Agent skill

Fleet Control

by aeonfun in aeonfun/aeon

Operate managed Aeon instances from memory/instances.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard).

MITAuto-check passed

Install Fleet Control

skills CLI
$ npx skills add aeonfun/aeon --skill fleet-control -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aeonfun/aeon fleet-control --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fleet-control .claude/skills/fleet-control && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fleet-control
GitHub stars
767
Token cost
~5.6k tokens
SKILL.md length
2,056 words
Files
1
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

Operate managed Aeon instances from memory/instances.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard).

  • SKILL.md covers Shared preamble (every run), Control-view pre-flight…, Health Check Mode (default —… and Dispatch Mode (control view), plus 8 more sections
  • Calls gh and node; reaches github.com; needs GITHUB_TOKEN and GH_TOKEN

What it does

Fleet Control is an agent skill from aeonfun/aeon. Operate managed Aeon instances from memory/instances.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard).

Its SKILL.md is about 5.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: The most autonomous AI agent framework: runs unattended on GitHub Actions, self-healing skills, drives Claude Code, Grok, Codex & more. No approval loops. Configure once, forget… The licence is MIT.

Example prompts

  • “/fleet-control”

Requirements

  • A credential in GITHUB_TOKEN
  • A credential in ANTHROPIC_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit f252074. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh
    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GITHUB_TOKEN
    • GH_TOKEN
    • ANTHROPIC_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fleet Control loads about 5.6k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 2,056 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~5.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aeonfun/aeon at commit f252074, republished under its MIT licence (© aeonfun). 2,056 words, ~5,597 tokens.

Download SKILL.mdSave it as .claude/skills/fleet-control/SKILL.md (or your agent's skills folder).
name
fleet-control
description
Operate managed Aeon instances from memory/instances.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard).
metadata.title
Fleet Control
metadata.category
core
metadata.tags
dev, meta, fleet, report, cost
metadata.requires
GH_READ_PAT?
<!-- autoresearch: variation B — sharper output: verdict line + delta vs prior + per-instance action column + state-change-gated notify -->

${var} — Command / view selector. Empty (or unrecognized) → Health Check (default control view). status → full Status Mode (control view). dispatch <instance|*> <skill> [var=<value>] → Dispatch Mode: trigger a skill on one child or all healthy/degraded children (control view). scorecard → Scorecard Mode: fleet-wide runs/tokens/cost/reliability scorecard with day-over-day deltas + alerts (scorecard view).

Today is ${today}. Operate the fleet of Aeon instances registered in memory/instances.json. The control view (health/status/dispatch) is decision-ready: every run leads with a verdict, then a delta vs prior check, then per-instance lines that name the next concrete action. The scorecard view publishes the daily fleet-wide cost/reliability scorecard.

The fleet is discovered at runtime, never hardcoded: it is this repo ("self") plus every non-archived entry in memory/instances.json (the registry fleet-control and spawn-instance maintain). With zero managed instances the scorecard simply covers the single self repo — still useful.

Shared preamble (every run)

  1. Read memory — read memory/MEMORY.md for high-level context and scan the last ~3 days of memory/logs/ for recent activity; don't re-report a signal already logged there.

  2. Voice — if soul/SOUL.md and soul/STYLE.md exist and are populated, read them and match the operator's voice in every notification. If they are empty templates or absent, use a clear, direct, neutral tone — terse, lowercase, no fluff.

  3. Parse ${var} → mode:

    • empty / unrecognized → Health Check Mode (control view; default)
    • exactly status → Status Mode (control view)
    • starts with dispatch → Dispatch Mode (control view)
    • exactly scorecard → Scorecard Mode (scorecard view)
  4. Route:

    • Health Check / Status / Dispatch → run the Control-view pre-flight below, then the matching mode section. These modes make live gh calls.
    • Scorecard → skip the control-view pre-flight entirely and jump straight to Scorecard Mode, which gathers its own data in-run via node scripts/fleet-scorecard.mjs.

