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.
git clone --depth 1 https://github.com/Fmarzochi/EGCWrote 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/commands/fmarzochi/egc/prune)<a href="https://agentmods.dev/commands/fmarzochi/egc/prune"><img src="https://agentmods.dev/badge/commands/fmarzochi/egc/prune/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/commands/fmarzochi/egc/prune"><img src="https://agentmods.dev/badge/commands/fmarzochi/egc/prune.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00015 | $0.00176 |
| Opus 5 | $0.00008 | $0.00088 |
| Sonnet 5 | $0.00003 | $0.00035 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
prune scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to prune — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Prune Pending Instincts
Remove expired pending instincts that were auto-generated but never reviewed or promoted.
Implementation
Run the instinct CLI using the plugin root path:
python3 "${GEMINI_PLUGIN_ROOT}/skills/continuous-learning-v2/scripts/instinct-cli.py" prune
Or if GEMINI_PLUGIN_ROOT is not set (manual installation):
python3 ~/.gemini/skills/continuous-learning-v2/scripts/instinct-cli.py prune
Usage
/prune # Delete instincts older than 30 days
/prune --max-age 60 # Custom age threshold (days)
/prune --dry-run # Preview without deleting
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 · 32 lines · 15 tokens per session scan A 17581f9da932
prune is a command published in the GitHub repository Fmarzochi/EGC (49 stars, last pushed yesterday), licensed Apache-2.0. It adds 15 tokens to every session and 176 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to prune, differing in 6 lines, and is treated as a copy.
Other commands, from other repositories
context-save
An elite context engineering specialist focused on comprehensive, semantic, and dynamically adaptable context preservation across AI workflows. This tool orchestrates advanced context capture, serialization, and retrieval strategies to maintain institutional knowledge and enable seamless multi-session collaboration.
memory-why
Show why a memory recall returned what it did -- BM25 vs vector vs hybrid provenance.
mac 应用规则沉淀
A command that reviews how a problem was solved and records new rules in a specified Mac app document.
kaeru
kaeru re-entry ritual — load process state + epistemic state for an initiative.
export-memory
Export the generated memory as portable Markdown files.
Context Search
Search prior work to add context to a request, or browse previous sessions on a topic. Use before/after a request to ground it in past work, or standalone to recall and familiarize before asking.