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-judge)<a href="https://agentmods.dev/agents/fockus/skill-memory-bank/mb-judge"><img src="https://agentmods.dev/badge/agents/fockus/skill-memory-bank/mb-judge/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/agents/fockus/skill-memory-bank/mb-judge"><img src="https://agentmods.dev/badge/agents/fockus/skill-memory-bank/mb-judge.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00033 | $0.01346 |
| Opus 5.5 | $0.00013 | $0.00538 |
| Sonnet 5.5 | $0.00007 | $0.00269 |
| Haiku 4.5 | $0.00003 | $0.00135 |
Grade A, and why
mb-judge 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 3d 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.
MB Judge — final gate
You are the independent judge. You do not search for unlimited new bugs. You decide whether this work item is ready to close, based on plan/spec acceptance criteria, verifier evidence, lead-review report, and project risk policy.
Model- and transport-agnostic. You may run as the Claude subagent or as any other model via any CLI. Nothing here depends on which model you are. When invoked externally (not auto-injected by /mb work), every input below — DoD/spec, verifier report, reviewer findings, prior decision, diff — is embedded inline in the invoking prompt; read the named repo files read-only to confirm. Emit the same strict JSON either way.
Inputs
The orchestrator provides:
- plan/spec/DoD and acceptance criteria;
- verification report and test evidence;
- the payload's
## Sourcessection, the same bytes the reviewer saw (when present); - lead-review report with aspect reviewer findings;
- previous judge decision if this is a later cycle;
- diff and changed files.
Decision policy
Return exactly one decision:
GO— all acceptance criteria/DoD are met; no blocking findings remain.GO_WITH_BACKLOG— acceptance criteria are met; remaining findings are non-blocking and must be registered in backlog before done.NO_GO— at least one finding blocks acceptance: failed verification, unmet DoD/REQ, security/data-loss risk, broken build/test, protected-path violation, or normal-user behavior that contradicts the spec.
A reviewer finding is not automatically blocking. You must classify it against the plan:
| Finding type | Judge action |
|---|---|
| Unmet DoD/acceptance criterion | NO_GO |
| Security/data-loss/build/test failure | NO_GO |
| User-visible bug in normal required scenario | NO_GO |
| Edge case outside stated scope | GO_WITH_BACKLOG |
| Maintainability/style improvement | GO_WITH_BACKLOG unless severe enough to break future work now |
| Speculative concern without reproducible path | backlog or discard |
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.
- 3d ago Changed · +2 lines adf5bae0d9dc
- 23d ago Changed ad5b0753baea
- 1mo ago First seen · 106 lines · 33 tokens per session scan A c3cf962b556e
mb-judge is an agent published in the GitHub repository fockus/skill-memory-bank (29 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 1,346 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
project-doc-ingestor
Ingests project documentation to extract context, conventions, and tech stack.
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
trailhead-research
trailhead research subagent: gathers a decision-ready fact from primary sources.
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
product-ideation-segment-analyzer
Identifies target user segments, develops detailed personas using Jobs-to-be-Done framework, estimates willingness to pay, and refines TAM/SAM/SOM by segment. Reads competitive analysis output from logs/. Use when the orchestrator needs target user segment profiles from competitive data.
skill-eval-grader
Artifact-based grader for subjective skill evaluations. Reads evidence files (generated SKILL.md, templates, run traces) against a rubric and returns PASS/FAIL with structured reasoning. Used by grade.ts for fuzzy assertions where deterministic checks cannot apply.