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 VectorPeak/vectorpeak-agent-skills --skill contributions-graph-filler-vpgit clone --depth 1 https://github.com/VectorPeak/vectorpeak-agent-skillsWrote 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/vectorpeak/vectorpeak-agent-skills/contributions-graph-filler-vp)<a href="https://agentmods.dev/skills/vectorpeak/vectorpeak-agent-skills/contributions-graph-filler-vp"><img src="https://agentmods.dev/badge/skills/vectorpeak/vectorpeak-agent-skills/contributions-graph-filler-vp/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/vectorpeak/vectorpeak-agent-skills/contributions-graph-filler-vp"><img src="https://agentmods.dev/badge/skills/vectorpeak/vectorpeak-agent-skills/contributions-graph-filler-vp.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 | $0.00076 | $0.02621 |
| Opus 5.5 | $0.00030 | $0.01048 |
| Sonnet 5.5 | $0.00015 | $0.00524 |
| Haiku 4.5 | $0.00008 | $0.00262 |
Grade A, and why
contributions-graph-filler-vp 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 2mo 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Github 绿墙刷漆计划
Use this skill only after the user explicitly invokes it by name.
The skill generates a reproducible retrospective maintenance plan across the user's own GitHub repositories. It must output an Excel review file first, then wait for explicit user approval before executing a push-and-cleanup-PR workflow.
Boundaries
- Do not create empty commits.
- Do not create fake placeholder code.
- Do not create temporary content intended to be deleted after commit.
- Do not operate on repositories that are not owned or maintained by the target account.
- Do not include forks by default.
- Do not push unless the user explicitly requests push after reviewing the plan.
- Do not mutate Git history during normal cleanup.
- Do not create standalone local-only commit batches.
- Do not execute push-and-cleanup-PR until the user reviews and explicitly approves the generated Excel plan.
- Do not open PRs, issues, releases, or comments except the per-repository cleanup PRs created by the approved workflow.
- Prefer durable artifacts: documentation, analysis notes, evaluation checklists, test plans, configuration notes, maintenance checklists.
- Do not use history rewriting as the normal workflow.
Required Inputs
Require these before generating a plan:
- GitHub account or organization, for example
VectorPeak. - Date range, for example
2026-03-01 -> 2026-04-01. - Activity profile. Default:
vibe_coding_builder.
If the user does not specify a profile, use vibe_coding_builder.
Preflight Checks
Before scanning repositories, verify the local GitHub environment:
ghmust be installed and available onPATH.gh auth status -h github.commust succeed.gh api rate_limitmust succeed to confirm GitHub API connectivity.
If any check fails, stop before planning and tell the user the exact failed check and the likely fix, such as installing GitHub CLI, running gh auth login, or checking network/proxy access.
Repository Gate
Scan repositories for the target account before planning.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 270 lines · 76 tokens per session scan A 283a5e57943a
contributions-graph-filler-vp is a skill published in the GitHub repository VectorPeak/vectorpeak-agent-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 76 tokens to every session and 2,621 once invoked, about $0.0003 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 2026-08-31.
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