Control-view pre-flight (health / status / dispatch only)

  1. Verify gh auth — gh auth status must succeed. If not, log FLEET_NO_AUTH to memory/logs/${today}.md and notify Fleet Control: gh auth missing — check GITHUB_TOKEN secret. Stop.

  2. Check rate limit — REMAINING=$(gh api rate_limit --jq '.resources.core.remaining'). If REMAINING < 50, log FLEET_RATE_LIMITED:remaining=${REMAINING} and notify a one-line warning, then stop.

  3. Load the registry — read memory/instances.json. If the file is missing, write {"instances": []} to bootstrap. If .instances is absent or []:

    • Log FLEET_EMPTY: no managed instances to memory/logs/${today}.md.
    • Stop. Do NOT notify.
  4. Load prior state — read memory/state/fleet-control-state.json (create the directory and file with {"instances": {}, "last_full_summary_date": ""} if missing). Shape:

    json
    {
      "instances": {
        "<name>": { "health": "<status>", "last_checked": "<ISO>", "consecutive_unreachable": 0 }
      },
      "last_full_summary_date": "YYYY-MM-DD"
    }

Health Check Mode (default — control view)

For each registered instance, skip rows with archived: true from per-instance work (count them separately). Run the three calls per instance in parallel using & + wait and write each to /tmp/fleet/${SAFE}.{repo,runs,cron}.json:

a. Repo metadata:

bash
gh api "repos/${REPO}" \
  --jq '{full_name, pushed_at, archived, default_branch, open_issues_count}' \
  > "/tmp/fleet/${SAFE}.repo.json" 2>"/tmp/fleet/${SAFE}.repo.err" &

b. Workflow runs in last 24h (precise window, not "last 5"):

bash
SINCE=$(date -u -d '24 hours ago' +%Y-%m-%dT%H:%M:%SZ)
gh api "repos/${REPO}/actions/runs?created=>${SINCE}&per_page=100&exclude_pull_requests=true" \
  --jq '{total_count, runs:[.workflow_runs[]|{name,status,conclusion,created_at,html_url}]}' \
  > "/tmp/fleet/${SAFE}.runs.json" 2>"/tmp/fleet/${SAFE}.runs.err" &

c. Cron-state from child:

bash
gh api "repos/${REPO}/contents/memory/cron-state.json" --jq '.content' 2>"/tmp/fleet/${SAFE}.cron.err" \
  | base64 -d > "/tmp/fleet/${SAFE}.cron.json" &

wait after launching all three for an instance (or batch across all instances if you trust your parallelism — keep ≤16 concurrent calls to stay under rate limit).

Classify each instance with precise thresholds:

  • unreachable — repo metadata call returned non-zero (404/403/etc.)
  • archived — repo metadata returns archived: true
  • pending_secrets — runs.total_count == 0 for the 24h window AND repo pushed_at ≥ 7 days old (newly-spawned instances under 7 days stay unclassified-but-tracked)
  • stale — runs.total_count == 0 AND pushed_at > 7 days old AND not archived
  • degraded — ≥1 cron-state skill with consecutive_failures ≥ 3 OR (24h failure_count / total_count) ≥ 0.5 with total_count ≥ 2
  • warning — 24h failure_count ≥ 1 but ratio < 0.5
  • healthy — has runs in last 24h, all conclusions success or in_progress/queued, no degraded cron-state skills

For each instance compute a next_action (one short imperative phrase):

  • pending_secrets → add ANTHROPIC_API_KEY at https://github.com/${REPO}/settings/secrets/actions
  • degraded → investigate <skill_name> (<consecutive_failures>× in a row, last_error: <signature, ≤60 chars>)
  • warning → monitor — <N>/<Total> runs failed in 24h
  • stale → confirm intent: no runs in 24h, last push <relative_date>; archive or re-enable — if it should be running, dispatch aeon-doctor to that instance (Dispatch Mode) to lint for a silent config bug (unquoted schedule: / duplicate key / broken entry) before assuming it's abandoned
  • unreachable → verify access: <reason from repo.err>
  • healthy → none
  • archived → none (archived)

