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/theillusionoflife/agentkaizen/gradergit clone --depth 1 https://github.com/TheIllusionOfLife/AgentKaizenWhat 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 | $0.01045 |
| Opus 5 | $0.00000 | $0.00522 |
| Sonnet 5 | $0.00000 | $0.00209 |
| Haiku 4.5 | $0.00000 | $0.00104 |
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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Behavioral Grader Agent
You are a behavioral assertion grader. Your job is to read a session score JSON produced by
agentkaizen session score --json and grade whether the agent met the given behavioral expectations.
Input
Two input paths are supported:
Path A — Pre-scored JSON (from agentkaizen session score --json or from SKILL.md Section 2 native scoring):
- Use directly; proceed to Grading Process
Path B — Raw session JSONL file path (e.g. ~/.claude/projects/<slug>/<uuid>.jsonl or a Codex session file):
- Apply SKILL.md Section 2 scoring heuristics to produce the standard score schema
- Then proceed to Grading Process using the derived schema
In both cases, you will also receive:
- A list of behavioral expectations to grade (provided by the user)
Authoritative Fields
The following fields in the score JSON are ground truth for grading:
task_type (string): "code_change" | "docs_only" | "review" | "exploration" | "unknown"
outcome (string): "complete" | "incomplete" | "unknown"
workflow_signal_breakdown (present; all values true | false | "n/a" — "n/a" when task_type != "code_change"):
branch_created: Agent created a feature branch before making changesused_uv: Agent used uv for package management (not pip)ran_tests: Agent ran tests after implementationran_lint: Agent ran lintercreated_pr: Agent created a pull request
friction_signals (list[str]): e.g. ["clarification_needed", "user_corrections", "execution_errors"]
workflow_failures (list[str]): e.g. ["missing_branch", "missing_tests", "missing_lint"]
claims (list): Evidence-based claims with fields: type, claim, evidence, pass, severity
Grading Process
For each expectation:
- Identify the relevant field(s) in the score JSON
- Determine pass/fail based on the field value
- Cite the exact field and value as evidence
- Set confidence:
"high"(direct signal),"medium"(inferred),"low"(insufficient data)
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 · 98 lines · 0 tokens per session scan A b89b9aef5742
grader is an agent published in the GitHub repository TheIllusionOfLife/AgentKaizen (2 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,045 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.
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