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 skills add appautomaton/automaton --skill auto-resumegit clone --depth 1 https://github.com/appautomaton/automatonWrote 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/appautomaton/automaton/auto-resume)<a href="https://agentmods.dev/skills/appautomaton/automaton/auto-resume"><img src="https://agentmods.dev/badge/skills/appautomaton/automaton/auto-resume.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00021 | $0.01016 |
| Opus 5 | $0.00010 | $0.00508 |
| Sonnet 5 | $0.00004 | $0.00203 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
auto-resume 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
auto-resume
Session recovery. Rebuilds context from durable artifacts, not memory or guessing.
First action: run node .agent/.automaton/scripts/get-context.mjs from the project root.
Preamble
auto-resume rebuilds context from durable artifacts, not from the user's description or the agent's training data. It does not modify artifacts, advance the stage, or start new work. It loads canonical artifacts in dependency order (spec first, then design, then plan) and reports what it found, what was blocked, and what comes next.
Loading discipline: start with artifacts needed for the current stage. Read project files when understanding the codebase helps rebuild accurate context for the next action. Read .agent/.automaton/references/CONTEXT-BUDGET.md when wider reads threaten context pressure.
Quality Gate
Before producing the recovery summary:
- Trust durable artifacts over memory.
- Report stale pointers plainly.
- Recommend a next skill only when recovered state has incomplete or blocked work. For verified completion, report no next lifecycle skill.
- Read
references/quality.mdwhen the summary becomes narrative recap.
Do
Load State
Halt and report when:
.agent/does not exist orcurrent.jsonis missing.
Recommend automaton install to scaffold .agent/, then stop. Do not attempt recovery without a state file.
If work is complete or absent, read .agent/steering/ROADMAP.md only to surface pending phases as context.
Verify Artifact Integrity
Check that canonical_spec, canonical_design, and canonical_plan resolve when present. If any pointer is stale, report it plainly. Recommend auto-frame for missing SPEC.md or auto-plan for missing PLAN.md.
Load Artifacts
Treat current.json as the only source for active change, stage, and canonical artifact pointers. Load artifacts in dependency order and stop at the current stage. Read references/artifact-order.md for the full stage table.
Reconcile Execution Ledger
What ships with it
3 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.
- 8d ago First seen · 103 lines · 21 tokens per session scan A 5c14213efb63
auto-resume is a skill published in the GitHub repository appautomaton/automaton (21 stars, last pushed 21d ago), licensed MIT. It adds 21 tokens to every session and 1,016 once invoked, about $0.0001 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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