memory-distill

memory-distill is a skill for Claude Code, Codex from Lolaplex/agents-memory. It costs 76 tokens per session (736 once invoked), scanned A, original, MIT.

A workflow for turning temporary notes in a staging inbox into lasting memory files. It keeps durable decisions and working rules while discarding one-off debugging notes and other temporary noise.

In plain words
What is it for?
Use it after an information-ingest run or when the staging inbox needs processing. It helps classify, name, save, or discard captured notes.
Why use it?
It prevents useful project or user knowledge from being buried in an un整理ed inbox. It also reduces clutter by separating facts that should persist from information that has already served its purpose.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it after an information-ingest run or when the staging inbox needs processing. It helps classify, name, save, or discard captured notes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lolaplex/agents-memory/memory-distill
Install

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.

Any agent
npx skills add Lolaplex/agents-memory --skill memory-distill
Clone the repo
git clone --depth 1 https://github.com/Lolaplex/agents-memory

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for memory-distill

README.md
[![agentmods](https://agentmods.dev/badge/skills/lolaplex/agents-memory/memory-distill/github.svg)](https://agentmods.dev/skills/lolaplex/agents-memory/memory-distill)
Your own site
<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.

agentmods 80×15 button for memory-distill

Your own site · 80×15
<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>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 736 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 21d ago against content hash e89ec1f370cd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

skills/memory-distill/SKILL.md · 56 lines

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)

  1. Ingest (local): ingest_catalog → ingest_extract — auto-pushes staging.
  2. Distill (local): get_staging_inbox → classify → distill_batch — auto-pushes typed memory + repo mirrors.
  3. 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 — prioritize auto_distill + distill_batch before 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

  1. 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 candidates with suggested targets (suggested_kind, suggested_name).
  2. Step 2: Confirm Remaining Candidates via Distill Batch

    • If candidates remain (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.
  3. distill_batch automatically syncs to all IDEs/CLIs upon completion.

Read the full file on GitHub · 56 lines

Changes

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.

  1. 21d ago Changed · -3 lines e89ec1f370cd
  2. 1mo ago Changed · +14 lines 0047827cc2b2
  3. 1mo ago First seen · 45 lines · 76 tokens per session scan A 04c9cd453c80

Subscribe to this mod's changes

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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