pull request", "CI" becomes "continuous integration". Acronyms that read fine are noise when spoken.

  • Speaks dates and numbers naturally. "the twentieth of August", not "2026-08-20". "About three thousand", not "2,847".

  • Never reads URLs aloud. Say "linked in the written version" instead.

  • Uses short sentences. Split anything past roughly 25 words.

  • Signposts transitions. "Turning to the product side…", "Two things need your attention…".

  • Ends with the actions. Recap what the listener should do, since that is what they need to retain and they cannot scroll back.

  • Drops anything purely visual. Tables, code blocks, and diagrams should be summarised in a sentence or omitted, never read out.

Write this spoken script to its own .txt file. Keep the original written version with its links intact — the audio is a companion to it, not a replacement. The user will want to click through later.

Synthesise

./scripts/tts.sh <input.txt> <output.mp3> [voice.safetensors]

The script strips any residual markdown, splits the text on sentence boundaries into ~600 character chunks (quality degrades on long single inputs), synthesises each chunk, and concatenates the result into a mono MP3 at 96kbps — small enough to sync to a phone, good enough for speech.

Environment overrides:

VariablePurpose
SPEAK_TTS_BINPath to a specific pocket-tts binary; skips all auto-detection.
SPEAK_TTS_HOMEWhere to create/find the cached virtualenv. Default ~/.cache/speak-summary/venv.

Voices

The default English voice is alba. To use a different one, pocket-tts supports voice cloning from a short clean audio sample:

pocket-tts export-voice --help

Pass the resulting .safetensors file as the third argument to the script.

Only clone a voice you have the rights to use. Do not clone a real person's voice — colleague, customer, or public figure — without their explicit consent.

Output

  • Default to ~/Music/Briefings/ unless the user says otherwise; it is easy to point a phone or podcast app at.

  • Name files <subject>-<YYYY-MM-DD>.mp3.

  • Report the path, duration, and size.

  • Offer to play it: afplay <path> on macOS, ffplay -nodisp -autoexit <path> elsewhere.

Length guidance

Aim for 4–6 minutes for a routine digest, which is roughly 600–900 spoken words at a natural pace. If the source would run past about 10 minutes, say so and offer either a tighter edit or a split into multiple files — attention drops off sharply beyond that for informational audio.

Chaining onto other skills

The natural pattern is gather → summarise → speak:

  • roundup → speak-summary — a spoken version of the status briefing.

  • daily-prep → speak-summary — tomorrow's schedule, listened to tonight.

  • meeting-minutes → speak-summary — catch up on a meeting you missed.

When invoked as part of a chain, do not re-summarise. The upstream skill owns what to say; this skill owns how it sounds. Take its output, rewrite it for the ear, and synthesise.

To run unattended (a briefing waiting before breakfast), schedule the upstream skill with a workflow and have it finish by calling this one.

Troubleshooting

Audio cuts off mid-sentence. A chunk exceeded the model's comfortable length. Shorten the sentences in the spoken script.

Words mispronounced. Spell them phonetically in the input — "Kubernetes" as "koo-ber-net-eez". This is a normal part of preparing a spoken script.

First run is slow. That is the one-off model download. Later runs start in about a second.

pocket-tts not found after install. The virtualenv may be stale, or your python3 may be outside the supported 3.10–3.14 range. Delete ~/.cache/speak-summary/venv and re-run, or point SPEAK_TTS_BIN at a known binary.

Files

scripts/tts.sh

#!/usr/bin/env bash
# Convert a plain-text briefing into an MP3 using local neural TTS (Kyutai pocket-tts).
#
# Usage: tts.sh <input.txt> <output.mp3> [voice.safetensors]
#
# CPU-only and fully offline after the first model download, so it runs the same
# on an Apple Silicon Mac and in a Linux cloud agent container. No text is sent
# to any cloud TTS service.
#
# Resolution order for the engine:
#   1. pocket-tts already on PATH (e.g. `brew install pocket-tts`)
#   2. a cached venv at $SPEAK_TTS_HOME (default ~/.cache/speak-summary/venv)
#   3. create that venv and `pip install pocket-tts`
# Set SPEAK_TTS_BIN to point at a specific pocket-tts binary to skip all this.
set -euo pipefail

IN="${1:?usage: tts.sh <input.txt> <output.mp3> [voice.safetensors]}"
OUT="${2:?usage: tts.sh <input.txt> <output.mp3> [voice.safetensors]}"
VOICE="${3:-}"

TTS_HOME="${SPEAK_TTS_HOME:-$HOME/.cache/speak-summary/venv}"

# pocket-tts supports Python >=3.10,<3.15. The system python3 is often outside
# that range, so search for a usable interpreter rather than assuming.
find_python() {
  for c in python3.14 python3.13 python3.12 python3.11 python3.10 python3; do
    p="$(command -v "$c" 2>/dev/null)" || continue
    "$p" -c 'import sys; raise SystemExit(0 if (3,10) <= sys.version_info < (3,15) else 1)' 2>/dev/null \
      && { echo "$p"; return 0; }
  done
  return 1
}

resolve_tts() {
  if [ -n "${SPEAK_TTS_BIN:-}" ]; then echo "$SPEAK_TTS_BIN"; return; fi
  if command -v pocket-tts >/dev/null 2>&1; then command -v pocket-tts; return; fi
  if [ -x "$TTS_HOME/bin/pocket-tts" ]; then echo "$TTS_HOME/bin/pocket-tts"; return; fi

