One On One Prep logo

One On One Prep

Organization
techwolf-ai
one-on-one-prep

Deep-dive preparation for 1:1 meetings with direct reports. Surfaces recent work, wins, friction, wellbeing signals, and development goal progress, anchored in the org's performance framework, organizational values, and management best practices. Produces a prep sheet with suggested conversation topics, not a script.

Overview

Publishertechwolf-ai
Repositoryai-first-toolkit
Skill nameone-on-one-prep
Stars
138
Forks
7
Bundled files
3
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.

  • 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 techwolf-ai on GitHub. Read the source before you install it.

Installation

Install the One On One Prep 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/techwolf-ai/ai-first-toolkit/tree/main/plugins/people-management/skills/one-on-one-prep
  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/techwolf-ai/ai-first-toolkit.git /tmp/ai-first-toolkit
mkdir -p .claude/skills
cp -r /tmp/ai-first-toolkit/plugins/people-management/skills/one-on-one-prep .claude/skills/one-on-one-prep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable One On One Prep 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 One On One Prep 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 One On One Prep 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.

1:1 Prep

Great 1:1s live at the intersection of performance and care. Development keeps growth alive. Wellbeing makes sure the person behind the work is seen. Both matter every time.

Deep-dive preparation for 1:1 meetings with a specific direct report, anchored in the org's performance framework (from manager-context/performance-framework.md) and organizational values.

When to Use

  • Before any 1:1 meeting with a direct report
  • When the manager says "prep my 1:1 with [name]", "what should I discuss with [name]"
  • Can be invoked with just a name, the skill finds the relevant context

Instructions

If any MCP connector is unavailable, follow the connector unavailability protocol in references/operating-principles.md.

1. Identify the Team Member

If a name is provided, match it against manager-context/team/ profiles.

If no name is specified, check the calendar for the next upcoming 1:1 meeting and identify the attendee.

If ambiguous:

Which team member? Your upcoming 1:1s are:
- [Name], [day] at [time]
- [Name], [day] at [time]

Load the team member's profile from manager-context/team/[name].md for:

  • Their role, current projects, communication style
  • Goals location (Notion page, Drive doc)
  • Last review date and development areas
  • Last 1:1 date and open items

If no profile exists:

⚠️ No profile found for [name]. Run /setup to build team context, or I'll work with what I can find from sources directly.

2. Surface Recent Work & Wins

Slack (last 7-14 days):

  • Messages posted by this person in team/project channels
  • Threads they've been active in
  • Any shoutouts or recognition they received (search for their name + positive signals like "great", "shipped", "amazing", "thanks")
  • Features shipped, PRs merged, deliverables completed (visible in public channels)

Google Drive:

  • Documents they've recently created or edited
  • Especially docs related to their projects or deliverables

Notion:

  • Pages they've authored or updated recently
  • Project status updates they've contributed to

Compile into a "recent activity" summary.

3. Detect Friction or Concerns

Look for signals (NOT diagnoses, signals for the manager to explore):

Slack:

  • Repeated blockers or unanswered questions
  • Messages where they expressed frustration or confusion
  • Decreased activity compared to usual patterns (if baseline is known)
  • Long threads where they're asking for help without clear resolution

Calendar:

  • Cancelled or rescheduled 1:1s
  • Unusually heavy or light meeting load

Important: Frame these as conversation starters, not assessments:

💡 Potential topics to explore (signals, not conclusions):
- [Name] asked about [topic] in #channel 3 times this week without a clear resolution, might be a blocker worth discussing
- [Name]'s activity in #project-channel has been lower than usual, could be fine, but worth checking in

4. Values & Wellbeing Check

Scan for signals related to the organization's values (from manager-context/values.md). These give the manager conversation threads beyond just goals and deliverables.

If no values are configured, focus on these universal management dimensions:

Wellbeing & Energy:

  • Late-night or weekend Slack activity (potential overwork)
  • Calendar density (back-to-back days, no focus time)
  • Tone in recent messages: enthusiasm vs. fatigue (soft signal only)
  • Have they taken time off recently?

Team Connection & Collaboration:

  • Are they collaborating across the team, or working in isolation?
  • Engagement in team channels (social, celebrations)
  • Have they helped or unblocked teammates recently?

Communication & Transparency:

  • Are they sharing context, asking for feedback, raising issues openly?
  • Any threads where they seemed hesitant to speak up?

