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Ecosystem

Community
glebis
ecosystem

Audit the Claude Code ecosystem — skill health and staleness, project activity pulse, CLAUDE.md instruction drift, Mac Mini service status. Use this skill whenever the user asks about ecosystem health, stale skills, abandoned projects, system status, infrastructure check, "what's broken", "what's stale", "how's my setup", or any request to review the state of their Claude Code environment. Also triggers on "/ecosystem", "audit my ecosystem", "ecosystem health", "ecosystem audit".

Overview

Publisherglebis
Repositoryclaude-skills
Skill nameecosystem
Stars
379
Forks
56
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Ecosystem 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/glebis/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/ecosystem .claude/skills/ecosystem
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ecosystem 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 Ecosystem 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 Ecosystem 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.

Ecosystem Audit

On-demand audit of the Claude Code ecosystem. Runs 4 checks, prints a full report with an attention summary, and appends a summary to today's daily note.

Workflow

Run all 4 checks sequentially using the bash blocks below. Each block runs in a fresh shell, so after all checks complete, compose the daily note summary and attention rollup from the terminal output you've collected.

CRITICAL: Run date +"%Y%m%d" before writing to the daily note.

Step 1: Skill Health

Scan ~/.claude/skills/ for skill freshness and broken symlinks.

bash
NOW=$(date +%s)
D30=$((NOW - 30*86400))
D90=$((NOW - 90*86400))
ACTIVE="" BROKEN=""
ACTIVE_N=0 RECENT_N=0 STALE_N=0 BROKEN_N=0 TOTAL=0

for entry in ~/.claude/skills/*/; do
  [ -d "$entry" ] || continue
  name=$(basename "$entry")
  TOTAL=$((TOTAL + 1))

  if [ -L "${entry%/}" ] && [ ! -e "${entry%/}" ]; then
    target=$(readlink "${entry%/}")
    BROKEN="${BROKEN}  - ${name} -> ${target}\n"
    BROKEN_N=$((BROKEN_N + 1))
    continue
  fi

  real_path="$entry"
  if [ -L "${entry%/}" ]; then
    real_path="$(readlink "${entry%/}")/"
  fi

  newest=$(find "$real_path" -type f -exec stat -f %m {} + 2>/dev/null | sort -rn | head -1)
  [ -z "$newest" ] && newest=0

  if [ "$newest" -ge "$D30" ]; then
    ACTIVE="${ACTIVE}, ${name}"
    ACTIVE_N=$((ACTIVE_N + 1))
  elif [ "$newest" -ge "$D90" ]; then
    RECENT_N=$((RECENT_N + 1))
  else
    STALE_N=$((STALE_N + 1))
  fi
done

echo "### Skills (${TOTAL} total)"
echo "- Active (30d): ${ACTIVE_N}${ACTIVE:+ — ${ACTIVE:2}}"
echo "- Recent (30-90d): ${RECENT_N}"
echo "- Stale (>90d): ${STALE_N}"
echo "- Broken symlinks: ${BROKEN_N}"
[ -n "$BROKEN" ] && printf "$BROKEN"

Step 2: Project Pulse

Scan ~/ai_projects/ for git repo activity and CLAUDE.md presence. Cap dormant and abandoned lists at 10 names to keep output readable.

bash
NOW=$(date +%s)
D30=$((NOW - 30*86400))
D180=$((NOW - 180*86400))
ACTIVE="" DORMANT="" ABANDONED="" DORMANT_N=0 ABANDONED_N=0 NO_CLAUDE=0
ACTIVE_N=0 GIT_TOTAL=0 DIR_TOTAL=0 NODATA=0

for dir in ~/ai_projects/*/; do
  [ -d "$dir" ] || continue
  DIR_TOTAL=$((DIR_TOTAL + 1))
  [ -d "${dir}.git" ] || continue
  GIT_TOTAL=$((GIT_TOTAL + 1))

  last_commit=$(git -C "$dir" log -1 --format=%ct 2>/dev/null)
  if [ -z "$last_commit" ]; then
    NODATA=$((NODATA + 1))
    continue
  fi

