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 skills add aksika/abtars --skill abmind_recall_workaroundgit clone --depth 1 https://github.com/aksika/abtarsWrote 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/aksika/abtars/abmind_recall_workaround)<a href="https://agentmods.dev/skills/aksika/abtars/abmind_recall_workaround"><img src="https://agentmods.dev/badge/skills/aksika/abtars/abmind_recall_workaround/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/aksika/abtars/abmind_recall_workaround"><img src="https://agentmods.dev/badge/skills/aksika/abtars/abmind_recall_workaround.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 14 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00028 | $0.00709 |
| Opus 5 | $0.00014 | $0.00354 |
| Sonnet 5 | $0.00006 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
abmind_recall_workaround 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
abmind recall — direct DB workaround
When abmind recall errors out (daemon down, embedding provider missing, tool not registered), memories can still be read directly from the memory database. Read-only sqlite3 queries — macOS ships sqlite3.
DB path (Molty): ~/.abmind/memory/memory.db
Rules
- Always open read-only (no write transactions from a skill)
- Always filter
encrypted = 0— encrypted rows store ciphertext and are unreadable directly - SECRET memories (classification = 3) are always sealed/encrypted — they never appear in direct reads
- Timestamps are epoch milliseconds →
datetime(timestamp/1000,'unixepoch','localtime')
Recent memories
sqlite3 -header -column ~/.abmind/memory/memory.db "SELECT id, memory_type, topic, datetime(created_at/1000,'unixepoch','localtime') AS created, substr(content_en,1,200) AS content FROM extracted_memories WHERE encrypted = 0 ORDER BY created_at DESC LIMIT 20;"
Keyword search (memories, not raw messages)
sqlite3 -header -column ~/.abmind/memory/memory.db "SELECT id, memory_type, topic, datetime(created_at/1000,'unixepoch','localtime') AS created, substr(content_en,1,200) AS content FROM extracted_memories WHERE encrypted = 0 AND (content_en LIKE '%keyword%' OR content_original LIKE '%keyword%') ORDER BY created_at DESC LIMIT 20;"
- Search English
content_enand original-languagecontent_original - Use
%wildcards for partial words (LIKE, not FTS)
Memories from the last N hours
sqlite3 -header -column ~/.abmind/memory/memory.db "SELECT id, memory_type, topic, substr(content_en,1,200) AS content FROM extracted_memories WHERE encrypted = 0 AND created_at > (strftime('%s','now') - 86400) * 1000 ORDER BY created_at DESC LIMIT 20;"
(86400 = 24h; use 3600 for 1h, 172800 for 48h)
Useful columns
| Column | Meaning |
|---|---|
content_en |
English-normalized content |
content_original |
As-spoken content |
memory_type |
fact / preference / decision / event |
classification |
0 group, 1 personal, 2 confidential (3 = SECRET, sealed) |
created_at / source_timestamp |
epoch ms |
topic, emotion_tags |
metadata |
recall_count |
how often recalled (popularity hint) |
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.
- 5d ago First seen · 61 lines · 28 tokens per session scan A c5655f1ffe21
abmind_recall_workaround is a skill published in the GitHub repository aksika/abtars (9 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 709 once invoked, about $0.0001 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 2026-09-05.
Other skills, from other repositories
memory-enhancement
Manage memory citations, verify code references, and track confidence scores. Use when adding citations to memories, checking memory health, or verifying code references are still valid. Use when you say "add a citation", "verify this memory's code refs", "check memory health". Do NOT use for searching or creating…
memory-reflexion
Tier 2 episode extraction, the reflexion write path split out of the memory router per ADR-063. Extracts an episode from a completed session log so later sessions can replay what was tried and what it cost. Use when you say extract episode from session, record what happened this session, or re-extract this episode. Do…
research-and-incorporate
Research external topics, create comprehensive analysis, and incorporate learnings into Serena and Forgetful memory systems. Use when you say "research and incorporate {topic}", "study {topic} and add to memory", "deep dive on {topic}", or "learn about {topic} for the project". Works on external concepts, frameworks…
using-forgetful-memory
Guidance for using Forgetful semantic memory effectively. Applies Zettelkasten atomic memory principles. Use when deciding whether to query or create memories, structuring memory content, or understanding memory importance scoring. Guidance only. Use when you say "how do I create a memory", "how do I link memories…
memory-search
Tier 1 semantic memory search across the Serena corpus with progressive disclosure and token-budget warnings. The focused search operation split out of the memory router per ADR-063. Use when you say search memory, what do we know about X, or recall prior context. Do NOT use to extract session episodes or add…
encode-repo-serena
Populates the Forgetful knowledge base using Serena's LSP-powered symbol analysis for accurate, comprehensive codebase understanding. Use when you say "encode this repository", "populate forgetful with this codebase", "onboard to this repo", "refresh project understanding", or "build knowledge base for this project".…