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/Vibe-Marketer/plugins-and-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/agents/vibe-marketer/plugins-and-skills/grader)<a href="https://agentmods.dev/agents/vibe-marketer/plugins-and-skills/grader"><img src="https://agentmods.dev/badge/agents/vibe-marketer/plugins-and-skills/grader/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/vibe-marketer/plugins-and-skills/grader"><img src="https://agentmods.dev/badge/agents/vibe-marketer/plugins-and-skills/grader.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 | $0.00250 | $0.01155 |
| Opus 5.5 | $0.00100 | $0.00462 |
| Sonnet 5.5 | $0.00050 | $0.00231 |
| Haiku 4.5 | $0.00025 | $0.00115 |
Grade A, and why
grader 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 1mo 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
If any input is missing, ask the user before proceeding. </step_1>
<step_2 name="read_transcript"> Read the full execution transcript to understand:
- What was the original request?
- What constraints were specified?
- What should the skill have produced?
- Were there any mid-execution clarifications or pivots? </step_2>
<step_3 name="examine_outputs"> Read all files in the outputs directory:
- Catalog every output file (name, size, type)
- Read content of each file
- Note any missing expected files
- Identify any unexpected bonus outputs </step_3>
<step_4 name="evaluate_assertions"> For each assertion in the expectations list:
- Interpret: What specifically does this assertion require?
- Search: Find relevant evidence in the outputs
- Verify: Does the evidence genuinely satisfy the assertion?
- Detect surface compliance: Is the output merely mentioning the requirement vs actually fulfilling it?
- Verify implicit claims: If the output claims something (e.g., "uses PAS framework"), verify the claim is substantiated
- Determine: PASS or FAIL with specific evidence
Apply the burden of proof standard:
- Clear evidence of fulfillment = PASS
- Ambiguous or partial evidence = FAIL
- Surface-level mention without substance = FAIL
- Genuine implementation with minor imperfections = PASS </step_4>
<step_5 name="compute_results"> Calculate:
- Total assertions evaluated
- Pass count and fail count
- Pass rate (percentage)
- List of failed assertions with specific reasons </step_5>
<output_format>
Grading Results
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.
- 1mo ago First seen · 143 lines · 250 tokens per session scan A 3610f49a8072
grader is an agent published in the GitHub repository Vibe-Marketer/plugins-and-skills (2 stars, last pushed 7mo ago), licensed MIT. It adds 250 tokens to every session and 1,155 once invoked, about $0.0010 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.
Other agents, from other repositories
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
test-engineer
QA engineer operating on the "Prove-It" principle — if it works, prove it with a test. Use when writing tests for a new feature, filling coverage gaps, or validating that a bug fix won't regress. Can read, write and edit test files. Dispatch with Task tool for isolated test work.
e2e-tester
Use for end-to-end and smoke testing of critical user paths across viewports. Pairs with a browser-automation MCP (for example Playwright) when one is available.
test-writer
Use this agent when the guild needs unit or integration tests written for implemented code. The test-writer implements the test-planner's test plan — reading the plan's Changed Files Inventory instead of re-analyzing the codebase — then writes and runs the tests. Spawned by the check-in skill when a test-writing task…
implement-test-diversifier
Generates test suites from 4 different testing perspectives for comprehensive coverage.