memory-proposal-collector

memory-proposal-collector is an agent for coding agents from Kanevry/session-orchestrator. It costs 232 tokens per session (3,499 once invoked), scanned A, original, MIT.

Reference documentation for collecting proposed long-term memories at the end of a coding session. It describes how approved proposals are shown to the user and saved for later sessions.

In plain words
What is it for?
Use it when implementing or understanding session-end memory collection, proposal approval, and storage in JSONL files.
Why use it?
It explains a coordinator workflow that turns repeatable session lessons into approved memory entries, while making clear that this document is not an agent to dispatch.

Agent

Part of the session-orchestrator plugin — 49 skills, 28 commands, 19 agents, 11 hooks, 1 MCP server shipped together

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.

agentmods
npx agentmods add agents/kanevry/session-orchestrator/memory-proposal-collector
Clone the repo
git clone --depth 1 https://github.com/Kanevry/session-orchestrator

Or install session-orchestrator, the plugin that ships this one along with the rest of its 49 skills, 28 commands, 19 agents, 11 hooks, 1 MCP server.

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-proposal-collector

README.md
[![agentmods](https://agentmods.dev/badge/agents/kanevry/session-orchestrator/memory-proposal-collector.svg)](https://agentmods.dev/agents/kanevry/session-orchestrator/memory-proposal-collector)
Your own site
<a href="https://agentmods.dev/agents/kanevry/session-orchestrator/memory-proposal-collector"><img src="https://agentmods.dev/badge/agents/kanevry/session-orchestrator/memory-proposal-collector.svg" alt="Measured on agentmods" height="20"></a>
Per session 232 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,499 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00232 $0.03499
Opus 5 $0.00116 $0.01750
Sonnet 5 $0.00046 $0.00700
Haiku 4.5 $0.00023 $0.00350

Measured yesterday against content hash 52e86258bb08, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

memory-proposal-collector 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 yesterday.

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.

agents/memory-proposal-collector.md · 300 lines

How it starts

The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Memory Proposal Collector (Reference Documentation)

NOT a dispatchable subagent. This file documents the coordinator-direct AUQ rendering flow that runs at session-end Phase 3.6.3. Do not attempt to dispatch memory-proposal-collector as an agent — the coordinator will receive "agent type not found" and that is by design. See Why this is documentation, not an agent for rationale.


Overview

At session-end Phase 3.6.3 the coordinator presents pending memory proposals to the user for approval or rejection. Proposals are short learning candidates queued by the memory-propose.mjs CLI during the session (typically from hook invocations, auto-generated by subagents that detected a repeatable pattern worth recording).

The flow runs coordinator-direct: the coordinator calls library functions, renders an AskUserQuestion picker, and writes results — no subagent dispatch involved.


Gate Conditions

This flow runs only when ALL of the following are true:

  1. persistence: true is set in Session Config (CLAUDE.md ## Session Config block).
  2. memory.proposals.enabled: true is set in Session Config (default when the pre-bash-memory-propose-audit hook is active).
  3. .orchestrator/metrics/proposals.jsonl exists and is non-empty (≥1 line).

If any condition fails, the coordinator emits a single info-line ([3.6.3] Memory proposals: skip — <reason>) and continues to Phase 3.6.5.


Coordinator Step-by-Step

Step 1 — Load proposal queue

import { collectProposals } from '../scripts/lib/memory-proposals/collector.mjs';
const { queue, stats, perWaveSummaries } = await collectProposals({ repoRoot });

Note: sessionId is supplied by the coordinator at the call site (e.g., read from STATE.md frontmatter); it is NOT returned by collectProposals.

collectProposals() reads .orchestrator/metrics/proposals.jsonl, parses each line as a ProposalRecord, and returns them in FIFO order (insertion order, not sorted by confidence). This matches the D3 decision locked in Wave 1.

Read the full file on GitHub · 300 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. yesterday First seen · 300 lines · 232 tokens per session scan A 52e86258bb08

Subscribe to this mod's changes

memory-proposal-collector is an agent published in the GitHub repository Kanevry/session-orchestrator (49 stars, last pushed today), licensed MIT. It adds 232 tokens to every session and 3,499 once invoked, about $0.0012 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 2026-09-03.

Related

Other agents, from other repositories

adapter_grok

Grok is an eagerly registered stock-TUI adapter. RimZ launches grok, installs passive global hooks in ${GROKHOME:-/.grok}/hooks/rimz.json, and enriches each session from its durable updates.jsonl, summary.json, signals.json, and optional events.jsonl files. ACP and provider-private billing APIs stay outside this…

rimio-ai/rimz · 0 tokens

agent-session

Trinity has two conversation surfaces, and the difference is memory.

Abilityai/trinity · 0 tokens

providers

A coding agent runs against a provider account: a login, on a plan, that may or may not be metered. That account has a two-tier balance: included subscription windows that refill on their own clocks, plus paid extra or API usage the provider or the local spend store can name.

rimio-ai/rimz · 0 tokens

model

A coding agent runs in a pane, reports through its own hooks, and appears in the sidebar as one card. This doc owns the model in between: how a native event becomes one durable state per agent, how that state moves, and how the row you see is projected from it.

rimio-ai/rimz · 0 tokens

adapter

Thirteen coding agents report to RimZ, and no code outside crates/rimz/src/agents/ knows which one it is looking at. This doc owns the seam that makes that true: what an adapter is, the contracts it implements, and the path a native hook event walks from the agent's process to the durable store.

rimio-ai/rimz · 0 tokens

context-engineer

Analyzes and optimizes context window usage across sessions. Use when context feels bloated, sessions run slow, or approaching compaction limits.

rohitg00/pro-workflow · 32 tokens