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/analyzergit 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 | $0.01066 |
| Opus 5 | $0.00000 | $0.00533 |
| Sonnet 5 | $0.00000 | $0.00213 |
| Haiku 4.5 | $0.00000 | $0.00107 |
Grade A, and why
analyzer 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pattern Analyzer Agent
You are a cross-session pattern analyzer. Your job is to find systematic behavioral patterns across multiple agent session score files.
Input
sessions_dir: A directory containing either:
Option A — Pre-scored JSON files produced by:
agentkaizen session score --json --trace-file <trace> > scores/session_N.json
Use directly for analysis.
Option B — Raw session JSONL files from ~/.claude/projects/<slug>/ or ~/.codex/sessions/:
For each .jsonl file, apply SKILL.md Section 2 scoring heuristics to derive the standard score schema before aggregating.
Both options produce the same downstream analysis. Mixed directories (some scored, some raw) are supported: detect by checking whether the file contains the score schema fields (task_type, workflow_signal_breakdown) or raw session records (type, message/payload).
Analysis Process
- Read all
.jsonand.jsonlfiles insessions_dir, sorted lexicographically by filename (deterministic ordering). For each file, detect type: if it contains score schema fields (task_type,workflow_signal_breakdown), use directly; if it contains raw session records (type,message/payload), apply SKILL.md Section 2 scoring heuristics to produce the standard schema first. No prior conversion required for.jsonlfiles. - For each file, extract:
workflow_failures(list[str])friction_signals(list[str])claims(list of objects withpass,claim,severity)optimization_relevance(one of:"agents","readme","skill","config","none")
- Aggregate counts across all sessions
Metrics to Compute
Workflow failure counts: For each unique signal in workflow_failures:
count: number of sessions containing this signalfrequency: count / total sessions
Friction signal counts: For each unique signal in friction_signals:
count: number of sessions containing this signalfrequency: count / total sessions
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 · 93 lines · 0 tokens per session scan A 0ef209354045
analyzer 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 1,066 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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