mind-mem
AGENTS.md22 starsChanged 6 months agoApache-2.0View on GitHub
Before every response, run mm context "$QUERY" and prepend the output. Legend: 🎯session 🔴bugfix 🟣feature 🔄refactor ✅change 🔵discovery ⚖️decision Format: ID TIME TYPE TITLE Fetch details: get_observations([IDs]) | Search: mem-search skill Stats: 4 obs (1,646t read) | 85,780t work | 98% savings 6705 11:42p 🟣 mind-mem v3.8.11 MIC/MAP Discoverability Layer mind-mem v3.8.11 shipped the discoverability layer for MIC/MAP codec functionality. The core codec shipped pure-Python in v3.8.5; this release adds the user-facing surface: two MCP tools (mic_convert for format conversion…
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 31,774 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository7 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- mind-mem-developmentSkill
- apply-proposalSkill
- integrity-scanSkill
- memory-recallSkill
- mind-memSkill
The file
We split this AGENTS.md into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 3
Before every response, run mm context "$QUERY" and prepend the output.
≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
mind-mem: agent instructions (auto-written)
Before every response, run mm context "$QUERY" and prepend the output.
<claude-mem-context>
# Memory Context[mind-mem] recent context, 2026-05-09 2:53am PDT
Legend: 🎯session 🔴bugfix 🟣feature 🔄refactor ✅change 🔵discovery ⚖️decision Format: ID TIME TYPE TITLE Fetch details: get_observations([IDs]) | Search: mem-search skill
Stats: 4 obs (1,646t read) | 85,780t work | 98% savings
Apr 21, 2026
6661 9:25p 🟣 mind-mem v3.4.0 Pre-Release Security Hardening The mind-mem v3.4.0 release introduces four new recall modules for enhanced AI memory retrieval. A two-agent security audit identified and fixed six issues before final review: prompt injection via evidence wrapping, missing iteration limits on iterative_retrieve (now capped at 5 rounds, 3 followups, 20 total queries), unsafe follow-up query patterns (now regex-validated), out-of-bounds temporal dates, unsafe object deduplication using id() instead of content fingerprints, and a NameError when sub_queries was empty. The LoCoMo benchmark on conversation-0 jumped from 77.06 to 95.0 with the new features enabled, indicating significant retrieval quality improvements. ~378t 🛠️ 19,507
May 2, 2026
6702 11:17p 🔵 mind-mem v3.8.x Ship Train Complete — 11 Releases Audited The mind-mem v3.8.x release train spanning 10 versions completed the Model Safety Audit theme end-to-end: Ed25519 signing of model manifests, HuggingFace base_model allowlists, MCP tool wrappers for audit/sign/verify, a load-gate registry enforced inside the transformers extractor, CI hooks for auditing pinned models, and a full MIC/MAP Python toolchain for MIND IR serialization. The v3.8.8 release added Hypothesis fuzzing and 26 adversarial DoS tests which caught a real UnicodeDecodeError leak. v3.8.9 added a streaming MICB parser with bounded memory. v3.8.10 added an optional Cython accelerator for hot-path serialization primitives. Social Ingestion was architecturally descoped from mind-mem and moved to the agent layer, recognizing that fetching is an agent concern not a memory concern. Next priorities include v3.9.0 (hash-of-code invalidation, per-byte lineage) and v4.0.0 (sharded Postgres, Kubernetes operator, gRPC, event fan-out). ~519t 🔍 22,157
6705 11:42p 🟣 mind-mem v3.8.11 MIC/MAP Discoverability Layer mind-mem v3.8.11 shipped the discoverability layer for MIC/MAP codec functionality. The core codec shipped pure-Python in v3.8.5; this release adds the user-facing surface: two MCP tools (mic_convert for format conversion between mic2 text and micb binary with auto-detection and 8 MiB input guard, mic_inspect for structural analysis), CLI commands (mm mic convert/inspect), a runnable quickstart example demonstrating residual block building and round-trip with streaming parser, comprehensive user documentation, and README feature callout. The implementation honors critical constraints: zero new dependencies preserving mind-mem's zero-dep status, no exposure of private STARGA Rust backends, and separation from the MIC/MAP v15 patent filing track. ~439t 🛠️ 22,021
May 6, 2026
6709 6:32p ⚖️ mind-mem 4B Full Fine-Tune GO/NO-GO Checkpoint GO/NO-GO checkpoint for mind-mem 4B full fine-tune training run. Configuration shows strong alignment between training corpus and eval spec: 55 production queries define success criteria, corpus includes verbatim prompts plus 2700+ paraphrases for robustness, keyword coverage is saturated (23-670 mentions per eval keyword), and system prompts are matched. Learning rate 1.5e-5 with cosine over 3 epochs is conservative for 4B scale. Zero contamination claim valid if paraphrases are semantically distinct. This setup optimizes for the specific 55-query production spec rather than general capability. ~310t ⚖️ 22,095
Access 86k tokens of past work via get_observations([IDs]) or mem-search skill.
</claude-mem-context>
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.