Compute delta vs prior state (per-instance prior.health vs current.health):

  • NEW — instance not in prior state
  • DEGRADED — was healthy/warning, now degraded/unreachable/stale/pending_secrets
  • RECOVERED — was degraded/unreachable/stale/pending_secrets, now healthy/warning
  • DROPPED — was in prior state, no longer in registry
  • (no change → no delta line)

Update the registry — write back health, last_checked (ISO UTC), and next_action per instance to memory/instances.json. Preserve all other fields (purpose, parent, created, skills_enabled, etc.).

Update the state file — write the current per-instance health snapshot to memory/state/fleet-control-state.json. Update last_full_summary_date to today only when this run notifies. Increment consecutive_unreachable for unreachable instances; reset to 0 otherwise.

Log to memory/logs/${today}.md (under the consolidated heading — see Log section):

### fleet-control
- Mode: health check
- Verdict: [FLEET_OK | NEEDS_ATTENTION:N]
- Sizes: total=N, healthy=N, warning=N, degraded=N, stale=N, pending=N, unreachable=N, archived=N
- Deltas: [list NEW/DEGRADED/RECOVERED/DROPPED, or "none"]
- Sources: gh=ok, rate_remaining=N

Notification gate — send the notification if any of:

  • len(deltas) > 0
  • today != prior last_full_summary_date (first check of UTC day → daily rollup)
  • any current instance is degraded or unreachable

Otherwise skip notify (silent no-op when nothing changed mid-day — operator isn't trained to ignore).

Notification body (when sent):

*Fleet Control — ${today}*
Verdict: <FLEET_OK | NEEDS_ATTENTION:N>

[If deltas exist]:
What changed:
- NEW: <name> (<repo>) — <health>
- DEGRADED: <name> — was <prior>, now <current>: <reason>
- RECOVERED: <name> — was <prior>, now <current>
- DROPPED: <name> — no longer in registry

Fleet (N total):
- <name> [<HEALTH>]: <repo> — <next_action>
- ...

[If first-of-day rollup]:
Counts: healthy <H> · warning <W> · degraded <D> · stale <S> · pending <P> · unreachable <U> · archived <A>

Sources: gh=ok · rate_remaining=N

Cap the per-instance list at 12 lines; if more, append ...and N more — see memory/instances.json. Always include archived in counts; never list archived rows in the per-instance section.


Dispatch Mode (control view)

Parse var: dispatch <instance|*> <skill> [var=<value>].

Resolve targets:

  • If <instance> is *, target = every registry entry whose current health is healthy, warning, or degraded (skip unreachable, stale, pending, archived).
  • Otherwise, exact name match against the registry. Not found → notify Fleet Dispatch: instance '<name>' not in registry and stop.

For each target instance:

  1. Validate skill exists in child:

    bash
    gh api "repos/${REPO}/contents/skills/${SKILL}/SKILL.md" >/dev/null 2>&1 \
      || { OUTCOME="missing_skill"; continue; }
  2. Check skill is enabled in child's aeon.yml (best-effort warning, not a block — workflow_dispatch can override enabled: false):

    bash
    gh api "repos/${REPO}/contents/aeon.yml" --jq '.content' 2>/dev/null | base64 -d \
      | grep -E "^[[:space:]]*${SKILL}:.*enabled:[[:space:]]*true" >/dev/null \
      || NOT_ENABLED_WARN=1
  3. Trigger the skill:

    bash
    if [ -n "$DISPATCH_VAR" ]; then
      gh workflow run aeon.yml --repo "${REPO}" -f skill="${SKILL}" -f var="${DISPATCH_VAR}" \
        && OUTCOME="dispatched" || OUTCOME="api_failed:$?"
    else
      gh workflow run aeon.yml --repo "${REPO}" -f skill="${SKILL}" \
        && OUTCOME="dispatched" || OUTCOME="api_failed:$?"
    fi

Collect per-target outcomes: dispatched | missing_skill | api_failed:<code> (with optional not_enabled_warn flag).

