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 Lolaplex/agents-memory --skill memory-distillgit clone --depth 1 https://github.com/Lolaplex/agents-memoryWrote 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/lolaplex/agents-memory/memory-distill)<a href="https://agentmods.dev/skills/lolaplex/agents-memory/memory-distill"><img src="https://agentmods.dev/badge/skills/lolaplex/agents-memory/memory-distill/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/lolaplex/agents-memory/memory-distill"><img src="https://agentmods.dev/badge/skills/lolaplex/agents-memory/memory-distill.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 | $0.00076 | $0.00736 |
| Opus 5.5 | $0.00030 | $0.00294 |
| Sonnet 5.5 | $0.00015 | $0.00147 |
| Haiku 4.5 | $0.00008 | $0.00074 |
Grade A, and why
memory-distill 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 21d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
memory-distill
Staging inbox is temporary — from any ingest source (staging/ingest/<id>/captured.md) or project/user staging. Distill durable facts into typed paths; discard ephemeral noise.
When remote-connected, all distill tools run locally; writes auto-push the mirror bundle to cloud. Other devices pull to stay current.
Pipeline order (connected mode)
- Ingest (local):
ingest_catalog→ingest_extract— auto-pushes staging. - Distill (local):
get_staging_inbox→ classify →distill_batch— auto-pushes typed memory + repo mirrors. - Pull on MCP start / periodic pull on other devices updates local files.
When staging inbox depth reaches staging_nag_threshold (default 50):
- 50–74: soft notice — distill during memory maintenance, not on every unrelated tool call.
- ≥75 (
staging_force_threshold): strong notice — prioritizeauto_distill+distill_batchbefore other memory MCP work this session. - After ingest extract only: deterministic noise pass when inbox >=
auto_distill_noise_threshold(discards obvious noise; no silent promote). - MCP start noise pass: off by default (
auto_distill_on_start: false).
Inbox→0 for non-noise bullets remains agent judgment via distill_batch.
Standard 2-Step Agent Workflow
-
Step 1: Run Auto-Distill (Noise Pruning & Heuristics)
- Call MCP
auto_distill(limit=50, discard_noise=true)(or CLI:python -m agents_memory distill --auto). - Deterministically removes build/test logs, SQL/code dumps, bug reports, questions, and ephemeral banter.
- Automatically promotes clear preferences (
always,never,prefer,stack:,datenschutz). - Returns a structured list of remaining
candidateswith suggested targets (suggested_kind,suggested_name).
- Call MCP
-
Step 2: Confirm Remaining Candidates via Distill Batch
- If
candidatesremain (typically 1–5 bullets): - Review the candidate list returned by
auto_distill. - Call MCP
distill_batch(items)in a single call to promote or discard the remaining bullets:[ { "bullet": "Die Controls sollen alle einzeln switchable sein", "kind": "note", "name": "ux", "project": "veadio", "source_path": "user/staging/ingest/antigravity/captured.md" }, { "bullet": "Can you check line 40 of main.py", "discard": true, "source_path": "user/staging/ingest/cursor/captured.md" } ] - Inbox drops to 0 cleanly in one turn.
- If
-
distill_batchautomatically syncs to all IDEs/CLIs upon completion.
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.
- 21d ago Changed · -3 lines e89ec1f370cd
- 1mo ago Changed · +14 lines 0047827cc2b2
- 1mo ago First seen · 45 lines · 76 tokens per session scan A 04c9cd453c80
memory-distill is a skill published in the GitHub repository Lolaplex/agents-memory (5 stars, last pushed 7d ago), licensed MIT. It adds 76 tokens to every session and 736 once invoked, about $0.0003 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 2026-08-31.
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