Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ovidiu-eremia/mind-memnpx agentmods add skills/ovidiu-eremia/mind-mem/memory-recallWrote 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.
[](https://agentmods.dev/skills/ovidiu-eremia/mind-mem/memory-recall)<a href="https://agentmods.dev/skills/ovidiu-eremia/mind-mem/memory-recall"><img src="https://agentmods.dev/badge/skills/ovidiu-eremia/mind-mem/memory-recall/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ovidiu-eremia/mind-mem/memory-recall"><img src="https://agentmods.dev/badge/skills/ovidiu-eremia/mind-mem/memory-recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00567 |
| Opus 5.5 | $0.00000 | $0.00227 |
| Sonnet 5.5 | $0.00000 | $0.00113 |
| Haiku 4.5 | $0.00000 | $0.00057 |
Grade A, and why
memory-recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to memory-recall — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/recall — Memory Search
Search across all structured memory files. Default backend: BM25 scoring with Porter stemming and domain-aware query expansion. Optional: graph-based cross-reference boosting (--graph). Optional: vector/embedding backend (configure in mind-mem.json). Returns ranked results with block ID, type, score, excerpt, and file path.
When to Use
- Before making decisions (check if a similar decision already exists)
- When asked about past events, decisions, or tasks
- To find related context for a current problem
- To check what's known about a person, project, or tool
- To explore connections between decisions and tasks (
--graph)
How to Run
Basic Search
python3 maintenance/recall.py --query "authentication" --workspace "${MIND_MEM_WORKSPACE:-.}"
Graph-Boosted Search (cross-reference neighbor discovery)
python3 maintenance/recall.py --query "database" --graph --workspace "${MIND_MEM_WORKSPACE:-.}"
JSON Output (for programmatic use)
python3 maintenance/recall.py --query "auth" --workspace "${MIND_MEM_WORKSPACE:-.}" --json --limit 5
Active Items Only
python3 maintenance/recall.py --query "deadline" --workspace "${MIND_MEM_WORKSPACE:-.}" --active-only
What It Searches
decisions/DECISIONS.md— All decisionstasks/TASKS.md— All tasksentities/projects.md— Projectsentities/people.md— Peopleentities/tools.md— Toolsentities/incidents.md— Incidentsintelligence/CONTRADICTIONS.md— Known contradictionsintelligence/DRIFT.md— Drift detectionsintelligence/SIGNALS.md— Captured signals
Scoring
Results are ranked by BM25 relevance (k1=1.2, b=0.75) with:
- Stemming — "queries" matches "query", "deployed" matches "deployment"
- Query expansion — "auth" expands to include "authentication", "login", "oauth", "jwt"
- Recency — Recent items score higher
- Active status — Active items get 1.2x boost
- Priority — P0/P1 items get 1.1x boost
- Graph neighbors — With
--graph, blocks connected via cross-references to keyword matches get a 0.3x boost (tagged[graph]in output)
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.
- 1mo ago First seen · 53 lines · 0 tokens per session scan A 505c7f683c93
memory-recall is a skill published in the GitHub repository ovidiu-eremia/mind-mem (0 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 567 tokens. A static security scan graded it A with 0 findings. It is 100% identical to memory-recall, differing in 0 lines, and is treated as a copy.
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