echo-memory

A persistent memory skill backed by a local database that stores project facts and decisions between sessions.

In plain words
What is it for?
It is for recalling earlier project context and recording decisions, corrections, preferences, and standing rules. It can also process pending memory files.
Why use it?
It prevents the agent from repeatedly asking for information that was already provided and keeps important corrections, preferences, and decisions available later.

Skill for Claude CodeCodex

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 skills/ayushcodes10/echo-mem/skill
Any agent
npx skills add ayushcodes10/echo-mem --skill skill
Clone the repo
git clone --depth 1 https://github.com/ayushcodes10/echo-mem

Made for: Claude Code, Codex.

Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,269 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 $0.00075 $0.01269
Opus 5 $0.00037 $0.00634
Sonnet 5 $0.00015 $0.00254
Haiku 4.5 $0.00007 $0.00127

Measured yesterday against content hash 91196c5991cf, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

echo-memory 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.

src/echo_memory/skill/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Echo Memory

Memory that survives the session. Facts are edges between entities in a graph, scoped to this user and attributed to this project, readable by any other tool pointed at the same store.

Recall before you ask

Call query_memory at session start, and before asking the user anything they plausibly already told a past session or another tool. Checking costs one call; making them re-explain costs their patience and is the entire problem this exists to solve.

query_memory(scope="shared", query="why is the deploy branch master")
query_memory(scope="shared", digest=True)   # "catch me up", ignores query

If the response carries a pending_ingest field, memory files were written that the graph hasn't heard about. Read each listed file, call write_episode with what it states, then close it:

echo-memory pending --done <path>

Write the moment it happens

Call write_episode in the same turn, not batched at the end, whenever:

  • the user states a decision — "we're using X", "X only deploys from branch Y"
  • the user corrects you — "actually, X not Y"
  • the user states a preference or a standing rule
  • the user says "remember this" / "for future reference" / "don't do that again"
  • you discover something non-obvious that cost real time to learn

Skip genuinely throwaway exchanges: typo fixes, one-off questions with no lasting relevance. A missed memory costs more than one extra call.

The exact shape

The server never calls an LLM. You extract the entities and facts; it stores, resolves and retrieves them.

write_episode(
  scope="shared",
  session_id="<this session's id>",
  entities=[
    {"name": "Postgres", "type": "tool"},
    {"name": "storage decision", "type": "decision"},
  ],
  facts=[
    {"source": "storage decision", "target": "Postgres",
     "relation_type": "uses",
     "fact": "Switched from SQLite to Postgres for durability, 2026-08-20.",
     "confidence": "extracted"},
  ],
)
  • entities[].name — non-empty, unique within the call. type is free text you choose ("tool", "person", "decision", "bug", "policy"), not a fixed enum.
  • facts[].source / .target — must each match an entities[].name exactly.
  • facts[].relation_type — free text ("uses", "caused_by", "blocked_by").
  • facts[].fact — the sentence to remember. Write it so it still makes sense read cold in six months by a different tool: name the thing, don't say "it".
  • facts[].confidenceexactly one of "extracted" (the user said it), "inferred" (you deduced it), "ambiguous" (uncertain). Not a number, not "high"/"low", never omitted. Anything else is rejected.

Read the full file on GitHub · 135 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 · 135 lines · 75 tokens per session scan A 91196c5991cf

Subscribe to this mod's changes

echo-memory is a skill published in the GitHub repository ayushcodes10/echo-mem (1 stars, last pushed yesterday), licensed Apache-2.0. It adds 75 tokens to every session and 1,269 once invoked, about $0.0004 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 2026-08-31.

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