Performance Cycle logo

Performance Cycle

Organization
techwolf-ai
performance-cycle

Evidence gathering for performance review cycles. Gathers goal completion evidence, peer feedback, development progress, scope changes, and values alignment, organised along the org's performance framework dimensions, with organizational values as the 'how' lens. Surfaces evidence gaps. Never suggests ratings, only organises evidence for the manager's judgment.

Overview

Publishertechwolf-ai
Repositoryai-first-toolkit
Skill nameperformance-cycle
Stars
138
Forks
7
Bundled files
5
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.

  • 5 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 Performance Cycle 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/performance-cycle
  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/performance-cycle .claude/skills/performance-cycle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Performance Cycle 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 Performance Cycle 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 Performance Cycle 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.

Performance Cycle Assistant

Principle: "You are responsible." This skill gathers and organises evidence. Rating decisions and development assessments are the manager's alone.

Helps managers prepare evidence-based assessments for performance review cycles. The org's performance framework dimensions measure what was achieved and how the person developed. Organizational values measure how they showed up while doing it.

When to Use

  • During full review cycles (per the org's review cadence)
  • During lighter check-ins between full reviews
  • When the manager says "help me prep [name]'s review", "gather evidence for [name]'s performance"
  • Can be run for one team member or all reports in batch

Context: Performance Framework

Load the org's performance framework from manager-context/performance-framework.md (created during /setup). This defines:

  • Framework dimensions and sub-dimensions
  • Rating scale
  • Promotion readiness labels (if tracked)
  • Review cadence

If manager-context/performance-framework.md doesn't exist, ask the manager to run /setup first.

Instructions

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

1. Identify Scope

Determine who to prepare for:

  • Single team member: "prep [name]'s review"
  • Whole team: "prep all reviews" (runs sequentially for each report)

Determine the review period:

  • Default: last 6 months (for bi-annual review) or last 3 months (for check-in)
  • Can be customised: "since [date]"

2. Load Context

For the target team member, read from manager-context/team/[name].md:

  • Their goals (locations in Notion/Drive)
  • Their development areas from last review
  • Their role and level (from Job Architecture)
  • Their projects and responsibilities

Also load:

  • manager-context/performance-framework.md: org-specific framework dimensions and rating descriptors (falls back to references/performance-framework.md defaults)
  • manager-context/management-framework.md: org-specific management dimensions (falls back to references/management-framework.md defaults)
  • references/values-guide.md: values definitions and signal guidance
  • manager-context/values.md: the organization's specific values

3. Gather Evidence Along Each Dimension

For each dimension and sub-dimension in the org's performance framework (from manager-context/performance-framework.md), gather evidence from connected sources.

For each sub-dimension:

  • Notion: Pull goals, project pages, status updates, metrics relevant to this dimension
  • Slack: Search for messages showing activity, feedback, recognition, or friction related to this dimension
  • Google Drive: Look for deliverables, reports, documents tied to this dimension
  • Calendar: Check for activities that signal growth or scope changes (new meetings, new stakeholders)
  • Compile: what evidence was found, with links

Common evidence patterns by 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

For dimensions that are hardest to assess digitally (e.g., behavioural growth, leadership presence), explicitly flag that the manager's direct observations carry more weight.

4. Gather Values Evidence

Values are the "how": how this person delivered their results and showed up for the team. Search for evidence across the organization's values (from manager-context/values.md). See references/values-guide.md for guidance on finding value signals.

For each value defined in manager-context/values.md, search for evidence using the signal guidance stored there. Common evidence sources by value type:

Collaboration / teamwork values:

  • Slack: cross-team collaboration, helping unblock others, participating in team decisions
  • DMs (with manager): conversations about team dynamics, commitment to group decisions

Ambition / ownership values:

  • Slack/Notion: volunteering for stretch work, proposing ideas, driving outcomes
  • Evidence of taking things to completion without being pushed

Innovation / resourcefulness values:

  • Slack: creative problem-solving, finding workarounds, learning from obstacles
  • Evidence of unblocking themselves or the team under constraints

Transparency / communication values:

  • Slack: sharing context proactively, raising issues early, giving and receiving feedback
  • DMs (with manager): being open about challenges, asking for help

Care / wellbeing values:

  • Slack: celebrating others, recognising teammates, showing empathy
  • Calendar: sustainable work patterns or concerning overwork patterns

For each value, compile evidence as observations (not judgments):

  • Strong signal: Multiple visible examples
  • Some signal: 1-2 examples
  • Gap: No evidence found (note: absence of evidence ≠ absence of the behaviour)

5. Check Peer Recognition

Search Slack for recognition this person received during the review period:

  • Direct shoutouts from teammates
  • Recognition in team channels
  • Reactions on their messages (high-reaction messages = valued contributions)

6. Identify Evidence Gaps

For each dimension, assess evidence strength:

  • Strong evidence: Multiple sources corroborate
  • Some evidence: 1-2 data points
  • Gap: No evidence found, manager needs to gather this manually

7. Produce the Evidence Summary

Read references/output-template.md for the full output template structure (individual and batch mode).

8. For Batch Mode (All Reports)

If preparing for the whole team, produce individual evidence summaries for each team member plus a team-level comparison view. See the batch mode template in references/output-template.md.

9. Present

Here's the evidence I gathered for [name]'s review. I've flagged gaps where you'll want to add your own observations.

Remember: this is evidence gathering only. Rating decisions and promotion assessments are yours to make based on the full picture, including things I can't see.

10. Sub-Agent Review

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

  • Check for recency bias: is most evidence from the last few weeks, or spread across the review period?
  • Check for dimension imbalance: are some dimensions well-evidenced while others are thin? Flag under-covered areas.
  • Check for interpretive language: flag any phrasing that crosses from evidence ("shipped X on time") into interpretation ("demonstrated strong execution").
  • Verify evidence gaps are honestly flagged, not papered over with weak data.
  • Check that values evidence is presented as "examples I found", not "the full picture."

Incorporate the reviewer's feedback before presenting the final summary to the manager.

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:

  • NEVER suggest a rating. Not even hints. The manager decides ratings. Period.
  • NEVER compare team members to each other. Evidence is per-person. Calibration is the manager's job.
  • Evidence, not interpretation. "Shipped feature X on time" is evidence. "Demonstrated strong execution" is interpretation. Stick to evidence.
  • Flag gaps honestly. "I found no evidence for behavioral growth" is more useful than padding with weak data.
  • Recency bias warning. Note if most evidence is from the last month vs. spread across the review period.
  • Values evidence is hardest to gather digitally. Many values are lived in person, not in Slack. The manager's direct observations are more authoritative.
  • This supplements, not replaces. The manager has 6 months of direct observation.

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 Performance Cycle AI skill do?

Evidence gathering for performance review cycles. Gathers goal completion evidence, peer feedback, development progress, scope changes, and values alignment, organised along the org's performance framework dimensions, with organizational values as the 'how' lens. Surfaces evidence gaps. Never suggests ratings, only organises evidence for the manager's judgment.

Why use Performance Cycle on TypingMind?

Because you install it once and use it with any model. Performance Cycle 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 Performance Cycle 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/performance-cycle. 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 Performance Cycle?

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 Performance Cycle?

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

Is the Performance Cycle 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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