  name=$(basename "$dir")

  if [ "$last_commit" -ge "$D30" ]; then
    ACTIVE="${ACTIVE}, ${name}"
    ACTIVE_N=$((ACTIVE_N + 1))
  elif [ "$last_commit" -ge "$D180" ]; then
    DORMANT_N=$((DORMANT_N + 1))
    [ "$DORMANT_N" -le 10 ] && DORMANT="${DORMANT}, ${name}"
  else
    ABANDONED_N=$((ABANDONED_N + 1))
    [ "$ABANDONED_N" -le 10 ] && ABANDONED="${ABANDONED}, ${name}"
  fi

  [ ! -f "${dir}CLAUDE.md" ] && NO_CLAUDE=$((NO_CLAUDE + 1))
done

DORMANT_SUFFIX=""
[ "$DORMANT_N" -gt 10 ] && DORMANT_SUFFIX=" and $((DORMANT_N - 10)) more"
ABANDONED_SUFFIX=""
[ "$ABANDONED_N" -gt 10 ] && ABANDONED_SUFFIX=" and $((ABANDONED_N - 10)) more"

echo ""
echo "### Projects (${DIR_TOTAL} dirs, ${GIT_TOTAL} git repos)"
echo "- Active (30d): ${ACTIVE_N}${ACTIVE:+ — ${ACTIVE:2}}"
echo "- Dormant (30-180d): ${DORMANT_N}${DORMANT:+ — ${DORMANT:2}${DORMANT_SUFFIX}}"
echo "- Abandoned (>6mo): ${ABANDONED_N}${ABANDONED:+ — ${ABANDONED:2}${ABANDONED_SUFFIX}}"
[ "$NODATA" -gt 0 ] && echo "- No data (empty/corrupt): ${NODATA}"
echo "- Missing CLAUDE.md: ${NO_CLAUDE}"

Step 3: CLAUDE.md Drift

Check if CLAUDE.md files are stale relative to their project's latest commit. Uses git commit dates (not filesystem mtime) for accuracy.

bash
NOW=$(date +%s)
STALE_LIST="" STALE_N=0
UNTRACKED_LIST="" UNTRACKED_N=0

while IFS= read -r claude_file; do
  project_dir=$(dirname "$claude_file")
  while [ ! -d "${project_dir}/.git" ] && [ "$project_dir" != "$HOME/ai_projects" ]; do
    project_dir=$(dirname "$project_dir")
  done
  [ -d "${project_dir}/.git" ] || continue

  claude_commit=$(git -C "$project_dir" log -1 --format=%ct -- "$claude_file" 2>/dev/null)
  project_commit=$(git -C "$project_dir" log -1 --format=%ct 2>/dev/null)
  [ -z "$project_commit" ] && continue

  if [ -z "$claude_commit" ]; then
    rel_path="${claude_file#$HOME/}"
    UNTRACKED_LIST="${UNTRACKED_LIST}  - ~/${rel_path}\n"
    UNTRACKED_N=$((UNTRACKED_N + 1))
    continue
  fi

  diff=$((project_commit - claude_commit))
  if [ "$diff" -gt $((90*86400)) ]; then
    rel_path="${claude_file#$HOME/}"
    claude_date=$(date -r "$claude_commit" +%Y-%m-%d)
    project_date=$(date -r "$project_commit" +%Y-%m-%d)
    STALE_LIST="${STALE_LIST}  - ~/${rel_path} (last: ${claude_date}, project: ${project_date})\n"
    STALE_N=$((STALE_N + 1))
  fi
done < <(find ~/ai_projects -maxdepth 2 -name CLAUDE.md 2>/dev/null)

echo ""
echo "### CLAUDE.md Drift"
echo "- Stale instructions (>90d behind project): ${STALE_N}"
[ -n "$STALE_LIST" ] && printf "$STALE_LIST"
echo "- Untracked: ${UNTRACKED_N}"
[ -n "$UNTRACKED_LIST" ] && printf "$UNTRACKED_LIST"

Step 4: Mac Mini Health

SSH to agents-mac-mini and check services. Uses -T to avoid pseudo-tty and ECOSYS: prefix to filter iTerm escape codes from the output.

