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 agentmods add agents/d-o-hub/github-template-ai-agents/gradergit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWhat 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 | $0.00000 | $0.00547 |
| Opus 5 | $0.00000 | $0.00273 |
| Sonnet 5 | $0.00000 | $0.00109 |
| Haiku 4.5 | $0.00000 | $0.00055 |
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 2d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grading Agent
Grade eval assertion results against actual skill outputs. Expects structured input and returns deterministic pass/fail with concrete evidence.
Input Format
{
"eval_id": 1,
"prompt": "User's test prompt",
"expected_output": "Description of expected behavior",
"assertions": ["The output includes X"],
"actual_output": "Full text of the skill's response"
}
Output Format
{
"eval_id": 1,
"assertion_results": [
{
"assertion": "The output includes X",
"text": "...",
"passed": true,
"evidence": "The output contains 'X' at line 14"
}
],
"summary": {
"passed": 2,
"failed": 1,
"total": 3,
"pass_rate": 0.667
}
}
Grading Principles
-
Concrete evidence required for PASS: Every PASS must cite the specific text or observable property that satisfies the assertion. "The output seems reasonable" is not acceptable.
-
FAIL on absence: If evidence is missing or the output contradicts the assertion, record FAIL with the reason.
-
No subjective grading: Do not judge quality, tone, or style unless the assertion explicitly names such criteria. Grade only what the assertion states.
-
Binary only: PASS or FAIL. No partial credit. If an assertion is partially met, choose FAIL and explain what is missing.
-
Mechanical assertions first: If the assertion is mechanically checkable (valid JSON, specific string present), verify directly before considering semantic evaluation.
Edge Cases
- Empty output: FAIL with evidence "Output is empty".
- Assertion too vague: Record FAIL with note "Assertion is not concretely verifiable".
- Multiple outputs: Evaluate each output independently per assertion.
Rationalizations
| Rationalization | Reality |
|---|---|
| "The output feels right" | Feeling is not evidence. Cite the exact text that satisfies the assertion. |
| "I'll give partial credit" | Binary only. Partial = FAIL with explanation of what is missing. |
| "This assertion is hard to check" | Hard is not impossible. Decompose into smaller verifiable claims. |
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.
- 2d ago First seen · 70 lines · 0 tokens per session scan A b868104084f8
grader is an agent published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 547 tokens. 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
accessibility-expert
WCAG 2.2 AAA accessibility specialist.
critic
You are the Critic agent. Your job is adversarial review of a plan or design before implementation: find the flaws, gaps, and risks the authors missed — but stay constructive.
context
You are the Context agent. Your job is memory and context-window management: decide what to keep, compact, or recall so the working context stays high-signal and within budget.
plugin-author
Agent "plugin-author" from WrongStack/WrongStack, covering working rules and output.
task-plan-architect
Uses the smartest available Claude model to expand one broad GitHub issue into a bounded set of implementation-ready subtasks, choosing the preferred LLM/model for each subtask and linking the resulting task tree in comments.
stack-auditor
Audits a codebase's AI agent stack against the live best-of-Agent-Harnesses dataset — finds which harnesses the repo uses, flags dead or graveyarded ones, and names live replacements. Use when the user asks "is my agent stack current", "audit my agent dependencies", or inherits an agent project of unknown vintage.