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Session Log

Community
michalparkola
session-log

Summarize the current conversation session and append results to the weekly agent-log. Use when user says "log this", "session log", "summarize this session", or asks to write results to the agent-log.

Overview

Publishermichalparkola
Repositorytapestry-skills
Skill namesession-log
Stars
558
Forks
66
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by michalparkola on GitHub. Read the source before you install it.

Installation

Install the Session Log AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/michalparkola/tapestry-skills/tree/main/session-log
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/michalparkola/tapestry-skills.git /tmp/tapestry-skills
mkdir -p .claude/skills
cp -r /tmp/tapestry-skills/session-log .claude/skills/session-log
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Session Log in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Session Log on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Session Log is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Session Log

Summarize the current conversation and append to the weekly agent-log file.

Output Location

YYYY-wWW Agent Log.md

Where YYYY-wWW is the ISO week of today's date. Calculate with:

bash
date +%Y-w%V

Format Rules

  1. Reverse chronological order — newest day on top
  2. One ## heading per day — format: ## YYYY-MM-DD
  3. Bullets, not subheadings — inside a day, use plain bullet - Topic title as topic separator, not ###. No bold, no formatting on topic lines.
  4. Details as nested bullets — one sentence per sub-bullet, can nest if needed for details. No bold. Nesting uses a TAB.
  5. CHUNK markers — if a topic produced a reusable output (a plan, a summary, a framework, a draft message), add nested bullet: CHUNK: <descriptive title>
  6. No explanatory text — no intros, no "in this session we discussed", no meta-commentary
  7. Append, don't replace — when a day heading already exists, add new bullets under it without removing existing content

Example

markdown
## 2026-02-28

- Analiza strategii X vs framework Y + moje obserwacje
  - Strategia jest silna w A i B, słaba w C — brakuje fosy i horyzontu 3+lat.
  - Naming produktu "Rescue" implikuje że kupujący jest ofiarą, co blokuje referencje.
  - Anty-segment nie jest sprawdzalny z zewnątrz — to opis doświadczenia, nie filtr.
  - CHUNK: 3-zdaniowe podsumowanie strategii
  - CHUNK: Scorecard po 6 osiach
- Decyzja: follow-up z klientem
  - Nie wysyłać feedbacku (nie prosił), wysłać link do artykułu jako wartość bez CTA.

Step-by-Step Workflow

1. Determine the target file

bash
WEEK=$(date +%Y-w%V)

Target: ${WEEK} agent-log.md

2. Read existing file (if any)

The file may already have entries from earlier sessions this week. Read it first to avoid overwriting.

3. Review the full conversation and determine dates

Scan the entire conversation history. Identify:

  • Topics — distinct subjects discussed (group related back-and-forth into one topic)
  • Decisions — what was decided or concluded
  • Outputs — any reusable artifacts (summaries, plans, draft messages, frameworks, scorecards)

Date attribution: A conversation may span multiple days. Determine the correct date for each topic using these signals (in priority order):

  1. System reminders about date changes ("The date has changed. Today's date is now...")
  2. File names with dates (e.g., 2026-02-26 client email.md was created/discussed on that date)
  3. Context from session summaries — if the session was continued from a compacted conversation, the summary may mention which work happened when
  4. Default — if no date signal exists, use today's date

Group topics by their actual date, not just "today."

4. Write the log entry

For each date that has topics:

  • If that date's heading (## YYYY-MM-DD) already exists in the file, append new topics under it
  • If the date heading is not yet in the file, add it in the correct reverse-chronological position
  • If the file doesn't exist, create it

Cross-week dates: If a topic belongs to a date in a different ISO week than the target file, note this to the user and ask whether to add it to the current file or the correct week's file.

Condensation rules:

  • Multiple related exchanges → one topic bullet
  • Back-and-forth refinement → only the final conclusion matters
  • If user edited/corrected something → use the corrected version, ignore earlier drafts

Append rules:

  • If today's ## YYYY-MM-DD heading already has bullets, add new ones at the end — never delete or rewrite existing bullets
  • If a topic from this session overlaps with an existing bullet, add the new details as additional nested bullets under a new topic bullet — don't merge into existing text

5. Confirm

Tell the user what was logged (topic titles only, one line).

Important Notes

  • The user may have a preferred topic title style — if they edited a previous entry, match that style
  • Keep bullets ruthlessly short — one sentence, no semicolons chaining multiple thoughts
  • CHUNKs reference outputs that exist in the conversation, not in files — they're bookmarks for the user to find later
  • Do NOT include the full chunk content in the log — just the marker
  • Language: match the language the conversation was conducted in (use the user's language)

Frequently asked questions

What does the Session Log AI skill do?

Summarize the current conversation session and append results to the weekly agent-log. Use when user says "log this", "session log", "summarize this session", or asks to write results to the agent-log.

Why use Session Log on TypingMind?

Because you install it once and use it with any model. Session Log is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Session Log in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/michalparkola/tapestry-skills/tree/main/session-log. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Session Log?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Session Log?

As many as you like. As long as a model supports skills, you can use Session Log with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Session Log AI skill free?

Yes. It is published on GitHub by michalparkola under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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