| name | aftercall |
| description | Analyze post-call materials such as recordings, transcripts, meeting notes, therapy/coaching sessions, study calls, and voice notes. Use for aftercall/post-call workflows: transcribe recordings, label speakers, summarize decisions, extract action items/owners, draft follow-ups, export notes, and reflect on themes after a conversation. |
| version | 1.0.0 |
| author | HinkoK / OpenClaw import |
| license | MIT |
| platforms | ["linux","macos","windows"] |
| tags | ["meetings","transcription","summaries","follow-up","action-items","notes","coaching"] |
aftercall
Hermes usage rules
- Use after a conversation has happened; do not use for live note-taking.
- If the user provides audio/video, transcribe first, then analyze.
- If the user provides transcript/notes, analyze directly.
- Pick the narrowest mode that fits: work call, client follow-up, study call, therapy/coaching reflection, voice note, or speaker-labeling.
- For therapy/coaching content, do not diagnose; separate observations from interpretations and use careful language.
- End with practical next steps, owners, deadlines, or a ready-to-send follow-up when relevant.
Overview
Use this skill when the user wants to turn a finished conversation into a clear outcome: transcript, structured recap, decisions, owners, next steps, follow-up draft, task list, study notes, or deeper reflection.
This skill is for after a conversation happened. It is not for live note-taking during a call.
Trigger examples
Typical requests:
- "Разбери этот созвон и собери итоги"
- "Вот запись звонка, сделай выжимку и action items"
- "Проанализируй мою сессию с психологом и скажи, над чем поработать"
- "Из этого transcript сделай follow-up письмо"
- "Что мы решили на этом колле и кто за что отвечает?"
- "Сделай учебную выжимку из этого study call"
- "Подготовь заметку в Obsidian по итогам звонка"
- "Сделай транскрипт с Speaker 1 / Speaker 2"
- "Попробуй отличить, кто говорит в записи"
Workflow
1) Identify the input type
Start by determining what the user provided:
- Audio/video recording → transcribe first, then analyze
- Transcript → analyze directly
- Raw notes/messages → normalize into a clean after-call recap
- Several materials → merge them, deduplicate overlaps, then analyze
If the input is missing, ask for exactly one thing: the recording, transcript, or notes.
2) Pick the right mode
Choose the narrowest mode that fits the conversation. If the user does not specify one, infer it from context.
Mode: work-call
Use for team syncs, client calls, planning calls, sales calls, and internal work meetings.
Prioritize:
- decisions
- owners
- deadlines
- follow-up message
- unresolved blockers
Mode: therapy-session
Use for therapy, coaching, self-reflection, personal support, and emotionally important conversations.
Prioritize:
- key themes
- recurring patterns
- tensions / avoided topics
- useful reflection prompts
- practical next step between sessions
Guardrails:
- do not diagnose
- separate observations from interpretation
- use careful wording like "похоже", "можно обратить внимание", "стоит исследовать"
Mode: client-follow-up
Use when the user mainly needs a message or email after the call.
Prioritize:
- concise recap
- agreed scope / commitments
- next step
- ready-to-send follow-up draft
The final output can be just the sendable draft plus a tiny internal summary if that solves the task fastest.
Mode: study-call
Use for lessons, tutoring, study groups, mentoring sessions, lecture debriefs, and educational calls.
Prioritize:
- main concepts
- what the learner understood / missed
- terms to review
- questions that remain open
- next study steps
Prefer a more educational structure than a business one.
3) Transcribe only when needed
If the user gave audio/video but no transcript:
- Use the
openai-whisper skill for local speech-to-text when available
- Preserve speaker changes when practical
- Clean obvious filler/noise only if it improves clarity
- Do not over-edit meaning
If the user already gave a transcript, do not spend time retranscribing.
3.5) After transcription, offer speaker-aware mode before analysis
If the transcript was just created from audio/video, pause before the deeper analysis and offer the user a short choice — unless they already explicitly requested or declined speaker labeling.
Default interaction:
- tell the user that the transcription is ready
- explain in 1-2 lines what speaker-aware transcription means
- offer a simple choice: continue with a normal analysis, or first separate speakers as
Speaker 1 / Speaker 2 / Speaker 3
- mention that this is best effort and depends on audio quality
- if useful, invite the user to provide a rename map such as
Speaker 1 = я, Speaker 2 = психолог
Recommended wording:
Транскрибация готова.
Перед разбором могу дополнительно включить **speaker-aware transcription** — это когда я пытаюсь отделить говорящих в тексте (`Speaker 1`, `Speaker 2`, и т.д.), чтобы было визуально понятно, кто что говорил.
Если хочешь, могу:
1. сразу продолжить обычный разбор,
2. или сначала сделать transcript с разделением по спикерам.
