How do I install the Segment Topics skill?

Run npx skillmds@latest add jperrello/segment-topics in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Segment Topics safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Segment Topics?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Segment Topics free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Segment Topics?

jperrello (@jperrello) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Segment Topics by jperrello — Agent Skill

Name: Segment Topics
Author: jperrello

Segment Topics

Split a transcript into topical chapters. Claude reads the full transcript and emits a list of {t0, t1, title, summary} regions where each region covers one coherent subject. Used by pick-segments to ensure picked spans don't cross topic boundaries.

jperrello 6b17abc 7 files · 37.3 KB Updated Sep 17, 2026

File contents

segment-topics

Claude-driven topical chunking. No keyword heuristics — Claude judges where the subject changes.

Inputs

transcript: path to transcribe output JSON (must have segments or words with timestamps)
out (optional): output JSON path. Default: <dir>/topics.json next to the transcript.

Output

{
  "source": "/path/to/source.mp4",
  "topics": [
    {
      "t0": 0.0,
      "t1": 87.4,
      "title": "Intro and channel pitch",
      "summary": "Greets viewers, teases the compilation."
    },
    {
      "t0": 87.4,
      "t1": 213.1,
      "title": "Soccer challenge fail",
      "summary": "Tries to juggle a ball, eats the field, screams."
    }
  ]
}

Boundaries are contiguous (topics[i].t1 == topics[i+1].t0). Together they cover [0, duration].

How

Build a compact transcript view (timestamped lines).
Send to Claude with the instruction "partition this transcript into contiguous topical regions; emit ~5–20 segments depending on length; favor longer regions over choppy ones; each region must be self-contained (one subject, one bit, one anecdote)".
Validate: monotonic non-overlapping spans, full coverage, snap edges to the nearest transcript segment boundary so spans align with sentence breaks.

Why not just include this in pick-segments

Topic boundaries are reusable. Other consumers (chapter markers, retrieval, future skills) want the same partition. Keeping it separate also lets pick-segments cache-hit even when N or duration changes.

RLM-assisted path (long sources)

On long sources (RLM_TOPICS=1, or auto when duration ≥ RLM_TOPICS_MIN_SEC=1500s) the single-prompt compression loses back-half detail, so it runs an rlm map-reduce instead: build_rlm_prompt.py chunks the FULL transcript on natural seams (with overlap), the orchestrator dispatches one rlm-segment-subcall (Sonnet) per chunk, verifies coverage, and synthesizes topics.json + candidates.hint.json (ranked by confidence, including cross-chunk THREAD candidates). Per-chunk results cache under work/<id>/rlm/. Falls back to the single-prompt path on any failure. Full design + the adopted/rejected tactic ledger: RLM.md.

Status

Implemented (single-prompt + rlm-assisted). Tested on work/<id>/transcript.json.

---name: segment-topicsdescription: Split a transcript into topical chapters. Claude reads the full transcript and emits a list of {t0, t1, title, summary} regions where each region covers one coherent subject. Used by pick-segments to ensure picked spans don't cross topic boundaries.---# segment-topicsClaude-driven topical chunking. No keyword heuristics — Claude judges where the subject changes.## Inputs- `transcript`: path to `transcribe` output JSON (must have segments or words with timestamps)- `out` (optional): output JSON path. Default: `<dir>/topics.json` next to the transcript.## Output```json{ "source": "/path/to/source.mp4", "topics": [ { "t0": 0.0, "t1": 87.4, "title": "Intro and channel pitch", "summary": "Greets viewers, teases the compilation." }, { "t0": 87.4, "t1": 213.1, "title": "Soccer challenge fail", "summary": "Tries to juggle a ball, eats the field, screams." } ]}```Boundaries are contiguous (`topics[i].t1 == topics[i+1].t0`). Together they cover `[0, duration]`.## How1. Build a compact transcript view (timestamped lines).2. Send to Claude with the instruction "partition this transcript into contiguous topical regions; emit ~5–20 segments depending on length; favor longer regions over choppy ones; each region must be self-contained (one subject, one bit, one anecdote)".3. Validate: monotonic non-overlapping spans, full coverage, snap edges to the nearest transcript segment boundary so spans align with sentence breaks.## Why not just include this in pick-segmentsTopic boundaries are reusable. Other consumers (chapter markers, retrieval, future skills) want the same partition. Keeping it separate also lets `pick-segments` cache-hit even when N or duration changes.## RLM-assisted path (long sources)On long sources (`RLM_TOPICS=1`, or auto when duration ≥ `RLM_TOPICS_MIN_SEC`=1500s) thesingle-prompt compression loses back-half detail, so it runs an rlm map-reduce instead:`build_rlm_prompt.py` chunks the FULL transcript on natural seams (with overlap), theorchestrator dispatches one `rlm-segment-subcall` (Sonnet) per chunk, verifies coverage,and synthesizes `topics.json` + `candidates.hint.json` (ranked by confidence, includingcross-chunk THREAD candidates). Per-chunk results cache under `work/<id>/rlm/`. Falls backto the single-prompt path on any failure. **Full design + the adopted/rejected tacticledger: [`RLM.md`](RLM.md).**## StatusImplemented (single-prompt + rlm-assisted). Tested on `work/<id>/transcript.json`.

Frequently asked questions