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 aeonfun/aeon --skill memory-flushgit clone --depth 1 https://github.com/aeonfun/aeonWrote 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/aeonfun/aeon/memory-flush)<a href="https://agentmods.dev/skills/aeonfun/aeon/memory-flush"><img src="https://agentmods.dev/badge/skills/aeonfun/aeon/memory-flush/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/aeonfun/aeon/memory-flush"><img src="https://agentmods.dev/badge/skills/aeonfun/aeon/memory-flush.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 | $0.00017 | $0.02339 |
| Opus 5.5 | $0.00007 | $0.00936 |
| Sonnet 5.5 | $0.00003 | $0.00468 |
| Haiku 4.5 | $0.00002 | $0.00234 |
Grade A, and why
memory-flush scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`gh pr list` uses the `gh` CLI's built-in auth - no curl env-var expansion. `python3 scripts/memory_prep.py` and all other work is local file I/O against `memory/` (plus `git rm` for log rotation). Copies of this mod
1 near-identical copy found in the catalogue:
- memory-flush — 100% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
${var} - Topic to focus on. If empty, flushes all recent activity.
If ${var} is set, only promote entries related to that topic. Pruning (step 3), the index upkeep (step 6), and the deterministic watermark + rotation (steps 0 and 8) still run globally - a focused flush must never leave the rest of the store stale.
Read memory/MEMORY.md for current memory state. The scan window and log rotation are computed for you in step 0 - you no longer parse the watermark or rotate logs by hand.
Steps
0. Prepare (deterministic bookkeeping - run this first)
Run python3 scripts/memory_prep.py window and read its stdout. It:
- computes your scan window from the structured watermark
memory/memory-flush-state.json(fallback for a first-run migration: the MEMORY.md*Last consolidated:*line; then the last 3 days; a gap over 14 days is clamped to 14 and flagged), and prints the exact in-window log files to read; - has already rotated whole old months out of
memory/logs/intomemory/logs/archive/YYYY-MM.md(content-preserving) once the directory passed ~45 files. - prints
MEMORY.md size(bytes, lines, over-long lines) against the budget step 3e holds.
Read exactly the files it lists. Do not recompute the window or rotate logs yourself - that work is now deterministic and unit-tested (scripts/memory_prep.py), so it never silently falls back to 3 days or gets skipped. This closed two old holes: entries older than 3 days were lost whenever the agent skipped runs, and a daily schedule re-scanned the same 3 days every time.
1. Scan the in-window logs for entries worth promoting to long-term memory
- New lessons learned (errors encountered, workarounds found)
- Topics covered (articles, digests) - add to the recent output/articles/digests tables
- Features built or tools created
- Important findings from monitors (on-chain, GitHub, papers)
- Ideas captured that are still relevant
- Goals completed or progress milestones
2. Check each candidate against existing MEMORY.md content - dedup precisely
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.
- 4d ago Changed · +2 lines 173e60d8c746
- 7d ago Changed · +3 lines 25334251f085
- 19d ago Changed ec0d057c56aa
- 1mo ago First seen · 92 lines · 17 tokens per session scan A e996e8110cfc
memory-flush is a skill published in the GitHub repository aeonfun/aeon (778 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 2,339 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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