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 agentmods add skills/ayushcodes10/echo-mem/skillnpx skills add ayushcodes10/echo-mem --skill skillgit clone --depth 1 https://github.com/ayushcodes10/echo-memWhat 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 | $0.00075 | $0.01269 |
| Opus 5 | $0.00037 | $0.00634 |
| Sonnet 5 | $0.00015 | $0.00254 |
| Haiku 4.5 | $0.00007 | $0.00127 |
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.
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.typeis free text you choose ("tool", "person", "decision", "bug", "policy"), not a fixed enum.facts[].source/.target— must each match anentities[].nameexactly.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[].confidence— exactly 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.
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.
- yesterday First seen · 135 lines · 75 tokens per session scan A 91196c5991cf
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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