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/system-cleanupnpx skills add alivecontext/alive --skill system-cleanupgit 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 | $0.00052 | $0.05499 |
| Opus 5 | $0.00026 | $0.02750 |
| Sonnet 5 | $0.00010 | $0.01100 |
| Haiku 4.5 | $0.00005 | $0.00550 |
Grade A, and why
alive:system-cleanup 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 3d 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 — 528 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tidy
System maintenance. Root health first, then one walnut at a time. Surfaces issues with recommended fixes — the human picks.
Not a dashboard (that's world). Not a search (that's find). Not session history (that's alive:session-history). Pure maintenance.
v3 Architecture Reference
Walnut structure is flat under _kernel/:
walnut-name/
_kernel/
key.md
log.md
insights.md
tasks.json
now.json
completed.json
bundle-a/
context.manifest.yaml
raw/
bundle-b/
context.manifest.yaml
There is NO _kernel/_generated/ subdirectory. There is NO bundles/ container directory. Bundles sit as direct children of the walnut root alongside _kernel/.
Task operations go through tasks.py, never by reading/writing task files directly:
tasks.py list --walnut {path}— all active tasks as JSONtasks.py list --walnut {path} --status active— filter by statustasks.py summary --walnut {path}— structured summary with counts
Projection rebuilds go through project.py:
project.py --walnut {path}— rebuilds_kernel/now.json
Three-Phase Flow
Phase 1: Root Audit (system-level, 7 checks — parallel subagents)
|
Phase 2: Walnut Summary (single subagent scans frontmatter, human picks)
|
Phase 3: Deep Audit (one walnut, 12 checks — parallel subagents)
Subagent Strategy (non-negotiable)
Tidy is read-heavy. Every phase uses subagents to keep the main context clean.
Discovery subagents MUST use subagent_type: "Explore" — Explore agents have direct file read access without needing Bash. Use Explore for all Phase 1, Phase 2, and Phase 3 discovery checks.
Fix-execution subagents MUST use subagent_type: "general-purpose" — only general-purpose agents have Write/Edit access. Dispatch one general-purpose agent per approved fix.
- Phase 1: Dispatch all 7 root checks as parallel subagents. Wait for all. Present results together — one line per passing check, expand on failures only.
- Phase 2: Single subagent reads all walnut frontmatter, returns the health table.
- Phase 3: Dispatch all 12 checks as parallel subagents (one check per subagent). Wait for all. Present results together — passing checks collapsed, failures expanded one at a time.
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.
- 3d ago First seen · 528 lines · 52 tokens per session scan A 738be21d87b4
alive:system-cleanup is a skill published in the GitHub repository alivecontext/alive (127 stars, last pushed 7d ago), licensed MIT. It adds 52 tokens to every session and 5,499 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
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-overhead
Audit the fixed context overhead every session starts with — system prompt, MCP tools, agents, CLAUDE.md, memory — measured from real transcript usage.