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/anombyte93/atlas-session-lifecycle/v1npx skills add anombyte93/atlas-session-lifecycle --skill v1git clone --depth 1 https://github.com/anombyte93/atlas-session-lifecycleWrote 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/anombyte93/atlas-session-lifecycle/v1)<a href="https://agentmods.dev/skills/anombyte93/atlas-session-lifecycle/v1"><img src="https://agentmods.dev/badge/skills/anombyte93/atlas-session-lifecycle/v1.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 5.1 | $0.00055 | $0.07835 |
| Opus 5 | $0.00028 | $0.03918 |
| Sonnet 5 | $0.00011 | $0.01567 |
| Haiku 4.5 | $0.00006 | $0.00783 |
Grade A, and why
start 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 6d 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 — 868 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Init & Lifecycle Skill
Single entry point for project initialization and soul purpose lifecycle management. Two modes, auto-detected. User runs
/start— that is all.
Overview
This skill has two operational modes:
- Init Mode — Triggered when
session-context/does NOT exist. First-run bootstrap: captures soul purpose, organizes files, generates CLAUDE.md, integrates Claude/init, onboards Ralph Loop. - Reconcile Mode — Triggered when
session-context/DOES exist. Lifecycle management: refreshes CLAUDE.md via Claude/init, assesses soul purpose status, optionally verifies with doubt agent, harvests active context on closure.
Mode is auto-detected from directory state. No flags, no arguments. User just runs /start.
Hard Invariants
These hold at all times, in both modes:
- User authority is absolute — AI NEVER closes a soul purpose. Only suggests; user decides.
- Zero unsolicited behavior — Skill ONLY runs when user types
/start. No hooks, no proactive triggers. - Human-visible memory only — All state lives in files. Nothing hidden in chat memory.
- Idempotent — Safe to run multiple times. Re-running does not corrupt state.
- Templates are immutable — NEVER edit files in
~/claude-session-init-templates/. Copy only. - Reconcile mode is audit, not rewrite — Targeted changes only. Do not regenerate what already exists.
Rules
- Act as pure bootstrap + organizer in Init Mode, pure auditor in Reconcile Mode
- All persistent state lives in files, not chat memory
- If
session-context/files exist, do NOT overwrite unless explicitly instructed - If project is already organized (few files at root), skip file organization steps
- NEVER edit template files in
~/claude-session-init-templates/— they are immutable - Confirm nothing except the organization map (Init Step 5) and harvest promotions (Reconcile Step 5). Execute deterministically otherwise.
- NEVER auto-invoke doubt agent. Only suggest it. User accepts or skips.
- NEVER auto-close a soul purpose. Present options; user chooses.
- All heavy file operations MUST be delegated to Task agents (see Agent-Driven Execution below)
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.
- 6d ago First seen · 868 lines · 55 tokens per session scan A 1c5ea5f10d32
start is a skill published in the GitHub repository anombyte93/atlas-session-lifecycle (88 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 7,835 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.
Other skills, from other repositories
session-orchestrator
Session orchestrator on top of the recall index of all Claude Code sessions: finds a past session by task description, dives in (context, project, skill, artifacts) and acts in one of three modes — finish here, hand off the resume command, or open the session in a new terminal window. Use when user asks: "find the…
vibe-research
Deep research and market validation for app ideas. Use when starting a new project, validating an idea, or when the user says "research my idea", "validate my app", or "help me start a new project".
iterative-development
TDD iteration loops using Claude Code Stop hooks - runs tests after each response, feeds failures back automatically.
mnemos
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies.
python
Python development with ruff, mypy, pytest - TDD and type safety.
kn-research
Use when you need to understand existing code, find patterns, search project knowledge, investigate current external facts, or explore a large codebase before implementation.