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/alive-prunenpx skills add alivecontext/alive --skill alive-prunegit 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/alive-prune)<a href="https://agentmods.dev/skills/alivecontext/alive/alive-prune"><img src="https://agentmods.dev/badge/skills/alivecontext/alive/alive-prune.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 | $0.00016 | $0.00415 |
| Opus 5 | $0.00008 | $0.00208 |
| Sonnet 5 | $0.00003 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
alive-prune 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 5d 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.
What it actually says
Weekly Prune
Suggest maintenance actions for log and insight hygiene.
Process
Log Chapter Synthesis
For each walnut, check _kernel/log.md:
- Count entries (## headings after frontmatter)
- If 50+ entries, suggest chapter synthesis
[walnut] log has [N] entries.
Suggest synthesizing older entries into _kernel/history/chapter-[nn].md?
1. Yes, synthesize
2. Not yet
Stale Insights
For each walnut, check _kernel/insights.md:
- Read each section
- Flag sections not referenced in recent log entries (last 30 days)
[walnut] insights:
"[section name]" -- not referenced in 45 days
1. Keep (still relevant)
2. Archive (move to bottom)
3. Remove
Stale Bundles
Find bundles in draft status older than 30 days:
[walnut]/[bundle] -- draft for 42 days
1. Advance (prototype)
2. Archive (done)
3. Kill (delete)
4. Leave
Output
If nothing needs attention: [SILENT]
If issues found, present one category at a time. Keep actionable.
Rules
- This is weekly, not urgent. Present calmly.
- Never auto-prune. Always suggest and wait.
- Chapter synthesis preserves full entries -- it's summarization, not deletion.
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.
- 5d ago First seen · 73 lines · 16 tokens per session scan A f144231e8d1f
alive-prune is a skill published in the GitHub repository alivecontext/alive (127 stars, last pushed 9d ago), licensed MIT. It adds 16 tokens to every session and 415 once invoked, about $0.0001 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
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.
cco
Context Control Center — one screen for budget, $ spent, tokens saved, waste, last prompt grade, the active task, and ready-to-run optimization actions.
cco-doctor
Health check for the context-optimizer plugin install — verifies versions, hooks, data dir, model config, and reports any issues.
cco-pack
Build an optimal context pack for the user's task — ranked file list with offset/limit suggestions, based on git state, mentioned paths, and historical patterns.
cco-templates
Manage context templates for common task types.
cco-replay
Show recent session summaries for quick context recovery.