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 alivecontext/alive --skill system-cleanupgit clone --depth 1 https://github.com/alivecontext/aliveWrote 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/alivecontext/alive/system-cleanup)<a href="https://agentmods.dev/skills/alivecontext/alive/system-cleanup"><img src="https://agentmods.dev/badge/skills/alivecontext/alive/system-cleanup/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/alivecontext/alive/system-cleanup"><img src="https://agentmods.dev/badge/skills/alivecontext/alive/system-cleanup.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00052 | $0.05499 |
| Opus 5.5 | $0.00021 | $0.02200 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (130 stars, last pushed 16d ago), licensed MIT. It adds 52 tokens to every session and 5,499 once invoked, about $0.0002 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.
Other skills, from other repositories
fresh-start
Continue in a new chat without losing anything — writes a compact handoff to paste into a fresh conversation. Use whenever the user wants to move to a new chat or conversation, hits usage limits, says replies got slow or Claude forgets earlier details, or the conversation has grown long.
token-check
Estimate how many tokens a pasted text, an attached file or the conversation so far takes, what it means for the context window and usage limits, and how to cut it. Use when the user asks how big something is in tokens, why they keep hitting limits, or whether a file is too large to send.
resuming-from-handoff
Use when user invokes /handoff-resume, says "pick up where we left off", "continue from last session", "what was I working on", or starts a session with a reference to prior work that may be captured in a previous handoff 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 and continue the interactive review loop 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.
slackcli
Read, send, search, and manage Slack workspaces with the slackcli binary. Installs slackcli if missing and checks or proposes authentication first. Use whenever the user mentions Slack or pastes a slack.com link.