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Session Flow

CommunityPopular
Yeachan-Heo
session-flow

Reorient in the current Claude Code session with an evidence-backed map of its goal, pivots, outcomes, current step, and open decisions.

Overview

PublisherYeachan-Heo
Repositoryoh-my-claudecode
Skill namesession-flow
Stars
39.8K
Forks
3.6K
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 Yeachan-Heo on GitHub. Read the source before you install it.

Installation

Install the Session Flow 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/Yeachan-Heo/oh-my-claudecode/tree/main/skills/session-flow
  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/Yeachan-Heo/oh-my-claudecode.git /tmp/oh-my-claudecode
mkdir -p .claude/skills
cp -r /tmp/oh-my-claudecode/skills/session-flow .claude/skills/session-flow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Session Flow 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 Flow 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 Flow 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 Flow

Build a compact, evidence-backed map of how this conversation actually unfolded so the user can reorient after switching sessions or compacting. This is an advisory summary, not a planner, executor, or replacement for the user's task list.

Modes

  • No argument: render the full map and reconcile its session-flow Tasks when native Task tools are available.
  • short: return only the goal, current stage, and what (if anything) needs the user. Read-only; do not change Tasks.
  • reset: discover and delete only Tasks owned by this skill, then confirm what was removed. Do not render a new map.

Workflow

  1. Reconstruct the flow from this conversation's user messages and tool results. Do not treat repository state alone as evidence that a stage happened. Separate the original goal from explicit goal changes, pivots, completed outcomes, dropped paths, and decisions still waiting on the user.
  2. Make 4–8 meaningful stages when the history supports them; use fewer rather than padding sparse histories or inventing work. Use one outcome per stage:
    • ✅ completed with an outcome supported by the conversation
    • ▶ the current unfinished stage
    • ○ an explicitly planned but unstarted stage
    • ✂ dropped, with the stated reason
    • ↪ a pivot, with the old direction and the evidenced new direction
  3. Give every stage one compact evidence pointer. Use a real URL or artifact path when one appears in the conversation; otherwise cite a distinctive short user quote or tool result. Never invent a link or state an inference as fact. Mark uncertain or pre-compaction reconstruction as inferred.
  4. Keep the full map to at most 15 lines: one line per stage, plus concise goal, Needs you, and Next lines. Include open decisions in Needs you; say none when there are none. Next must be a concrete next action only when the conversation supports one.
  5. In the default and reset modes, call TaskList and inspect every [S<number>] candidate with TaskGet before mutating it. A Task belongs to this skill only when its subject starts with [S<number>] and its description contains the exact marker line session-flow: managed stage. Never edit, complete, delete, or reuse an unmarked Task.
  6. In the default mode, update owned Tasks that still represent a stage, create missing stages with TaskCreate, a subject beginning [S<number>], and a description whose first line is the ownership marker followed by stage evidence. Match statuses to the map: completed (including dropped or pivoted) is completed, current is in_progress, and planned is pending. Delete owned Tasks for stages no longer in the reconstructed map with TaskUpdate status deleted; keep surviving stage numbers stable and avoid collisions with unowned Tasks.
  7. The OMC continuation hook excludes Tasks with both the [S<number>] subject prefix and the exact ownership marker, so these descriptive records are not treated as unfinished work. Preserve both markers on every create or update. short must not call mutating Task tools.
  8. For reset, discover owned Tasks with TaskList and TaskGet, then use TaskUpdate status deleted only on confirmed owned Tasks.
  9. If the native Task tools are unavailable, render the map without claiming it was persisted. For reset, report that owned Tasks could not be inspected or removed; do not guess which Tasks belong to the skill.

Output

Use a stable, concise shape:

text
🎯 Goal: <goal and any evidenced change>
✅ S1 <stage> → <outcome> — evidence: <source pointer>
↪ S2 <old direction> → <new direction> — evidence: <source pointer>
▶ S3 <current stage> — evidence: <source pointer>
✂ S4 <dropped path> — <reason> — evidence: <source pointer>
⏸ Needs you: <decision or none>
➡ Next: <supported next action or none>

Omit example rows that do not apply. Keep the stage order stable across calls unless the conversation provides evidence to correct it; update the owned Tasks to match any correction.

Frequently asked questions

What does the Session Flow AI skill do?

Reorient in the current Claude Code session with an evidence-backed map of its goal, pivots, outcomes, current step, and open decisions.

Why use Session Flow on TypingMind?

Because you install it once and use it with any model. Session Flow 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 Flow in TypingMind?

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

Which AI models can use Session Flow?

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 Flow?

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

Is the Session Flow AI skill free?

Yes. It is published on GitHub by Yeachan-Heo 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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