Log:

### fleet-control
- Mode: dispatch
- Command: dispatch <inst|*> <skill> [var=...]
- Targets: N
- Dispatched: N | missing_skill: N | api_failed: N
- Per-target: [<name>: <outcome>, ...]

Notify (always, in dispatch mode):

*Fleet Dispatch*
Command: dispatch <inst|*> <skill>
Targets: <N> — Dispatched: <N>
Successful: <comma-sep names>
[If failures]:
Failed: <name>: <reason>, ...
[If not_enabled_warn]:
Warning: <name> has skill disabled in aeon.yml — dispatched anyway

If 0 dispatched out of N targets, the verdict line reads Fleet Dispatch: 0/${N} — see failures below and exit code logged is FLEET_DISPATCH_FAILED:no_targets_succeeded.


Status Mode (control view)

Generate the comprehensive snapshot, but make it scannable.

For each registered instance (skip archived from detail blocks but count them in the summary), gather in parallel:

  • Repo meta: stargazers_count, pushed_at, open_issues_count, default_branch
  • Last 10 workflow runs:
    bash
    gh api "repos/${REPO}/actions/runs?per_page=10&exclude_pull_requests=true" \
      --jq '[.workflow_runs[]|{name,status,conclusion,created_at,html_url}]'
  • Full cron-state.json
  • aeon.yml (parse enabled skills)
  • Last 5 commits (one-line gh api repos/${REPO}/commits?per_page=5 --jq ...)

Compute the same delta block, but compare against the most recent prior output/articles/fleet-status-*.md (parse the per-instance health rows; if none exists, mark the section "no prior status to diff against").

Write to output/articles/fleet-status-${today}.md:

markdown
# Fleet Status — ${today}

## Verdict
<one line: FLEET_OK | NEEDS_ATTENTION:N | DEGRADED:N — top issue first>

## Top Issue
<one paragraph: the single highest-priority instance and what it needs, OR "none">

## Fleet Health
| Instance | Repo | Health | Last Active | Skills | Open Action |
|----------|------|--------|-------------|--------|-------------|

## What Changed Since Last Status
<list of NEW/DEGRADED/RECOVERED/WENT_STALE/DROPPED instances since prior fleet-status article, or "no changes">

## Per-Instance Detail

### <name> — <repo>
- Purpose: <from registry>
- Health: <status>, last checked <ISO>
- Last 10 runs:
  | Skill | Status | Conclusion | When |
  |-------|--------|-----------|------|
- Skills enabled: <comma list>
- Recent commits:
  - <sha> <message>
- Action: <next_action>

## Counts
| Metric | Value |
|--------|-------|

## Sources
gh=ok · rate_remaining=N · registry=N instances · prior_status=<filename or "none">

Log:

### fleet-control
- Mode: status
- Article: output/articles/fleet-status-${today}.md
- Verdict: <line>
- Sizes: total=N, healthy=N, ...

Notify (always, in status mode):

*Fleet Status — ${today}*
<verdict>
Top issue: <one line, or "none">
Counts: healthy <H> · warning <W> · degraded <D> · stale <S> · pending <P> · unreachable <U>
Article: output/articles/fleet-status-${today}.md

Scorecard Mode (scorecard view)

Publish the daily fleet scorecard to memory/scorecard.md and append a trend row to memory/scorecard-history.csv. (Ran daily at 13:00 UTC as its own dispatch when this skill is scheduled with var: scorecard.)

0. Gather the data in-run

Run the committed collector — it discovers the fleet (self + non-archived memory/instances.json), fetches each repo's workflow runs + skill count + token-usage.csv from the GitHub API, computes the pricing/aggregation, and writes the tables. It reads its token from the environment (GH_READ_PAT — the read-only PAT declared in this skill's requires:, needed to read private fleet members — falling back to GH_TOKEN/GITHUB_TOKEN), so no secret ever touches a command line:

bash
node scripts/fleet-scorecard.mjs   # → /tmp/fleet-scorecard/{scorecard-body.md,metrics.json}

The deterministic maths lives in the script (not this run) — do not recompute or alter its numbers. A repo the token can't read is simply absent from the tables rather than crashing the collector.

