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/alivecontext/alive/savenpx skills add alivecontext/alive --skill savegit clone --depth 1 https://github.com/alivecontext/aliveWhat 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 | $0.00078 | $0.05490 |
| Opus 5 | $0.00039 | $0.02745 |
| Sonnet 5 | $0.00016 | $0.01098 |
| Haiku 4.5 | $0.00008 | $0.00549 |
Grade A, and why
alive:save 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 2d 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 — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Save
Checkpoint. Route the stash. Update state. Generate projections. Keep working.
Save is NOT a termination. The session continues. Save can happen multiple times. Each save increments the saves: counter and updates last_saved:. The stop hook only blocks when saves: 0 (never saved).
Flow
1. Read First (understand before acting)
Read these in parallel before presenting the stash or writing anything:
_kernel/now.json— which bundle is active? What was the context? Which tasks are urgent or active?_kernel/log.md— first ~100 lines (recent entries — what have previous sessions covered?)- Active bundle's
context.manifest.yaml— ifnow.jsonreports an active bundle, read its manifest
Do NOT read per-bundle task files directly — task data lives in now.json already (computed projection), or call tasks.py list --walnut {path} if you need specific detail. In v3, tasks are stored in tasks.json per walnut and per bundle, managed only through the supported task tooling (alive tasks promote for batched stash promotion in step 6c, scripts/tasks.py for direct edits like done / priority changes). Never edit tasks.json from the agent.
If _kernel/now.json does not exist: suggest running python3 "$ALIVE_PLUGIN_ROOT/scripts/project.py" --walnut {path} to generate it.
Standalone session (no walnut loaded): If no walnut was opened this session, the squirrel still has a stash to route. Ask: "Which walnut does this session belong to?" If the human names one, load its core files and proceed normally. If the human cannot name one, abort the save — on the fn-12 CLI-only save path, alive log prepend requires a concrete walnut (it only targets {walnut}/_kernel/log.md). Surface a bordered block explaining the save is blocked until a walnut is chosen; the stash stays in conversation and the squirrel YAML at .alive/_squirrels/ keeps walnut: null until the next save. Do NOT attempt to write to a world-level .alive/log.md — the CLI does not support that path.
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.
- 2d ago First seen · 371 lines · 78 tokens per session scan A 4e539efb41a0
alive:save is a skill published in the GitHub repository alivecontext/alive (127 stars, last pushed 6d ago), licensed MIT. It adds 78 tokens to every session and 5,490 once invoked, about $0.0004 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
crit-cli
Use when an agent needs to author or reply to crit inline comments programmatically (including multi-agent workflows commenting on shared code/plans/docs/proposals), publish or unpublish a crit review with crit share, sync a crit review to or from a GitHub PR or GitLab MR, or read/interpret a crit review JSON file.…
crit
Review code changes, a plan, a live page (running dev server), or a local HTML file with Crit inline comments and structured human feedback. Use only when the user explicitly invokes /crit or directly asks to use Crit; a generic review request does not count.
crit-story
Author a crit story only when the user explicitly invokes /crit-story or directly asks you to generate a crit story. Do not infer this skill from generic review, PR, or diff-review requests.
cco-config
View and tune CCO's behavior thresholds — re-read warnings, cache staleness, prompt-coach length bands, and the /cco-pack budget cap.
cco-budget
Configure token budget limits, auto-compact settings, and view current budget status (model-aware — Opus 4.8 default, full 1M context at standard price).
cco-patterns
Share learned file patterns across a team — export an anonymized digest of what's usually waste/useful/co-edited, and import a teammate's so a fresh clone benefits day one.