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What Have We Done Today

Community
asimons81
what-have-we-done-today

Use when a recap of today's sessions, cron, and kanban helps.

Overview

Publisherasimons81
Repositoryhermes-field-kit
Skill namewhat-have-we-done-today
Stars
127
Forks
12
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by asimons81 on GitHub. Read the source before you install it.

Installation

Install the What Have We Done Today 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/asimons81/hermes-field-kit/tree/main/skills/what-have-we-done-today
  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/asimons81/hermes-field-kit.git /tmp/hermes-field-kit
mkdir -p .claude/skills
cp -r /tmp/hermes-field-kit/skills/what-have-we-done-today .claude/skills/what-have-we-done-today
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable What Have We Done Today 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 What Have We Done Today 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 What Have We Done Today 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.

What Have We Done Today

Overview

A manual, on-demand daily recap for any Hermes installation. It scans three local surfaces for today's activity — sessions (default profile plus every named profile, all sources), kanban boards (done today, running, blocked, todo/ready), and cron stores (jobs that ran, errored, are running now, or are due today) — then writes a plain append-friendly daily markdown log the user can read to remember what happened and what is still open.

Stdlib Python only. Read-only on all databases (mode=ro); the only write is the daily markdown file in the recap directory. It is intentionally NOT a cron job: it runs when a human actually wants the recap (tired, stoned, context-switch heavy, or closing out the day).

When to Use

  • "What have we done today?" / "Recap today" / "What did I get up to today?"
  • The user lost track of a busy multi-project day and wants the threads pulled together.
  • Before shutting down for the night: capture progress + leftovers.

Counter-triggers — do NOT load this skill when:

  • The user asks about a specific past session or topic (use session search).
  • The user wants a recurring scheduled recap (this skill is manual-only).
  • The user wants to mutate sessions, kanban cards, or cron jobs.

Workflow

  1. Scan all three surfaces. Invoke through the terminal tool: python3 ~/.hermes/skills/productivity/what-have-we-done-today/scripts/today_sessions.py --excerpts Output: sessions grouped by profile (time, source, title, counts, goal/outcome hints), then kanban per board (done today, ▶ running, ✋ blocked, todo/ready counts), then cron (ran today with ❌ on errors, ▶ running now, ⏳ due today after an error).
  2. Separate the signals. User-facing session sources are desktop, cli, telegram. kanban sessions are worker lanes (generic titles — outcome comes from the excerpt, not the title). Cron ❌/⏳ rows are real breakage candidates; running executions are work happening right now.
  3. Drill where it matters. session_search reads only the current profile's DB; use it for ambiguous or high-stakes default-profile sessions. For sessions from profiles/*/state.db, the scanner's excerpts are the cheap evidence.
  4. Split Done vs Open. Done = a verified outcome (session excerpt, kanban done-today card, cron ok run). Open = kanban ▶/✋/todo cards, cron ❌/⏳ rows, sessions with no resolution. Never infer completion from a session merely existing.
  5. Pull open items (optional). --ledger FILE lists non-completed items from a task ledger JSON: {"items": [{"id", "title", "status", "next_action", "updated_at"}]}. Statuses other than completed / cancelled count as open.
  6. Write the log. Target $RECAP_DIR/YYYY-MM-DD.md (default ~/.hermes/daily-recaps/); create the directory if missing. Create the file with a # YYYY-MM-DD heading if absent; otherwise append a ## Daily recap — HH:MM section with Done / In flight / Needs attention bullets. Re-runs the same day append new sections.
  7. Brief the user. Compact and ranked: ✅ Done today, 🟡 Still open, ⛔ Needs you, Next pick. Include @session:default/<id> links for anything they might reopen.

Common Pitfalls

  • ~/.hermes/sessions.db may be an empty legacy file — the real session store is ~/.hermes/state.db plus profiles/*/state.db. A scan of the wrong file reports a false quiet day.
  • started_at is epoch seconds; compare against local midnight (the scanner does this in Python). SQLite's 'start of day' modifier is UTC midnight and silently drops the first hours of the local day.
  • Sessions that straddle midnight still count: the filter is started_at OR last_activity_at ≥ local midnight.
  • Each kanban board is its own DB: root ~/.hermes/kanban.db holds only the default board; named boards live at ~/.hermes/kanban/boards/<slug>/kanban.db (_archived/ is skipped).
  • no_agent cron jobs leave no session at all — jobs.json last_run_at/last_status and executions.db are their only trace. A quiet sessions list does not mean a quiet day.
  • Executions use ISO-text timestamps; sessions use epoch floats. Parse each surface with its own type.
  • The recap log is append-friendly by design — never overwrite an existing day file or the earlier recap is destroyed.
  • The scanner never writes: every DB opens mode=ro with a busy timeout, so a live Hermes holding the default DB still scans fine.
  • Don't install this as a cron — the manual trigger is the design.

Verification Checklist

  • Scanner ran with exit code 0 and reported sessions, kanban, and cron.
  • The daily log exists and contains a recap section for today: grep -n "Daily recap" ~/.hermes/daily-recaps/$(date +%F).md
  • Re-running the scanner returns the same inventory.
  • Every open item in the briefing traces to an excerpt, kanban card, or ledger row read during this run.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the What Have We Done Today AI skill do?

Use when a recap of today's sessions, cron, and kanban helps.

Why use What Have We Done Today on TypingMind?

Because you install it once and use it with any model. What Have We Done Today 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 What Have We Done Today in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/asimons81/hermes-field-kit/tree/main/skills/what-have-we-done-today. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use What Have We Done Today?

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 What Have We Done Today?

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

Is the What Have We Done Today AI skill free?

Yes. It is published on GitHub by asimons81 under the Apache-2.0 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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