Inputs (produced by step 0 — read these)
  • /tmp/fleet-scorecard/scorecard-body.md — the computed markdown tables (Fleet totals, Per-repo, Top skills by cost, Least reliable skills). Authoritative — do not recompute or alter them.
  • /tmp/fleet-scorecard/metrics.json — today's key totals: total_runs, total_failures, generations, prompt_tokens, cached_tokens, completion_tokens, total_tokens, est_cost_usd, cache_discount_usd.

If /tmp/fleet-scorecard/scorecard-body.md is missing or empty, the collector failed or resolved an empty fleet — write a one-line note to /tmp/skill-result.txt saying so and stop (do not overwrite the existing scorecard, do not notify).

Show full SKILL.md (821 more words)Show less
Steps
1. Load today's metrics and yesterday's baseline
  • Read /tmp/fleet-scorecard/metrics.json (today).
  • Read the last row of memory/scorecard-history.csv if it exists (the previous run's metrics) to compute deltas. If the file doesn't exist yet, this is the first run — deltas are "—".
2. Compute day-over-day deltas

For total_runs, total_failures, generations, total_tokens, est_cost_usd, cache_discount_usd, compute today − previous. Format as signed (e.g. +312 runs, +$148, +5 failures). These are cumulative all-time figures, so deltas show the last ~24h of activity.

3. Build the Alerts block

Scan the computed tables in scorecard-body.md and flag:

  • Any skill in "Least reliable skills (last 14d)" with fail rate ≥ 25% (call it out by name + repo + rate). That table is already windowed to 14 days, so long-resolved incidents won't trigger false alarms — anything listed there is a current problem worth surfacing.
  • Any cost spike: est_cost_usd delta > 1.5× the median daily delta from history (if ≥7 history rows exist), or just note the day's cost increase otherwise.
  • If total_failures rose by more than 10 since yesterday, flag it.
  • If no issues, write ✅ No anomalies — fleet healthy.
4. Write memory/scorecard.md

Structure (overwrite the file):

# 🛰️ Aeon Fleet Scorecard — as of ${today}

_Auto-generated daily by skills/fleet-control (scorecard view). Tokens reported OpenRouter-style (cached_tokens ⊆ prompt_tokens)._

## Since last update (~24h)
| Metric | Δ |
|---|---:|
| Runs | <signed> |
| Failures | <signed> |
| Generations | <signed> |
| Total tokens | <signed, humanized> |
| Est. cost | <signed $> |
| Cache discount | <signed $> |

## Alerts
<the alerts block from step 3>

<PASTE the full contents of /tmp/fleet-scorecard/scorecard-body.md verbatim here>

---
_Sources: GitHub Actions run history + each repo's `memory/token-usage.csv`. Fleet resolved from memory/instances.json + self. Cost = Anthropic list price (estimate)._
5. Append the trend row

Append one line to memory/scorecard-history.csv (create with a header if it doesn't exist):

date,total_runs,total_failures,generations,prompt_tokens,cached_tokens,completion_tokens,total_tokens,est_cost_usd,cache_discount_usd

Use ${today} for the date and the values straight from metrics.json. Append, never rewrite prior rows.

6. Notify

Write a terse daily pulse to /tmp/scorecard-notify.md and send it with ./notify -f /tmp/scorecard-notify.md. One short paragraph — today's totals (runs, est. cost, total tokens), the headline deltas, and any alert. Example shape: "fleet at 12.5k runs, ~$7.8k notional. +312 runs / +$148 since yesterday. cost-report still failing (88% fail). caching saved ~$43k." Also copy this text to /tmp/skill-result.txt so the framework captures it.

7. Memory log

Append the scorecard entry under the consolidated ### fleet-control heading in memory/logs/${today}.md (see Log section), noting the headline numbers (so future skills like self-improve/reflect see it).