Если знаешь участников, можешь сразу написать:
`Speaker 1 = ...`, `Speaker 2 = ...`
Do not ask this if:
- the user already asked for speaker-aware mode
- the user already said they do not need speaker separation
- the transcript already has clear speaker labels / roles and no extra separation is needed
- the transcript is obviously single-speaker
- the task is explicitly ultra-fast and a normal transcript is enough
If the source is already a transcript but it is raw, unlabeled, or visually hard to follow, it is still valid to offer speaker-aware separation before the deeper analysis.
4) Optional function: speaker-aware transcription
Use this only when the user asks to distinguish speakers, or when speaker separation will materially improve the result.
Goal:
- label the transcript as
Speaker 1, Speaker 2, Speaker 3, etc.
- optionally rename them to roles or people if the mapping is known
- use those labels in the summary when it improves clarity
Default behavior:
- If no speaker information is requested, a normal transcript is enough
- If speaker labeling is requested, return the best-effort labeled transcript plus a short confidence note when needed
Preferred output:
[Speaker 1]
...
[Speaker 2]
...
If the user knows the participants, allow remapping:
Speaker 1 = яSpeaker 2 = психолог
Speaker-aware summary can include:
- Что говорил Speaker 1 / я
- Что отвечал Speaker 2 / психолог
- commitments by speaker
- open questions by speaker
- concerns or resistance by speaker
Guardrails:
- treat speaker labeling as best effort, not certainty
- if attribution is weak, say so clearly
- do not assign real names unless the user provided them or the source makes them explicit
- heavy overlap, noisy audio, or many similar voices reduce reliability
See: references/speaker-labeling.md
5) Build the analysis with a stable formatter
Before writing the final answer, normalize the request into this internal formatter:
- mode: work-call / therapy-session / client-follow-up / study-call
- source type: audio / video / transcript / notes / mixed
- output depth: short / standard / deep
- needs transcription: yes / no
- speaker aware: yes / no
- speaker rename map: optional
- needs follow-up draft: yes / no
- needs task extraction: yes / no
- export format: chat / telegram / obsidian / task-list
- language: ru / en / other
- special focus: decisions / themes / tasks / learning / emotions / blockers
Use the formatter template from references/prompt-formatter.md.
If the task is messy, rewrite the task to yourself once using that formatter before generating the answer.
6) Produce the core after-call output
Default output should be practical, not literary.
Return these blocks when available:
- О чем был разговор — 3-7 bullets
- К чему пришли — decisions / conclusions
- Кто за что отвечает — owners if present
- Что делать дальше — next steps with priority/order
- Открытые вопросы — unresolved items
Prefer explicit wording:
- "Решили"
- "Нужно сделать"
- "Осталось уточнить"
- "Ответственный: ..."
If some fields are absent in the source, say so briefly instead of inventing.
7) Add optional layers only when useful
A. Follow-up draft
Use when the user wants to send something after the call.
Possible outputs:
- short Telegram/Slack message
- email follow-up
- client recap
- internal handoff note
Keep it ready to paste.
B. Deeper reflection
Use when the user asks for analysis beyond summary.
Good examples:
- recurring themes
- hidden tension or ambiguity in the discussion
- where the conversation drifted
- what was avoided or left vague
- what the user should think through before the next session
C. Task extraction
If the conversation clearly implies tasks, convert them into a checklist.
Good format:
8) Export in the format the user will actually use
Default export is chat-friendly markdown.
If the user wants a specific destination, use the matching export template:
Rules:
- Telegram → short, scannable, no heavy formatting
- Obsidian → title, sections, tags, and wikilink-ready style
- Task list → action-first, owner/deadline explicit
- Speaker transcript → readable blocks, stable labels, optional rename mapping at the top
9) Match depth to the context
Use the smallest output that solves the need:
- quick work call → concise recap + tasks
- strategy call → recap + decisions + open questions
- therapy/coaching session → recap + themes + suggested reflection
- educational call → recap + concepts + next study steps
- client follow-up request → concise recap + ready draft
- speaker-label request → labeled transcript + short recap unless more was asked
Do not drown short calls in unnecessary structure.
Output defaults
Default response order:
- Short 1-2 sentence summary
- Structured blocks
- Follow-up draft or checklist if requested
- Exported version if explicitly asked
Use Russian by default unless the user explicitly asks for another language.
Quality bar
A good after-call result should:
- reduce memory load
- make the next action obvious
- distinguish facts from interpretation
- avoid hallucinating decisions, owners, or deadlines
- stay readable enough to use immediately
- match the chosen mode instead of forcing one universal structure
- be explicit about uncertainty in speaker attribution
Safety and privacy
- Treat call materials as private by default
- Do not share or quote sensitive details outside the current task
- For highly personal calls, keep the analysis supportive and non-diagnostic
- If the recording quality is poor or attribution is uncertain, say that clearly
Quick resources
