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Agent skill

Echo

by cwinvestments in cwinvestments/memstack

A skill your agent uses when the user references past sessions, asks 'what did we do', 'do you remember', 'last session', 'recall', or 'continue from'.

MITAuto-check passedAI & LLM Engineering

Install Echo

skills CLI
$ npx skills add cwinvestments/memstack --skill echo -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cwinvestments/memstack echo --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cwinvestments/memstack.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/echo .claude/skills/echo && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
echo
GitHub stars
423
Token cost
~1.7k tokens
SKILL.md length
734 words
Files
3
Skills in repo
87
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the user references past sessions, asks 'what did we do', 'do you remember', 'last session', 'recall', or 'continue from'.

  • Works in 6 steps: Semantic Vector Search (primary) → SQLite Keyword Search (augment or… → Recent Sessions and Insights → …
  • The user references past sessions
  • SKILL.md covers Activation, Context Guard, Anti-Rationalization and Protocol, plus 5 more sections
  • Runs Python scripts from its folder; calls python; needs OPENAI_API_KEY

What it does

Echo is an agent skill from cwinvestments/memstack. Use when the user references past sessions, asks 'what did we do', 'do you remember', 'last session', 'recall', or 'continue from'.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `index-sessions.py` and `search.py`).

It sits in AI & LLM Engineering, covering Vector databases. It works with SQLite. The repository describes itself as: Structured skill framework for Claude Code. 130 skills, persistent memory, TokenStack compression, localhost dashboard with 3-agent runner, real-time streaming, MCP tools. The licence is MIT.

When your agent uses it

  • The user references past sessions
  • Asks what did we do
  • Do you remember

Example prompts

  • “what did we do”
  • “do you remember”
  • “last session”
  • “/echo”

Requirements

  • Python 3
  • A credential in OPENAI_API_KEY

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Semantic Vector Search (primary)
  2. SQLite Keyword Search (augment or fallback)
  3. Recent Sessions and Insights
  4. Markdown Fallback
  5. Present Findings
  6. No Results

What it can do on your machine

Read from SKILL.md and the folder at commit 00370ce. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Echo loads about 1.7k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 734 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cwinvestments/memstack at commit 00370ce, republished under its MIT licence (© cwinvestments). 734 words, ~1,745 tokens.

Download SKILL.mdSave it as .claude/skills/echo/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
echo
description
Use when the user references past sessions, asks 'what did we do', 'do you remember', 'last session', 'recall', or 'continue from'.
version
1.0.0

🔊 Echo: Searching the Archives...

Recall information from past CC sessions using semantic vector search.

Activation

When this skill activates, output:

🔊 Echo: Searching the archives...

Then execute the protocol below.

Context Guard

ContextStatusPriority
User says "recall", "remember", "last session", "what did we"ACTIVE: search memoryP1
User asks about past work explicitly ("did we build X?")ACTIVE: search memoryP1
User says "continue from" or "resume" a past topicACTIVE: search memoryP2
User is describing NEW work to do ("build X", "add Y")DORMANT, this is new work, not recallnone
User mentions "memory" in code context (RAM, variables)DORMANT, technical term, not MemStack recallnone
User mentions a project name in present tense ("work on X")DORMANT, forward-looking, not recallnone
User says "save" or "log" (Diary/Project territory)DORMANT, Diary or Project skill handles writingnone

Anti-Rationalization

If you're thinking any of these, STOP, you're about to skip the protocol:

You're thinking...Reality
"I remember this from earlier in the conversation"You don't persist. Earlier context may be compacted. Run the search.
"I can just summarize from what I know"You know nothing from prior sessions. The database does. Search it.
"The user probably doesn't need exact details"Users ask Echo for specifics: dates, decisions, file paths. Run all steps.
"Vector search seems slow, I'll skip to SQLite"Vector search returns the best results. Always try it first.
"I found one result, that's probably enough"Run ALL steps (vector + SQLite + insights). One source misses context another catches.
"The keywords are too vague to search"Search anyway. Vague queries still return useful semantic matches.

Protocol

Step 1: Semantic Vector Search (primary)

Try LanceDB vector search first for best-quality results:

bash
python "$MEMSTACK_PATH/skills/echo/search.py" "<keywords>" --top-k 5

If this returns results, present them with scores, dates, and source files.

Step 2: SQLite Keyword Search (augment or fallback)

Always run SQLite search to supplement vector results or as fallback if Step 1 fails:

bash
python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" search "<keywords>" --project <project>
Step 3: Recent Sessions and Insights

For additional context:

bash
python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" get-sessions <project> --limit 5
python "${CLAUDE_PLUGIN_ROOT}/db/memstack-db.py" get-insights <project>
Step 4: Markdown Fallback

If both vector and SQLite return nothing, check memory/sessions/ and memory/projects/ for markdown files.

Step 5: Present Findings

Combine and deduplicate results from all sources:

  • Vector results: Show with similarity scores and section headings
  • SQLite results: Show with dates and accomplishment summaries
  • Source attribution: Always show which source (vector/SQLite/markdown) each result came from
  • Date and project name
  • What was accomplished
  • What was left pending
  • Key decisions and insights
Step 6: No Results

If nothing found across all sources, say clearly: "No session logs found for [topic]. Use Diary to save future sessions."

Indexing

To re-index sessions after new diary entries (normally done automatically):

bash
python "$MEMSTACK_PATH/skills/echo/index-sessions.py"

Use --force to re-embed all content (e.g., after changing embedding model):

bash
python "$MEMSTACK_PATH/skills/echo/index-sessions.py" --force
Show full SKILL.md (291 more words)Show less
Embedding provider

Echo uses LOCAL embeddings by default: sentence-transformers (all-MiniLM-L6-v2, 384-dim). No API key is needed and nothing leaves the machine.

