team-health

team-health is a skill for Claude Code from techwolf-ai/ai-first-toolkit. It costs 72 tokens per session (1,538 once invoked), scanned A, original, MIT.

A recurring review of direct reports’ work progress, growth, wellbeing, and connection with the team. Direct reports are the people who report to a manager.

In plain words
What is it for?
Use it for weekly or biweekly team reviews, planning or calibration meetings, skip-level preparation, and questions about how the team is doing.
Why use it?
It helps a manager notice who may need support, more challenge, or a conversation, including people who have not had a recent one-to-one meeting. It works from available team context and recent activity signals.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the people-management plugin — 8 skills shipped together

Good fit Use it for weekly or biweekly team reviews, planning or calibration meetings, skip-level preparation, and questions about how the team is doing.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/techwolf-ai/ai-first-toolkit/team-health
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add techwolf-ai/ai-first-toolkit --skill team-health
Clone the repo
git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkit

Made for: Claude Code.

Or install people-management, the plugin that ships this one along with the rest of its 8 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for team-health

README.md
[![agentmods](https://agentmods.dev/badge/skills/techwolf-ai/ai-first-toolkit/team-health/github.svg)](https://agentmods.dev/skills/techwolf-ai/ai-first-toolkit/team-health)
Your own site
<a href="https://agentmods.dev/skills/techwolf-ai/ai-first-toolkit/team-health"><img src="https://agentmods.dev/badge/skills/techwolf-ai/ai-first-toolkit/team-health/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for team-health

Your own site · 80×15
<a href="https://agentmods.dev/skills/techwolf-ai/ai-first-toolkit/team-health"><img src="https://agentmods.dev/badge/skills/techwolf-ai/ai-first-toolkit/team-health.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,538 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.01538
Opus 5 $0.00036 $0.00769
Sonnet 5 $0.00014 $0.00308
Haiku 4.5 $0.00007 $0.00154

Measured 9d ago against content hash 7391ba7ee72b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

team-health scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/people-management/skills/team-health/SKILL.md · 139 lines

How it starts

The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Team Health Check

Two lenses, always. "Development" asks: is this person growing and performing? "Wellbeing" asks: is this person thriving, connected, and energised? Great managers hold both.

A periodic overview of team dynamics, engagement signals, wellbeing, and development trajectory across all direct reports.

When to Use

  • Weekly or biweekly team health review
  • Before calibration or planning meetings
  • When the manager says "how's my team doing?", "team health check", "anyone I should check in with?"
  • When preparing for skip-level conversations

Instructions

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

1. Load Team Context

Read from manager-context/:

  • manager-profile.md: full list of direct reports
  • team/: individual profiles with goals, projects, last review dates
  • sources.md: channels and data locations per team member

If context is missing, note it and work with available sources.

2. Scan Per Team Member

For each direct report, gather data from the last 14 days (or since last health check):

Activity & Engagement (Slack):

  • Message volume in team/project channels (relative to their baseline if known)
  • Types of messages: asking questions, answering questions, sharing updates, celebrating, flagging issues
  • Channels they're active in
  • Any public wins or recognition received
  • Any public frustration or repeated blockers

1:1 Cadence (Calendar):

  • When was their last 1:1 with the manager?
  • Were recent 1:1s kept or cancelled?
  • Are they overdue for a 1:1?

Development Goals (Notion/Drive):

  • Last time their goals were updated
  • Any progress notes or self-assessments
  • Are there development areas with no recent activity?

Workload Signals (Calendar + Slack):

  • Meeting load (heavy/normal/light for their role)
  • Are they in channels/meetings outside their usual scope? (scope expansion, could be good or concerning)

Wellbeing & Connection Signals (Slack + Calendar):

  • Energy & wellbeing: late-night messages, weekend activity, signs of overwork
  • Connection: are they participating in non-work channels (#random, social threads, team celebrations)?
  • Celebration: have they been recognised or praised recently? Have they celebrated others?
  • Tone: are their messages upbeat, neutral, or showing signs of frustration/fatigue? (use as a soft signal only, never diagnose)
  • Fun & enjoyment: any signs of passion, enthusiasm, or joy in their work (shipping excitement, sharing wins, volunteering for things)?

Read the full file on GitHub · 139 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 139 lines · 72 tokens per session scan A 7391ba7ee72b

Subscribe to this mod's changes

team-health is a skill published in the GitHub repository techwolf-ai/ai-first-toolkit (99 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 1,538 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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