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.
npx agentmods add skills/heliohq/ship/autonpx skills add heliohq/ship --skill autogit clone --depth 1 https://github.com/heliohq/shipWrote 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.
[](https://agentmods.dev/skills/heliohq/ship/auto)<a href="https://agentmods.dev/skills/heliohq/ship/auto"><img src="https://agentmods.dev/badge/skills/heliohq/ship/auto.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00051 | $0.00333 |
| Opus 5 | $0.00026 | $0.00167 |
| Sonnet 5 | $0.00010 | $0.00067 |
| Haiku 4.5 | $0.00005 | $0.00033 |
Grade A, and why
auto scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Ship: Auto
Full staged workflow for explicit end-to-end production delivery.
Execution
Your cwd is the user's repo, not the plugin — a bare relative path will
not find the orchestrator. Set SKILL_DIR to this skill's base
directory (announced as "Base directory for this skill" when the skill
loaded), then run the shared stage-aware orchestrator:
SKILL_DIR="<base directory from the skill invocation>"
SHIP_ORCH="$SKILL_DIR/../../scripts/auto-orchestrate.sh"
if [ -f .ship/ship-auto.local.md ]; then
bash "$SHIP_ORCH" resume
else
bash "$SHIP_ORCH" init '<user requirement goes here>'
fi
Then follow the /ship:auto dispatch loop: read PROMPT_FILE, dispatch the
agent, classify the report card, and call complete <PHASE>.
Standalone Skill Boundary
/ship:auto is only for full end-to-end runs. For a single phase (design, dev,
E2E, review, QA, refactor, handoff), invoke that /ship:* skill directly
instead of routing through auto.
What ships with it
10 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.
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.
- 4d ago First seen · 43 lines · 51 tokens per session scan A 59a23008ded5
auto is a skill published in the GitHub repository heliohq/ship (92 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 333 once invoked, about $0.0003 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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