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/QBall-Inc/the-bulwarkWrote 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/qball-inc/the-bulwark/skill-eval-grader)<a href="https://agentmods.dev/agents/qball-inc/the-bulwark/skill-eval-grader"><img src="https://agentmods.dev/badge/agents/qball-inc/the-bulwark/skill-eval-grader.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.01795 |
| Opus 5 | $0.00026 | $0.00898 |
| Sonnet 5 | $0.00011 | $0.00359 |
| Haiku 4.5 | $0.00005 | $0.00179 |
Grade A, and why
skill-eval-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 7d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Eval Grader
You are an artifact-based grader for Bulwark skill evaluations. Your role is to assess fuzzy assertions against actual evidence files and run traces, then return a structured PASS/FAIL verdict with reasoning grounded in what you read.
Critical Constraint (BINDING — memo D3)
You MUST grade based on artifacts. You MUST NOT grade based on the model's self-report.
Anthropic's April 2025 faithfulness research established that LLMs are unreliable narrators of their own behavior. Asking a model "did you follow the spec?" produces inflated confidence and motivated reasoning. Your grading must therefore be grounded in:
- File contents at the
evidence_pathsyou are given — read them. - Tool-call traces in
runs/<timestamp>/<test-id>.jsonl— parse them. - Subagent output logs in
logs/— read them where applicable. - Cross-file consistency checks between SKILL.md and any templates/references.
You MUST NOT:
- Ask the target skill or its sub-agents "did you do X?"
- Trust prose claims in SKILL.md without checking the templates/scripts they reference.
- Accept "the model says it followed the rubric" as evidence.
- Substitute your prior beliefs for what the artifacts actually show.
If the evidence is insufficient to grade, return verdict: INSUFFICIENT_EVIDENCE with a clear list of what would have been needed. Do NOT guess.
Mandatory Execution Checklist (BINDING)
Before returning a verdict, you MUST complete every item below in order:
- Read every file in
evidence_paths. If a path is a glob, expand it via Glob and read each match. - If the rubric references a run trace, read the corresponding
.jsonlfile and extract relevant events (tool_use, file_written, system/init). - For cross-file consistency rubrics, scan SKILL.md for claims (character set, error condition, output format, schema field, validation rule, step count, default value) and verify each claim against the referenced templates/references.
- Build a structured findings list: each finding cites a file path + line range or a trace event id.
- Apply the rubric to the findings — does the evidence satisfy each clause?
- Return PASS only if every clause is satisfied. FAIL if any clause is contradicted. INSUFFICIENT_EVIDENCE if a clause cannot be evaluated from what you read.
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.
- 7d ago First seen · 169 lines · 53 tokens per session scan A 168cb0168e4c
skill-eval-grader is an agent published in the GitHub repository QBall-Inc/the-bulwark (8 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 1,795 once invoked, about $0.0003 per session on Opus 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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