OpenAI embeddings (text-embedding-3-small, 1536-dim) are strictly OPT-IN. Enable them either way:

  • set MEMSTACK_EMBED_PROVIDER=openai in the environment (also requires an OPENAI_API_KEY to be set), or
  • pass --provider openai to the indexer.

A bare OPENAI_API_KEY in the environment does NOT switch Echo to OpenAI on its own; the opt-in above is required. An explicit OpenAI opt-in with no key present is a hard error, not a silent downgrade to local, so an index is never built with a provider you did not choose.

Switching providers requires a --force re-index, because the vector dimensions differ (local 384 vs OpenAI 1536) and the two cannot be mixed in one index. Search automatically matches whatever provider the current index was built with (recorded in metadata.json).

Inputs

  • Keywords from the user's prompt (project name, feature name, date range)
  • Vector DB: $MEMSTACK_PATH/memory\vectors\lancedb\ (via LanceDB)
  • Database: ~/.memstack/memstack.db (via memstack-db.py)
  • Fallback: $MEMSTACK_PATH/memory\ (legacy markdown files)

Outputs

  • Ranked results with semantic similarity scores
  • Source type attribution (vector, database, or markdown fallback)
  • Summary of relevant past session context

Example Usage

User: "Do you remember what we did on AdminStack last session?"

🔊 Echo: Searching the archives...

Vector search (top 3):
  [1] AdminStack: 2026-02-18 (session)
      Section: Accomplished
      Score: 0.912
      Built CC Monitor page with session cards, auto-refresh, notifications.
      Created /api/cc-sessions CRUD + public report endpoint.

  [2] AdminStack, 2026-02-17 (session)
      Section: Decisions
      Score: 0.847
      Used SWR for auto-refresh instead of polling. API key via HMAC-SHA256.

  [3] AdminStack: 2026-02-18 (session)
      Section: Next Steps
      Score: 0.791
      Deploy dashboard, add notification preferences, test mobile view.

SQLite insights (3):
  - [decision] Used SWR for auto-refresh instead of polling
  - [decision] API key validation via HMAC-SHA256
  - [pattern] Next.js App Router + SWR for all dashboard pages

Level History

  • Lv.1: Base: Session log search and recall. (Origin: MemStack v1.0, Feb 2026)
  • Lv.2: Enhanced: Added YAML frontmatter, context guard, activation message. (Origin: MemStack v2.0 MemoryCore merge, Feb 2026)
  • Lv.3: Advanced: SQLite backend as primary source, markdown as fallback, insight search. (Origin: MemStack v2.1 Accomplish-inspired upgrade, Feb 2026)
  • Lv.4: Native: CC rules integration (.claude/rules/echo.md), /memstack-search slash command, auto-indexed CLAUDE.md context. (Origin: MemStack v3.0-beta, Feb 2026)
  • Lv.5: Semantic: LanceDB vector-powered recall with sentence-transformers embeddings (OpenAI opt-in). Auto-indexes sessions/plans, semantic similarity across all logs, SQLite fallback. (Origin: MemStack v3.1, Feb 2026)

© cwinvestments, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/echo of cwinvestments/memstack.

  • SKILL.md
  • index-sessions.py
  • search.py

Open the folder on GitHubat commit 00370ce

Compare with similar skills

Echo next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Echo compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Echo this skillcwinvestments/memstack423—~1.7kAutomated safety check: PassMIT
Hermes Memory Providersmnemosyne-oss/mnemosyne3.4k—~1.8kAutomated safety check: PassMIT
Open Second Brain Embeddings Setupitechmeat/open-second-brain430—~2.6kAutomated safety check: WarnMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT
Codebase Managementgiancarloerra/SocratiCode3.3k1 repos~1.8kAutomated safety check: PassAGPL-3.0
Pgvector Semantic Searchtimescale/pg-aiguide1.9k1 repos~3.8kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Echo

What does Echo do?

A skill your agent uses when the user references past sessions, asks 'what did we do', 'do you remember', 'last session', 'recall', or 'continue from'. Echo is an agent skill from cwinvestments/memstack. Use when the user references past sessions, asks 'what did we do', 'do you remember', 'last session', 'recall', or 'continue from'.

When should I use Echo?

Echo fits situations like: the user references past sessions; asks what did we do; do you remember.

How do I install Echo in Claude Code?

Run `npx skills add cwinvestments/memstack --skill echo -a claude-code`. Or copy the skill folder (skills/echo in cwinvestments/memstack) into .claude/skills/echo in your project. Claude Code loads it when a task matches its description.

How do I install Echo in Codex?

Run `npx skills add cwinvestments/memstack --skill echo -a codex`. Or copy the skill folder (skills/echo in cwinvestments/memstack) into .agents/skills/echo in your project. Codex loads it when a task matches its description.

Can I use Echo in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cwinvestments/memstack --skill echo -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/echo, .gemini/skills/echo, .github/skills/echo and .opencode/skills/echo in your project.

What does Echo need to run?

Going by SKILL.md and its folder, Echo needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named OPENAI_API_KEY. Our summary lists: Python 3; A credential in OPENAI_API_KEY.

Does Echo access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Echo safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Echo use?

Echo is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Echo use?

About 1.7k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Echo?

Skills that share tags, products or a category with Echo: Hermes Memory Providers (mnemosyne-oss/mnemosyne, 3.4k stars), Open Second Brain Embeddings Setup (itechmeat/open-second-brain, 430 stars), Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Codebase Management (giancarloerra/SocratiCode, 3.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Echo?

cwinvestments (a GitHub user) maintains it in cwinvestments/memstack, which has 423 GitHub stars. The repository holds 87 skills in this directory. The repository was last updated on September 26, 2026.

Source: cwinvestments/memstack on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.