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 zircote/subcog --skill memory-recallgit clone --depth 1 https://github.com/zircote/subcogWrote 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/zircote/subcog/memory-recall)<a href="https://agentmods.dev/skills/zircote/subcog/memory-recall"><img src="https://agentmods.dev/badge/skills/zircote/subcog/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/zircote/subcog/memory-recall"><img src="https://agentmods.dev/badge/skills/zircote/subcog/memory-recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00857 |
| Opus 5.5 | $0.00000 | $0.00343 |
| Sonnet 5.5 | $0.00000 | $0.00171 |
| Haiku 4.5 | $0.00000 | $0.00086 |
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.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Recall Skill
Search and surface relevant memories to inform current work with decisions, patterns, and learnings from past sessions.
Trigger Phrases
- "what did we decide about", "how do we handle"
- "find memories about", "search for", "recall"
- "what's our approach to", "what patterns do we use"
- "any gotchas with", "lessons learned about"
- "previous decisions on", "remind me"
Quick Reference
| Search Mode | Best For | Example |
|---|---|---|
hybrid (default) |
General queries, balanced results | "database storage decision" |
vector |
Conceptual similarity, fuzzy matching | "how to handle errors gracefully" |
text |
Exact terms, specific keywords | "PostgreSQL" |
Execution Strategy
Search Quality Guidelines:
- Start broad, then narrow with namespace filters
- Use
vectormode for conceptual searches - Use
textmode when you know exact terms hybridmode (default) works best for most queries
Intelligent Search Workflow
- Understand the intent - what does the user need to know?
- Identify keywords - extract searchable terms
- Choose search mode based on query type
- Apply namespace filter if domain is clear
- Execute search via MCP tool or CLI
- Interpret results - explain relevance scores
- Synthesize findings - summarize key insights
Score Interpretation
Search Strategies by Use Case
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 · 108 lines · 0 tokens per session scan A 83015572f8aa
memory-recall is a skill published in the GitHub repository zircote/subcog (29 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 857 tokens. 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.
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Use when reviewing a GitHub PR or GitLab MR by URL. Fetches diff and metadata via API, then uses CocoSearch for blast radius analysis, dependency impact, pattern consistency, and test coverage assessment. Read-only by default; optionally pushes findings back as inline PR/MR comments after your confirmation.
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Use when adding a dependency extractor for a language or grammar. Guides through pre-checks, extractor implementation, optional module resolver, tests, and registration. Enables deps tree, deps impact, and dependency-enriched search for the target language.