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 cyberbird2048/gbrainmcp-clean --skill maintaingit clone --depth 1 https://github.com/cyberbird2048/gbrainmcp-cleanWrote 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/cyberbird2048/gbrainmcp-clean/maintain)<a href="https://agentmods.dev/skills/cyberbird2048/gbrainmcp-clean/maintain"><img src="https://agentmods.dev/badge/skills/cyberbird2048/gbrainmcp-clean/maintain/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/cyberbird2048/gbrainmcp-clean/maintain"><img src="https://agentmods.dev/badge/skills/cyberbird2048/gbrainmcp-clean/maintain.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 | $0.00044 | $0.03833 |
| Opus 5 | $0.00022 | $0.01917 |
| Sonnet 5 | $0.00009 | $0.00767 |
| Haiku 4.5 | $0.00004 | $0.00383 |
Grade A, and why
maintain 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 9d 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.
This is a copy
86% identical to maintain — 135 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maintain Skill
Periodic brain health checks and cleanup.
Contract
This skill guarantees:
- All health dimensions are checked (stale, orphan, dead links, cross-refs, backlinks, citations, filing, tags)
- Each issue found has a specific fix action
- Back-link iron law is enforced
- Citation format is validated against the standard
- Results are reported with counts per dimension
Phases
- Run health check. Check gbrain health to get the dashboard.
- Check each dimension:
Stale pages
Pages where compiled_truth is older than the latest timeline entry. The assessment hasn't been updated to reflect recent evidence.
- Check the health output for stale page count
- For each stale page: read the page from gbrain, review timeline, determine if compiled_truth needs rewriting
Orphan pages
Pages with zero inbound links. Nobody references them.
- Review orphans: are they genuinely isolated or just missing links?
- Add links in gbrain from related pages or flag for deletion
Dead links
Links pointing to pages that don't exist.
- Remove dead links in gbrain
Missing cross-references
Pages that mention entity names but don't have formal links.
- Read compiled_truth from gbrain, extract entity mentions, create links in gbrain
Link graph extraction
If link_count is 0 or low relative to page_count, run batch extraction:
gbrain extract links --dir ~/brain
This scans all markdown files for entity references, See Also sections, and frontmatter fields, then creates typed links in the database.
Timeline extraction
If timeline_entry_count is 0, extract structured timeline from markdown:
gbrain extract timeline --dir ~/brain
Dream cycle (v0.23): synthesize + patterns
gbrain dream runs the full 8-phase maintenance cycle:
lint -> backlinks -> sync -> synthesize -> extract -> patterns -> embed -> orphans
The two new phases consolidate yesterday's conversations into long-term memory:
Synthesize phase: reads transcripts from dream.synthesize.session_corpus_dir,
runs a cheap Haiku verdict (cached in dream_verdicts) to filter routine
ops sessions, then fans out one Sonnet subagent per worth-processing
transcript. Each subagent writes reflections (wiki/personal/reflections/...),
originals (wiki/originals/ideas/...), and people timeline entries. The
orchestrator collects the slugs from subagent_tool_executions (NOT
pages.updated_at — that would pick up unrelated writes) and reverse-renders
each new page from DB → markdown on disk.
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.
- 9d ago First seen · 403 lines · 44 tokens per session scan A b4cd5b30d206
maintain is a skill published in the GitHub repository cyberbird2048/gbrainmcp-clean (0 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 3,833 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to maintain, differing in 135 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…