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Commitments

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davekilleen
commitments

Reconcile the promises you made and the asks you received across meetings and notes into a clear owner/due/source list, then — only with your confirmation — turn the real ones into tracked tasks. Use when the user says 'what did I promise', 'what am I on the hook for', 'anything I owe people', 'loose ends', or after a run of meetings. Also use proactively during daily-plan/daily-review when uncaptured commitments surface. Not for tracking work you handed off to others; use `delegate-check`. Not for recording a decision you made; use `decision-log`.

Overview

Publisherdavekilleen
RepositoryDex
Skill namecommitments
Stars
481
Forks
130
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Commitments 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.
  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/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/commitments .claude/skills/commitments
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Commitments 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 Commitments 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 Commitments 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.

/commitments

Small interpersonal promises are where trust is won or lost — "I'll send that over," "can you review this?" — and they evaporate because they never become tracked commitments. This skill surfaces them from what you already captured, and turns the real ones into tasks only when you say so.

Governing rule: do not rebuild the data layer. The Work MCP already ships the scanner — get_commitments_due reads recent meeting notes and person-page action items and returns them bucketed by due date. This skill is a thin present → confirm → create layer on top of it. It does not re-scan, add a store, or touch the extraction logic.


Arguments

$RANGE: optional — today (default), this-week, or all. Maps to the tool's date_range.

Step 1 — Scan (existing tool, don't reinvent)

Call get_commitments_due(date_range=$RANGE). It returns commitments_due_today, commitments_due_this_week, and commitments_no_date — each item carries commitment, due_date, source (the meeting file or person page), and, for person-page items, to_person.

If get_commitments_due returns a feature_status other than ok, follow the standard contract (surface the user-facing message it returns; never invent a result). If it errors or the Work MCP is unavailable, say so plainly and stop — do not fabricate a commitments list.

Optional enrichment (degrades silently): if ScreenPipe is active and opted-in, you may also fold in the commitment-scan (agent skill) results as an extra source. If ScreenPipe is absent — the normal case — skip it entirely and say nothing about it. The shipped skill must never depend on the beta.

Step 2 — Dedup against existing tasks

For each candidate, check it isn't already a task before showing it (Work MCP's create_task similarity gate is the backstop, but pre-filtering means the user isn't asked about things already captured). Drop obvious duplicates; keep near-matches but mark them "possibly already tracked."

Step 3 — Present, grouped by direction

Show two short lists so the user sees both sides of the ledger:

  • Promises you made (outbound — you owe someone)
  • Asks you received (inbound — someone owes you, or asked you to do something)

Direction is inferred from phrasing and to_person, not a field the scanner provides — so when a commitment's direction is genuinely unclear, put it under a third Unclear heading rather than guessing wrong. Each row shows: the commitment, the person (by name), the source (meeting or person page, by name), and the due_date (or "no date"). Refer to people and sources by name, never by id or file path noise.

If nothing is found, say so plainly ("No open commitments surfaced from the last two weeks of meetings and your person pages") — do not pad the list.

Step 4 — Confirm before creating (hard gate — never auto-create)

Nothing is written without the user's say-so. Offer, per item, three choices: create a task / already handled / not a real commitment. Only the items the user picks become tasks. This is the no-write-without-authority discipline — do not batch-create, and do not create anything the user didn't confirm.

Step 5 — Create, then inspect before claiming done

For each confirmed item, call Work MCP create_task (infer the pillar per the CLAUDE.md pillar-inference rules; carry the source into the task context so the commitment stays traceable). Then read back what was created — confirm each task ID and title — before reporting. Say exactly what you made: "Created 3 tasks: [task-…] …, [task-…] …". Never report "done / captured" without the created task IDs in hand. If a create_task call fails or is deduped as an existing task, say that honestly for that item rather than counting it as created.


Quality bar

A good run reflects only real, still-open commitments, splits them into what the user owes vs is owed, creates exactly the tasks the user confirmed, and reports them back by their real task IDs. The user should never be surprised by a task they didn't approve, nor told something was captured that wasn't.

Anti-patterns (do not do these)

  • Auto-creating tasks from the scan without per-item confirmation.
  • Guessing direction (made vs received) when the phrasing is ambiguous — use the Unclear group instead.
  • Re-implementing the scan — inventing a new commitment store or extraction logic instead of calling get_commitments_due.
  • Depending on ScreenPipe — the skill must work fully without it; enrichment is optional and silent when absent.
  • Claiming "captured / done" without reading back the created task IDs.
  • Padding an empty result with vague "maybe follow up on…" filler.

Degradation

  • get_commitments_due unavailable / errors → say so and stop; never fabricate commitments.
  • ScreenPipe / commitment-scan absent → skip silently; the vault scan alone is the product.
  • A create_task call fails → report that item as not created; do not silently count it.

Track Usage (Silent)

Update System/usage_log.md to mark commitments review as used. Analytics (Silent): call track_event with event_name commitments_reviewed and property created_count (the number of tasks actually created — no commitment text, no names). Fires only if the user opted into analytics; no action if it returns "analytics_disabled".

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 Commitments AI skill do?

Reconcile the promises you made and the asks you received across meetings and notes into a clear owner/due/source list, then — only with your confirmation — turn the real ones into tracked tasks. Use when the user says 'what did I promise', 'what am I on the hook for', 'anything I owe people', 'loose ends', or after a run of meetings. Also use proactively during daily-plan/daily-review when uncaptured commitments surface. Not for tracking work you handed off to others; use `delegate-check`. Not for recording a decision you made; use `decision-log`.

Why use Commitments on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/commitments. 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 Commitments?

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 Commitments?

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

Is the Commitments AI skill free?

It is published on GitHub by davekilleen. Check the repository for licensing terms. 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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