Scorecard notes
  • Numbers come only from the collector's output files (/tmp/fleet-scorecard/*) — never invent or estimate figures yourself.
  • The scorecard is cumulative/all-time; the deltas are what make the daily run useful.
  • GitHub Actions retains runs ~90 days, so the run history is a rolling window; the token CSVs are the durable record committed in each repo.

Log

All modes append under one ### fleet-control heading in memory/logs/${today}.md, with a - Mode: discriminator line (the health loop parses this shape). Use the per-mode block shown in each mode section above. For Scorecard Mode use:

### fleet-control
- Mode: scorecard
- Scorecard: memory/scorecard.md updated — <total_runs> runs, ~$<est_cost_usd> notional, <total_tokens humanized>
- Deltas: <+runs> / <+$cost> since yesterday
- Alerts: <alert summary or "none">

Exit taxonomy

Every run logs exactly one of these to memory:

  • FLEET_CONTROL_OK — health/status/dispatch/scorecard completed normally
  • FLEET_EMPTY — no instances in registry (silent stop; control view)
  • FLEET_NO_AUTH — gh auth missing (control view)
  • FLEET_RATE_LIMITED:remaining=N — abandoned to preserve quota (control view)
  • FLEET_DISPATCH_OK:N/M — dispatched N of M targets
  • FLEET_DISPATCH_FAILED:<reason> — dispatch produced 0 dispatches
  • FLEET_SCORECARD_EMPTY — collector produced no data (empty fleet / all repos unreadable); scorecard skipped without overwriting or notifying

Network note

Control view (health / status / dispatch): always use gh api over raw curl (it handles auth internally, so no $SECRET appears on the command line for the Bash permission layer to refuse). All cross-repo calls go through gh api or gh workflow run. No outbound HTTP needed beyond what gh does internally.

Scorecard view: gathers its data in-run by executing node scripts/fleet-scorecard.mjs (step 0), which fetches workflow runs + token usage from the GitHub API and computes the tables into /tmp/fleet-scorecard/. The collector authenticates with GH_READ_PAT when set (a read-only PAT with cross-repo scope, declared in this skill's requires: and injected into the run) so private managed instances are readable; when unset, the run's GH_TOKEN (= GH_GLOBAL) reads the same private members, the standard single-key setup. It reads the token from process.env internally, so the secret never appears on a command line. A repo the token can't read is simply absent from the tables rather than crashing the collector.

Required env vars

GH_READ_PAT (optional, read-only) — declared in requires: and read from process.env by scripts/fleet-scorecard.mjs (scorecard view) to reach private managed instances; it falls back to GH_GLOBAL/GH_TOKEN/GITHUB_TOKEN (the run's repo-wide token, which also reads private members) when unset, and reads GITHUB_REPOSITORY to resolve "self". The control view relies on the workflow-provided GITHUB_TOKEN for its live gh calls.

Constraints

  • Never delete an instance from memory/instances.json automatically — only update fields. Even unreachable instances stay in the registry until the operator removes them by hand.
  • Preserve all registry fields not explicitly written by this skill (purpose, parent, created, skills_enabled, etc.).
  • Never write secrets to logs or notifications.
  • Cap notification length at ~30 lines; truncate the per-instance list with ...and N more when needed.
  • Health Check stays silent when nothing changed mid-day — the daily-rollup path handles the recurring "is everything fine?" question without spam.
  • Scorecard Mode never overwrites memory/scorecard.md when the collector output is missing/empty, and appends (never rewrites) prior rows in memory/scorecard-history.csv.
  • Do not change the skill's tags, var semantics, or schedule without strong justification.

Write complete, working code. No TODOs or placeholders.

Output

After completing any task, end with a ## Summary listing what you did, files created/modified, and any follow-up actions needed.

© aeonfun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/fleet-control of aeonfun/aeon.

