query

A command for finding saved memories by structured fields in their YAML front matter, such as type, namespace, tags, dates, title, or confidence.

In plain words
What is it for?
Use it to find memories about a project, filter decisions by confidence or date, match title patterns, or limit results to a user or project scope.
Why use it?
It avoids manually opening memory files when you need a precise subset. Results can be returned as file paths, titles, JSON, or a count.

Command

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 commands/modeled-information-format/mnemonic/query
Clone the repo
git clone --depth 1 https://github.com/modeled-information-format/mnemonic
Per session 9 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,061 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 $0.00009 $0.01061
Opus 5 $0.00005 $0.00531
Sonnet 5 $0.00002 $0.00212
Haiku 4.5 $0.00001 $0.00106

Measured yesterday against content hash 21c311a606e3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

query 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.

commands/query.md · 158 lines

How it starts

The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Memory

Search first: /mnemonic:search {relevant_keywords} Capture after: /mnemonic:capture {namespace} "{title}"

Run /mnemonic:list --namespaces to see available namespaces from loaded ontologies.

/mnemonic:query

Structured queries on memory frontmatter fields using yq for YAML parsing.

Arguments

  • --type - Filter by memory type (semantic, episodic, procedural)
  • --namespace - Filter by namespace pattern (supports wildcards like decisions/*)
  • --tag - Filter by tag (can be specified multiple times)
  • --confidence - Filter by confidence with operators (>, >=, <, <=, ..)
  • --created - Filter by created date with operators
  • --modified - Filter by modified date with operators
  • --title - Filter by title pattern (regex)
  • --scope - Limit to user, project, or all (default: all)
  • --format - Output format: paths, titles, json, count (default: paths)
  • --limit - Limit output to N results

Operators

Operator Example Description
= (implicit) --confidence 0.9 Exact match
!= --type "!=episodic" Not equal
> --confidence ">0.8" Greater than
>= --confidence ">=0.9" Greater or equal
< --confidence "<0.5" Less than
<= --confidence "<=0.7" Less or equal
.. --confidence "0.7..0.9" Range (inclusive)

Procedure

Step 1: Check yq Installation

if ! command -v yq &>/dev/null; then
    echo "Error: yq is required. Install: brew install yq"
    exit 1
fi

Step 2: Execute Query

# Run mnemonic-query with provided arguments
./tools/mnemonic-query "$@"

Example Usage

# Find all semantic memories
/mnemonic:query --type semantic

# Find high-confidence memories
/mnemonic:query --confidence ">0.8"

# Find memories in confidence range
/mnemonic:query --confidence "0.5..0.9"

# Find by tag and type
/mnemonic:query --tag architecture --type semantic

# Find decisions with security tag
/mnemonic:query --namespace "_semantic/decisions" --tag security

# Exclude episodic memories
/mnemonic:query --type "!=episodic"

# Find memories created in date range
/mnemonic:query --created "2026-01-01..2026-01-31"

# Get count only
/mnemonic:query --type semantic --format count

# Get titles
/mnemonic:query --tag architecture --format titles

# Limit results
/mnemonic:query --type semantic --limit 10

# Pipe to content search
/mnemonic:query --tag security | xargs rg "password"

Read the full file on GitHub · 158 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 · 158 lines · 9 tokens per session scan A 21c311a606e3

Subscribe to this mod's changes

query is a command published in the GitHub repository modeled-information-format/mnemonic (22 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 1,061 once invoked, about $0.0000 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 2026-08-30.