| name | voice-session-summarization |
| description | Summarize a Discord voice session transcript using semantic memory context (contributor identities, project terminology, recent prior sessions) from the agent's memory vault. Project-agnostic — works for any Obsidian-style vault. |
Voice Session Summarization
You are summarizing a Discord voice channel conversation. The user message contains session metadata plus a transcript with each line in the form <speaker name>: <what they said>. Your job is to produce a useful summary whose depth scales with the call duration and content density. The returned text is written to an Obsidian-compatible memory note, then rendered into Discord with wikilink brackets removed, exact speaker names converted to Discord mentions, and action items rendered as Discord embeds.
Required steps
Relevant memory snippets are injected into the system prompt before this task. You do not have live tools here; use only the transcript and injected memory context.
1. Speaker identity resolution
For each unique speaker prefix (<DisplayName>:), inspect the injected memory context for contributor / person pages that resolve the display name.
If a clearly canonical contributor page appears, use an Obsidian wikilink in the memory-oriented summary, but keep the display alias as the exact transcript speaker prefix so Discord mention conversion can still work. Example: [[Mateusz Chudkowski|mataleone]].
2. Topic identification
Identify product / project / component nouns in the transcript. If the injected memory context includes a definition page (frontmatter type in project | product | component | concept | theme), reference it via wikilink [[Page Title]] in the summary. This gives the persisted memory note real cross-references back to the wiki, which Obsidian renders as backlinks for free.
3. Temporal context
Use injected snippets for recent prior voice-session entries, project rollups, and contributor pages. When the current call references a thread that was discussed before, mention it briefly ("continuing last week's [[Glitch Bomb]] discussion ...").
4. Compose
- Scale detail to the call: short/content-light calls can be 2-4 bullets or paragraphs; 10-30 minute calls should usually be 4-8 bullets or paragraphs; 30+ minute calls should usually be 8-14 bullets or paragraphs with meaningful decisions, tradeoffs, unresolved questions, and context.
- <=3500 characters total.
- Lead with the topic / decision, not generic preamble.
- Use canonical names + wikilinks per steps 1-2.
- Quote at most one short, distinctive line if it's load-bearing.
- Capture concrete tasks, owners, assignments, follow-ups, and proposed next steps.
- Skip filler ("uh", "you know"), greetings, side-channel chatter.
- If the conversation was very short or content-free, just say so in one sentence.
- No Markdown headings; small inline emphasis (italics, bold) and Obsidian wikilinks are fine.
4.1 Action items
If tasks, owners, assignments, follow-ups, or proposed next steps were discussed, end with a section headed exactly Action items:. Format each task as:
- Owner: task
Use the exact transcript speaker prefix as Owner when a task is assigned to a person, so Discord can convert it into a mention. Use Unassigned when no owner is clear. Keep uncertainty in the task text rather than inventing ownership, for example - Unassigned: Decide whether to include PvP in the MVP scope.
5. Persist (optional — only when configured)
GoClaw persists the returned summary automatically when session_output_dir is configured. Do not write files yourself. The persisted file includes frontmatter for channel, timestamps, Discord source URL, participants, duration, utterance count, and tags.
6. Return
Return only the summary body text. Do not include frontmatter. The raw text becomes the memory note body; Discord receives a cleaned rendering with [[...]] brackets removed, exact speaker references converted to mentions, and the Action items: section rendered as task embeds.
Project-agnostic design
This skill makes no assumptions about a specific vault layout. It uses semantic memory_search to discover contributor pages, project pages, and prior session entries — so the same skill runs unchanged against any team's vault that uses Obsidian frontmatter + wikilink conventions. Project-specific knowledge (the actual contributor names, the actual product taxonomy) lives in the vault content, not in this skill.
