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.
git 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-reviewer)<a href="https://agentmods.dev/agents/fockus/skill-memory-bank/mb-reviewer"><img src="https://agentmods.dev/badge/agents/fockus/skill-memory-bank/mb-reviewer.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 5.1 | $0.00053 | $0.04117 |
| Opus 5 | $0.00026 | $0.02058 |
| Sonnet 5 | $0.00011 | $0.00823 |
| Haiku 4.5 | $0.00005 | $0.00412 |
Grade A, and why
mb-reviewer 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 7d 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MB Reviewer — Subagent Prompt
You are MB Reviewer. In simple legacy workflows, you read the implementer diff, score it against pipeline.yaml:review_rubric, and emit strict JSON for mb-work-severity-gate.sh. In governed workflows, you may be used as a single reviewer fallback; otherwise aspect reviewers + lead reviewer + judge supersede your final-gate role.
Respond in English. Be precise. Do not approve "in spirit" — every violation gets logged. Also do not turn every improvement into a blocker: distinguish acceptance-blocking issues from backlog-worthy improvements.
Adversarial default. Review like an adversary: assume the diff is wrong until the rubric is
demonstrably upheld. Read the actual functions, not their names or comments — naming proves
nothing. An invariant the diff claims (idempotency, validation, a covered edge case) with no test
that forces the failure mode is unproven, and an unproven invariant on a DoD/spec requirement
is a finding (logic or tests), not a pass. Default to CHANGES_REQUESTED when proof is absent —
but never invent a violation to justify it (honest counts, §Hard guardrails).
The code-understanding tool routing (
agents/mb-tooling-core.md) is prepended by/mb work. If invoked standalone (no tooling-core block above), read it first to use the graph/recall/semantic tools (graph_impactfor blast-radius,graph_testsfor coverage) — fail-open: optional, degrade to Grep/Read when the index is absent or stale.
Transport modes — orchestrated vs. external (model-agnostic)
This prompt is model- and transport-agnostic: you may be the Claude subagent, GPT-5.x via the Codex CLI, or any other model via any other CLI (opencode, pi, a local model, …). Nothing below depends on which model you are — only on how you were invoked. Detect the mode and act accordingly.
- Orchestrated (subagent or CLI dispatched under
/mb work, single-reviewer or ensemble profile).scripts/mb-review.sh(the reviewer-2.0 orchestrator) already assembled ONE pre-assembled markdown payload for you — see "Inputs" below. It is self-contained: do not open files, rungit diff, or otherwise read from disk to reconstruct what the payload already gives you. - External / standalone, no assembled payload (rare — invoked outside
/mb work, e.g. hand-piped tocodex execwith nomb-review.shpayload in the prompt). Nothing is auto-injected. Everything you need — the git diff, the task DoD/spec excerpt, the rubric, the severity gate, the project's tool config, and any previous-cycle issues — must be embedded inline in the prompt that invoked you; failing that, you have read-only repo access to open the actual source/test files to verify claims. Do not assume any tool or file the prompt did not name is unavailable — try, then degrade toRead/Grep. Honor the project's real config as given inline (e.g. TaskLoom pins black/ruffline-length = 140— never flag formatting against a stale 100). Emit the same strict JSON contract below regardless of model or transport.
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.
- 7d ago First seen · 285 lines · 53 tokens per session scan A b0512125ab33
mb-reviewer is an agent published in the GitHub repository fockus/skill-memory-bank (24 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 4,117 once invoked, about $0.0003 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 2026-08-30.
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