  PY="$(find_python)" || {
    cat >&2 <<'MSG'
No suitable Python found. pocket-tts requires Python >=3.10 and <3.15.
Install one (e.g. 'brew install [email protected]' or 'apt-get install -y python3.12-venv'),
or install pocket-tts yourself and point SPEAK_TTS_BIN at the binary.
MSG
    exit 1
  }

  echo "pocket-tts not found; creating a virtualenv at $TTS_HOME using $PY (one-off, a few minutes)..." >&2
  "$PY" -m venv "$TTS_HOME" >&2 || { echo "Failed to create virtualenv (is the venv module installed?)." >&2; exit 1; }
  "$TTS_HOME/bin/pip" install --quiet --upgrade pip >&2
  "$TTS_HOME/bin/pip" install --quiet pocket-tts >&2 || { echo "Failed to install pocket-tts." >&2; exit 1; }
  [ -x "$TTS_HOME/bin/pocket-tts" ] || { echo "pocket-tts install completed but the binary is missing." >&2; exit 1; }
  echo "$TTS_HOME/bin/pocket-tts"
}

TTS="$(resolve_tts)"
[ -x "$TTS" ] || { echo "TTS engine not executable: $TTS" >&2; exit 1; }

# Encoder: prefer ffmpeg; fall back to macOS afconvert (AAC in .m4a) if absent.
ENCODER=""
if command -v ffmpeg >/dev/null 2>&1; then ENCODER="ffmpeg"
elif command -v afconvert >/dev/null 2>&1; then ENCODER="afconvert"
else
  echo "Need ffmpeg to encode audio. Install with 'brew install ffmpeg' or 'apt-get install -y ffmpeg'." >&2
  exit 1
fi

WORK="$(mktemp -d)"
trap 'rm -rf "$WORK"' EXIT

# pocket-tts quality degrades on very long inputs, so split into ~600-char chunks
# on sentence boundaries, synthesise each, then concatenate.
python3 - "$IN" "$WORK" <<'PY'
import re, sys, pathlib
src = pathlib.Path(sys.argv[1]).read_text()
work = pathlib.Path(sys.argv[2])

# Strip markdown that would otherwise be read aloud as noise.
src = re.sub(r'```.*?```', ' ', src, flags=re.S)          # fenced code
src = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', src)        # links -> label
src = re.sub(r'^\s*#{1,6}\s*', '', src, flags=re.M)       # headings
src = re.sub(r'^\s*[-*+]\s+', '', src, flags=re.M)        # bullets
src = re.sub(r'[*_`>|~]+', '', src)                       # emphasis/code/table pipes
src = re.sub(r'https?://\S+', '', src)                    # bare URLs
src = re.sub(r'[ \t]+', ' ', src)
src = re.sub(r'\n{2,}', '\n\n', src).strip()

MAX = 600
chunks, cur = [], ""
for sent in re.split(r'(?<=[.!?])\s+|\n\n', src):
    sent = sent.strip()
    if not sent:
        continue
    # A single sentence longer than MAX is split on commas as a last resort.
    while len(sent) > MAX:
        cut = sent.rfind(',', 0, MAX)
        cut = cut if cut > MAX // 2 else sent.rfind(' ', 0, MAX)
        cut = cut if cut > 0 else MAX
        if cur:
            chunks.append(cur); cur = ""
        chunks.append(sent[:cut].strip())
        sent = sent[cut:].strip(' ,')
    if len(cur) + len(sent) + 1 > MAX:
        if cur:
            chunks.append(cur)
        cur = sent
    else:
        cur = f"{cur} {sent}".strip()
if cur:
    chunks.append(cur)

if not chunks:
    raise SystemExit("No speakable text found in input.")

for i, c in enumerate(chunks):
    (work / f"chunk_{i:04d}.txt").write_text(c)
PY

N=0
for f in "$WORK"/chunk_*.txt; do
  IDX="$(basename "$f" .txt)"
  ARGS=(generate --quiet --text "$(cat "$f")" --output-path "$WORK/$IDX.wav")
  [ -n "$VOICE" ] && ARGS+=(--voice "$VOICE")
  N=$((N+1))
  echo "  synthesising chunk $N ..." >&2
  "$TTS" "${ARGS[@]}" >/dev/null
  echo "file '$WORK/$IDX.wav'" >> "$WORK/list.txt"
done

mkdir -p "$(dirname "$OUT")"

if [ "$ENCODER" = "ffmpeg" ]; then
  ffmpeg -hide_banner -loglevel error -y -f concat -safe 0 -i "$WORK/list.txt" \
    -c:a libmp3lame -b:a 96k -ar 24000 -ac 1 "$OUT"
else
  # afconvert cannot concat, so join the WAVs first, then encode to AAC.
  python3 - "$WORK" "$WORK/joined.wav" <<'PY'
import sys, wave, pathlib
work, out = pathlib.Path(sys.argv[1]), sys.argv[2]
parts = sorted(work.glob("chunk_*.wav"))
with wave.open(parts[0], 'rb') as w0:
    params = w0.getparams()
with wave.open(out, 'wb') as o:
    o.setparams(params)
    for p in parts:
        with wave.open(str(p), 'rb') as w:
            o.writeframes(w.readframes(w.getnframes()))
PY
  OUT="${OUT%.mp3}.m4a"
  afconvert -f m4af -d aac -b 96000 "$WORK/joined.wav" "$OUT"
  echo "Note: ffmpeg not available; wrote AAC (.m4a) instead of MP3." >&2
fi

SIZE="$(du -h "$OUT" | cut -f1)"
echo "Wrote $OUT ($SIZE, $N chunks)"

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of Sam-RoweSam-Rowe
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/speak-summary · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
Sam-Rowe

AutoGineer is not affiliated with or endorsed by Sam-Rowe or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download speak-summary.zip