Resourcefulness & Problem-Solving:

  • Evidence of creative problem-solving, unblocking themselves, learning new things

Ownership & Initiative:

  • Volunteering for stretch work, proposing ideas, taking initiative
  • Driving things to completion vs. waiting for direction

If organizational values are configured, map these dimensions to the specific values and use value names in the output.

Compile into a brief values snapshot (not a scorecard, conversation prompts):

Values Snapshot:
- [Value/Dimension 1]: [observation or "no signal"]
- [Value/Dimension 2]: [observation or "no signal"]
...

Only include values/dimensions where there's a meaningful signal. Don't force all of them every time.

5. Check Development Goals

Using the goals location from the team member's profile:

Notion:

  • Pull their current goals and development areas
  • Check when goals were last updated
  • Look for any self-assessment or progress notes

Google Drive:

  • Pull the 1:1 document for this person
  • Check the most recent entries for open action items and commitments

Reference the org's performance framework dimensions (from manager-context/performance-framework.md). Use the org's dimension names and sub-dimensions when checking goal progress. If this file doesn't exist, ask the manager to run /setup first.

See the "Evidence Gathering Guidelines" section in references/performance-framework.md for what to look for per dimension type:

  • Results/delivery dimensions: goal completion, shipped work, quality feedback, business outcomes
  • Growth/development dimensions: learning activities, new skills applied, scope expansion, behavioural changes
  • Collaboration/leadership dimensions: cross-team activity, mentoring, influence in discussions
📋 Development Goal Status:
- Goal 1: "[goal text]", [status: on track / needs attention / no update since [date]]
- Goal 2: "[goal text]", [status]
- Development area: "[area]", [any evidence of progress or attention]

⏰ Last goal update: [date], [flag if >6 weeks old]

6. Pull Previous 1:1 Notes

Search for the last 1:1 document/notes:

Google Drive: Search "[name] 1:1", "one on one [name]"

Extract:

  • Open action items (for both manager and team member)
  • Topics that were "parked" for follow-up
  • Development commitments made
📝 From last 1:1 ([date]):
Open items:
- [ ] [Manager] to [action], [status: done/pending/unknown]
- [ ] [Team member] to [action], [status: done/pending/unknown]
Parked topics: [if any]

7. Produce the Prep Sheet

Read references/output-template.md for the full output template structure.

8. Sub-Agent Review

Spawn a sub-agent to review the prep sheet with fresh eyes. The reviewer should:

  • Check that wellbeing signals are framed as conversation starters, not assessments or diagnoses.
  • Check that the values snapshot only includes values with meaningful signals, not forcing all values every time.
  • Verify that wins lead the document and the tone is constructive, not surveillance-like.
  • Flag any phrasing that crosses from observation ("posted after 22:00 three times") into interpretation ("seems burned out").
  • Check that evidence gaps are noted where data is thin, rather than padded with weak signals.

Incorporate the reviewer's feedback before presenting the final prep sheet.

9. Present and Offer Follow-Up

Here's your prep for your 1:1 with [name]. Anything you'd like me to dig deeper on?

Important Notes

Read references/operating-principles.md for shared operating principles (data scope, DM flagging, signals vs diagnoses, connector unavailability).

Additional notes specific to this skill:

  • Wins first. Always lead with recognition opportunities. This sets the tone.
  • Goals are the backbone. If goals are missing or stale, flag it prominently. Great 1:1s keep development goals alive.
  • Framework + values alignment. Suggested questions should map to the org's performance framework dimensions or organizational values. Don't force all values every time, only surface values where there's a real signal.
  • Values are conversation threads, not checklists. The values snapshot helps managers notice things beyond deliverables. A 1:1 that only covers goals misses the human. A 1:1 that only covers feelings misses the growth. Both.
  • Don't script the conversation. Provide prompts and context, not a talk track.

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 One On One Prep AI skill do?

Deep-dive preparation for 1:1 meetings with direct reports. Surfaces recent work, wins, friction, wellbeing signals, and development goal progress, anchored in the org's performance framework, organizational values, and management best practices. Produces a prep sheet with suggested conversation topics, not a script.

Why use One On One Prep on TypingMind?

Because you install it once and use it with any model. One On One Prep 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 One On One Prep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/people-management/skills/one-on-one-prep. 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 One On One Prep?

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 One On One Prep?

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

Is the One On One Prep AI skill free?

Yes. It is published on GitHub by techwolf-ai 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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