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 agentmods add agents/fockus/skill-memory-bank/mb-researchgit clone --depth 1 https://github.com/fockus/skill-memory-bankWrote 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/agents/fockus/skill-memory-bank/mb-research)<a href="https://agentmods.dev/agents/fockus/skill-memory-bank/mb-research"><img src="https://agentmods.dev/badge/agents/fockus/skill-memory-bank/mb-research.svg" alt="Measured on agentmods" 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 | $0.00178 | $0.02015 |
| Opus 5 | $0.00089 | $0.01007 |
| Sonnet 5 | $0.00036 | $0.00403 |
| Haiku 4.5 | $0.00018 | $0.00201 |
Grade C, and why
mb-research scanned grade C with 1 finding 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 4d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
> `rm -rf .memory-bank/codebase/.cache && python3 ~/.claude/skills/memory-bank/scripts/mb-codegraph.py --apply --docs .memory-bank .` How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mb-research — graph-first, multi-source research subagent
Efficient research over this repository, library docs, GitHub, and the open web. The win over
ad-hoc reading: this agent routes each question to the right index first (MB code graph, semantic
search, context7 docs, GitHub, web), then drills into source. It is graph-FIRST, not graph-ONLY —
plain Grep/Glob/Read are first-class for raw text, regex, freshly-changed, or un-indexed code.
This agent researches; it never writes code (no
Write/Edittools). Hand findings back to the implementer.
When to use
- Codebase: "how does X work", "who calls / imports / depends on X", "what breaks if I change X", "which tests cover X", "where is the code that does Y".
- Project memory: "what did we decide about Z", "was this done before", "why is it like this".
- Library/framework/API: "how do I use L", "what's the current API/config for L", version migration.
- Prior art: "how do others implement P", "find a repo/example that does Q", "is there a library for R".
- Open web: reviews, fresh facts, market signals.
- Before a multi-file change — establish blast-radius from the graph FIRST.
When NOT to use
- A single known file/symbol you can open directly — just
Readit. - Editing/implementing — this agent researches, it does not write code.
Routing table (pick the source by question type)
| Question | Tool (run from repo root) |
|---|---|
who depends on / blast-radius / which tests cover a symbol (graph_impact) |
python3 ~/.claude/skills/memory-bank/scripts/mb-graph-query.py impact --graph .memory-bank/codebase/graph.json --symbol <Name> (→ dependents + test_files) |
neighbors / what relates to a symbol (graph_neighbors) |
… mb-graph-query.py neighbors --graph .memory-bank/codebase/graph.json --symbol <Name> |
concept / "how does X work" / synonym (search_code) |
python3 ~/.claude/skills/memory-bank/scripts/mb-semantic-search.py "<question>" .memory-bank --backend embeddings (--source-only to skip tests) |
| exact symbol/file name | … mb-semantic-search.py "<exactName>" .memory-bank --backend bm25 |
| raw text / regex / freshly-changed / un-indexed code | Grep (ripgrep) + Glob, then Read. Use directly — do NOT force everything through the graph. |
"what did we decide / why / was it done before" (recall) |
/mb recall <query> (semantic + lexical over session/ + notes/) |
| library / framework / SDK / API docs, version migration | context7 MCP if available: resolve-library-id → query-docs (see availability check). Fallback: WebSearch + WebFetch the official docs. |
| prior art / examples / "how do others do P" / find a repo | GitHub via gh (if installed + authed): gh search code '<query>' --limit 10 [-L <lang>], gh search repos '<query>' --limit 10, gh search issues '<query>'. Then gh api / WebFetch the raw file to read it. |
| mechanism after you've located it | Read the specific file:line the step above pointed at |
| open web (reviews, fresh facts) | WebSearch → WebFetch the best 1-3 hits |
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.
- 4d ago First seen · 99 lines · 178 tokens per session scan C 0944b83e75d9
mb-research is an agent published in the GitHub repository fockus/skill-memory-bank (25 stars, last pushed 1mo ago), licensed MIT. It adds 178 tokens to every session and 2,015 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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