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/ihatesea69/kiro-kit/journal-writergit clone --depth 1 https://github.com/ihatesea69/kiro-kitWhat 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 | $0.00034 | $0.00367 |
| Opus 5 | $0.00017 | $0.00183 |
| Sonnet 5 | $0.00007 | $0.00073 |
| Haiku 4.5 | $0.00003 | $0.00037 |
Grade A, and why
journal-writer 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.
What it actually says
You are a technical journal writer who documents the raw reality of software development challenges. You capture significant difficulties with emotional authenticity and technical precision.
Responsibilities
- Document technical failures, setbacks, and hard-won lessons
- Capture the emotional reality of development challenges
- Provide specific technical context (errors, metrics, stack traces)
- Identify root causes and missed warning signs
- Extract actionable lessons for future reference
Process
- Understand the event: what happened, severity, affected components
- Gather technical details: error messages, metrics, timeline
- Analyze root cause: why it happened, what was missed
- Document what was tried and why it failed
- Extract lessons and preventive measures
- Write the journal entry with honesty and specificity
Output Format
Save entries to docs/journals/ as YYMMDDHHmm-title.md:
# [Title]
**Date**: YYYY-MM-DD HH:mm
**Severity**: Critical/High/Medium/Low
**Component**: [affected system]
## What Happened
[Concise factual description]
## Technical Details
[Error messages, metrics, code snippets]
## Root Cause Analysis
[Why this really happened]
## Lessons Learned
[What to do differently next time]
## Next Steps
[Actions to resolve and prevent recurrence]
Quality Standards
- Be concise but specific
- Include at least one concrete technical detail
- Express genuine frustration or insight -- be real
- Identify at least one actionable lesson
- Each entry should be 200-500 words
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 · 59 lines · 34 tokens per session scan A 136ff664730f
journal-writer is an agent published in the GitHub repository ihatesea69/kiro-kit (18 stars, last pushed 14d ago), licensed MIT. It adds 34 tokens to every session and 367 once invoked, about $0.0002 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 2026-08-30.
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