Custom services on the Mac Mini use various prefixes (com.server.*, com.telegram-agent.*, com.photopulse.*, com.health.*, com.temporal.*, ai.hermes.*, etc.), so count all non-Apple LaunchAgents.

bash
echo ""
echo "### Mac Mini"

MINI_OUTPUT=$(ssh -T -o ConnectTimeout=5 -o BatchMode=yes mac-mini 'export TERM=dumb; thumb=$(curl -s --max-time 3 -o /dev/null -w "%{http_code}" http://localhost:8080/ 2>/dev/null); viz=$(curl -s --max-time 3 -o /dev/null -w "%{http_code}" http://localhost:8081/ 2>/dev/null); agents=$(launchctl list 2>/dev/null | tail -n +2 | awk "{print \$3}" | grep -cv "^com\.apple" || echo 0); hdb_size=$(stat -f %z ~/ai_projects/health-import/health.db 2>/dev/null || echo 0); echo "ECOSYS:${thumb}|${viz}|${agents}|${hdb_size}"' 2>/dev/null)

DATA=$(echo "$MINI_OUTPUT" | grep "^ECOSYS:" | sed 's/^ECOSYS://')

if [ -z "$DATA" ]; then
  echo "- Status: UNREACHABLE (SSH failed)"
else
  IFS='|' read -r THUMB VIZ AGENTS HDB_SIZE <<< "$DATA"
  [ "$THUMB" = "200" ] && echo "- Thumb server (:8080): UP" || echo "- Thumb server (:8080): DOWN (${THUMB})"
  [ "$VIZ" = "200" ] && echo "- Viz server (:8081): UP" || echo "- Viz server (:8081): DOWN (${VIZ})"
  echo "- Custom LaunchAgents: ${AGENTS}"
  if [ "$HDB_SIZE" -gt 0 ] 2>/dev/null; then
    HDB_GB=$(echo "scale=1; ${HDB_SIZE}/1073741824" | bc)
    echo "- Health DB: ${HDB_GB} GB"
  else
    echo "- Health DB: NOT FOUND"
  fi
fi

Step 5: Attention Summary + Daily Note

After printing the full terminal report, add an attention summary highlighting anything that needs action:

### Attention
- [list any: broken symlinks, services DOWN, stale CLAUDE.md, Mac Mini unreachable]
- If nothing needs attention: "All clear."

Then compose a summary and write it to today's daily note.

Get today's date first:

bash
TODAY=$(date +"%Y%m%d")

Build the summary block from the results you collected in Steps 1-4 (re-read the terminal output above — variables don't carry across bash blocks). Format:

markdown
## Ecosystem

Skills: N active / N recent / N stale / N broken
Projects: N active / N dormant / N abandoned
CLAUDE.md drift: N stale, N untracked
Mac Mini: [status summary]

_Ecosystem audit · YYYY-MM-DD_

Write to ~/Brains/brain/Daily/${TODAY}.md:

  • If ## Ecosystem section already exists, replace everything from ## Ecosystem to the next ## heading (or - - - separator)
  • If it doesn't exist, insert above the first - - - separator
  • If any red flags (broken symlinks > 0, services down, stale CLAUDE.md > 3), prepend > [!warning] Ecosystem issues detected

Frequently asked questions

What does the Ecosystem AI skill do?

Audit the Claude Code ecosystem — skill health and staleness, project activity pulse, CLAUDE.md instruction drift, Mac Mini service status. Use this skill whenever the user asks about ecosystem health, stale skills, abandoned projects, system status, infrastructure check, "what's broken", "what's stale", "how's my setup", or any request to review the state of their Claude Code environment. Also triggers on "/ecosystem", "audit my ecosystem", "ecosystem health", "ecosystem audit".

Why use Ecosystem on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glebis/claude-skills/tree/main/ecosystem. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ecosystem?

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 Ecosystem?

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

Is the Ecosystem AI skill free?

Yes. It is published on GitHub by glebis 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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