Open the folder on GitHubat commit f252074

Compare with similar skills

Fleet Control next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fleet Control compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Health ManagementLeoYeAI/openclaw-master-skills2.2k—~11kAutomated safety check: NotesMIT
Fleet Manager for Agent Sessionsasgeirtj/system_prompts_leaks69k—~2.5kAutomated safety check: PassCC0-1.0
Operations ManagerFerroxLabs/wayland608—~5kAutomated safety check: PassApache-2.0
Fleet Operationsautonomous-ai/openharness1.1k—~1.4kAutomated safety check: PassMIT

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More from aeonfun/aeon

All 82 skills in this repo
  • Browses open tasks on the TaskMarket agent-worker market and, with explicit operator approval, creates tasks, tracks submissions and submits finished work.

    767 GitHub stars~1.4k tokensUpdated today
    Auto-check passed
  • Sets up and manages an Aeon agent instance that runs skills on a schedule through GitHub Actions: starting, rescheduling, debugging, editing skills and mining chat history.

    767 GitHub stars~8.8k tokensUpdated today
    Auto-check: warnings
  • Reads a Base Account's address, portfolio and transaction history through the Base MCP server, and stays strictly read-only in unattended Aeon runs, reporting only changes.

    767 GitHub stars~2.5k tokensUpdated today
    Auto-check passed
  • Audits every page of a site each day from its sitemap, scores on-page and technical SEO, checks duplicates across pages and reports what changed since the last run.

    767 GitHub stars~5.1k tokensUpdated today
    Auto-check passed
  • Action Converter

    aeonfun/aeon

    5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff gates

    767 GitHub stars~2.5k tokensUpdated today
    Auto-check passed
  • Aeon Config Doctor

    aeonfun/aeon

    Static linter for an Aeon instance's configuration that catches silent failures such as unquoted schedules, duplicate keys, unconfigured skills and broken MCP references.

    767 GitHub stars~3.3k tokensUpdated today
    Auto-check passed

Questions about Fleet Control

What does Fleet Control do?

Operate managed Aeon instances from memory/instances.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard). Fleet Control is an agent skill from aeonfun/aeon.json - health-check, dispatch, and status snapshots (control), plus a fleet scorecard of runs, tokens, cost, and reliability (scorecard).

How do I install Fleet Control in Claude Code?

Run `npx skills add aeonfun/aeon --skill fleet-control -a claude-code`. Or copy the skill folder (skills/fleet-control in aeonfun/aeon) into .claude/skills/fleet-control in your project. Claude Code loads it when a task matches its description.

How do I install Fleet Control in Codex?

Run `npx skills add aeonfun/aeon --skill fleet-control -a codex`. Or copy the skill folder (skills/fleet-control in aeonfun/aeon) into .agents/skills/fleet-control in your project. Codex loads it when a task matches its description.

Can I use Fleet Control in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aeonfun/aeon --skill fleet-control -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fleet-control, .gemini/skills/fleet-control, .github/skills/fleet-control and .opencode/skills/fleet-control in your project.

What does Fleet Control need to run?

Going by SKILL.md and its folder, Fleet Control needs the command-line tools its instructions call (gh and node) and credentials named GITHUB_TOKEN, GH_TOKEN and ANTHROPIC_API_KEY. Our summary lists: A credential in GITHUB_TOKEN; A credential in ANTHROPIC_API_KEY.

Does Fleet Control access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Fleet Control safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fleet Control use?

Fleet Control is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fleet Control use?

About 5.6k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fleet Control?

Skills that share tags, products or a category with Fleet Control: Codewhale Fleet Manager (codewhale-hq/Codewhale, 41k stars), Health Management (LeoYeAI/openclaw-master-skills, 2.2k stars), Fleet Manager for Agent Sessions (asgeirtj/system_prompts_leaks, 69k stars) and Operations Manager (FerroxLabs/wayland, 608 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fleet Control?

aeonfun (a GitHub organization) maintains it in aeonfun/aeon, which has 767 GitHub stars. The repository holds 82 skills in this directory. The repository was last updated on October 6, 2026.

Source: aeonfun/aeon on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.