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 jesseposner/metacraft --skill session-lifecyclegit clone --depth 1 https://github.com/jesseposner/metacraftWrote 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/jesseposner/metacraft/session-lifecycle)<a href="https://agentmods.dev/skills/jesseposner/metacraft/session-lifecycle"><img src="https://agentmods.dev/badge/skills/jesseposner/metacraft/session-lifecycle/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.
<a href="https://agentmods.dev/skills/jesseposner/metacraft/session-lifecycle"><img src="https://agentmods.dev/badge/skills/jesseposner/metacraft/session-lifecycle.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00026 | $0.00970 |
| Opus 5.5 | $0.00010 | $0.00388 |
| Sonnet 5.5 | $0.00005 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
session-lifecycle 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 2mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Lifecycle
Every session has a beginning, a middle, and an end. Treat each one deliberately, or entropy will treat them for you.
What this is
A protocol for the arc of a working session with an AI agent. Not a rigid checklist, but a set of practices that prevent the two most common failures: starting without context (cold start amnesia) and ending without capture (lost work).
The deeper point: context windows are not infinite, memory compaction is not optional, and sessions end whether you plan for it or not. Design around these constraints instead of being surprised by them.
This covers the full arc. For a lighter mid-session pause, see Gather.
When to use it
- Every session. Scale the ceremony to the session's weight, but the pattern always applies.
- Especially: long sessions, multi-phase work, anything that will span multiple compaction cycles.
The pattern
Starting
-
Load persistent context. Read memory files, project state, any handoff notes from previous sessions. Don't trust what you remember from last time: trust what's written down.
-
Check for in-flight work. Did the last session leave something running? An eval, a build, a long process? Harvest those results before starting new work. Orphaned outputs are wasted compute.
-
Orient. Read the roadmap, the task list, the current priorities. Ask: "What matters most right now?" The answer determines the session, not momentum from last time.
-
Verify the environment. Is the system in the state you expect? Running processes, git status, server state. Surprises discovered mid-session cost more than surprises discovered at the start.
During
-
Log as you go. Record experiments, decisions, and results immediately, even failures. If it's worth trying, it's worth recording. The log is for future-you, who won't remember why you tried that thing.
-
Manage compaction deliberately. In long sessions, context will compress. Don't let auto-compaction surprise you. When it's approaching, run the handoff cycle yourself:
- Farewell: the agent wraps up its current thread, surfaces anything unsaid, captures final state
- Log: produce a structured handoff document: where things stand, what's decided, what's next, any critical context the new session needs
- Compact: trigger compaction manually, on your terms
- Re-orient: the agent comes back into a fresh context, re-reads persistent files, re-establishes the frame
- Load the log: paste the handoff document into the new context so nothing is lost
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.
- 2mo ago First seen · 66 lines · 26 tokens per session scan A 4d60eb7e3886
session-lifecycle is a skill published in the GitHub repository jesseposner/metacraft (11 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 970 once invoked, about $0.0001 per session on Opus 5.5. 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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