plugin-maintenance

plugin-maintenance is a skill for Claude Code from Galbaz1/video-research-mcp. It costs 30 tokens per session (653 once invoked), scanned A, original, MIT.

A maintenance workflow for auditing and updating a coding plugin's models, dependencies, instructions, documentation, tests, and release process. It checks official sources before making a small, documented change.

In plain words
What is it for?
Use it to inspect a plugin, verify its dependencies and provider APIs, update one evidenced problem, and record the tests, sources, and release decisions.
Why use it?
It reduces the risk of relying on outdated model names, package versions, or undocumented API behavior. It also helps protect other work in a shared repository.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions AGENTS.md.

Part of the gr plugin — 27 skills, 17 commands, 7 agents shipped together

Good fit Use it to inspect a plugin, verify its dependencies and provider APIs, update one evidenced problem, and record the tests, sources, and release decisions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/galbaz1/video-research-mcp/plugin-maintenance
Install

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.

Any agent
npx skills add Galbaz1/video-research-mcp --skill plugin-maintenance
Clone the repo
git clone --depth 1 https://github.com/Galbaz1/video-research-mcp

Made for: Claude Code.

Or install gr, the plugin that ships this one along with the rest of its 27 skills, 17 commands, 7 agents.

Wrote 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.

agentmods badge for plugin-maintenance

README.md
[![agentmods](https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/plugin-maintenance/github.svg)](https://agentmods.dev/skills/galbaz1/video-research-mcp/plugin-maintenance)
Your own site
<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/plugin-maintenance"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/plugin-maintenance/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.

agentmods 80×15 button for plugin-maintenance

Your own site · 80×15
<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/plugin-maintenance"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/plugin-maintenance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 653 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.00653
Opus 5.5 $0.00012 $0.00261
Sonnet 5.5 $0.00006 $0.00131
Haiku 4.5 $0.00003 $0.00065

Measured 22d ago against content hash c4a21d666293, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

plugin-maintenance 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 22d 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.

skills/plugin-maintenance/SKILL.md · 53 lines

What it actually says

Plugin Maintenance

Use the current user mandate and checkout as authority. Installed plugin files and a remembered model name are leads; the source tree, official provider documentation and registry metadata determine what can change.

  1. Read AGENTS.md, scoped instructions and the active tracker issue. Verify HEAD, dirty paths, remote and scripts/detect_review_scope.py --json. Preserve other work; isolate a new change when ownership requires it. Record the outcome, protected behavior, allowed writes and publication authority in the repository's tracker. If no tracker is configured, record that limitation.
  2. Inventory runtime model IDs, dependency constraints and locks, installed file mappings, public tools and front-door docs. Verify newer stable releases and exact API contracts using primary provider docs and PyPI/npm metadata. Record URLs, inspection date and any deliberately retained supported API.
  3. Choose one evidenced defect or upgrade. Implement the smallest complete correction with the affected tests and docs. Independent workers need disjoint write paths and a single integration owner; inherit the configured model. A newer model string is insufficient if its thinking, cache, response or session contract differs.
  4. Run the affected check. Let fresh results select the next correction. Preserve failures and skipped checks in the evidence. After one controlled intervention repeats the same infrastructure failure, stop that path and record the gap. Continue independent authorized work. Stop when the accepted outcome is achieved, evidence shows no useful next action, access is missing, resources are exhausted, or the user stops the work.
  5. Join required workers, then run the final gates once: locked tests and lint for all three Python packages; node --test tests/installer.test.js; security smoke and offline tool-security checks; uv run python scripts/check_release.py; Python builds and npm pack --dry-run. Re-run affected checks only after a new change or a concrete unresolved failure. Verify a built MCP artifact through tool discovery and a read-only configuration call before claiming packaging acceptance. Provider execution and generated-media quality need separate authorized evidence.
  6. Record exact commits, checks, limitations and next eligible work in the tracker and an audit receipt. Commit explicit owned paths. Push, merge, registry upload and GitHub release creation only within the user's stated authority. Keep review slices single-purpose; distinguish a source release from a registry install and from a running provider-backed server.

For later runs, resume the issue and verify any active worker/job identity before dispatching. One coordinator owns canonical writes. A scheduled prompt alone is not self-improvement: promote a workflow change only after a recorded recurring failure and a later independent check of protected capabilities. Use the host's supported automation facility only when recurring continuation is authorized; do not launch an unbounded paid loop from this skill.

Changes

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.

  1. 22d ago First seen · 53 lines · 30 tokens per session scan A c4a21d666293

Subscribe to this mod's changes

plugin-maintenance is a skill published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 5d ago), licensed MIT. It adds 30 tokens to every session and 653 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 2026-10-01.