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 skills add mpaarating/ai-workflow-kit --skill journalgit clone --depth 1 https://github.com/mpaarating/ai-workflow-kitWrote 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/skills/mpaarating/ai-workflow-kit/journal)<a href="https://agentmods.dev/skills/mpaarating/ai-workflow-kit/journal"><img src="https://agentmods.dev/badge/skills/mpaarating/ai-workflow-kit/journal/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mpaarating/ai-workflow-kit/journal"><img src="https://agentmods.dev/badge/skills/mpaarating/ai-workflow-kit/journal.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.01125 |
| Opus 5.5 | $0.00006 | $0.00450 |
| Sonnet 5.5 | $0.00003 | $0.00225 |
| Haiku 4.5 | $0.00002 | $0.00112 |
Grade A, and why
journal 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 2mo 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Journal
Trigger Phrases
- "journal:"
- "reflect:"
- "today I"
- "feeling [emotion]"
- "grateful for"
- "on my mind:"
Inputs
The user provides a journal entry inline with the trigger phrase. The entry can be:
- A sentence or two: "journal: Had a breakthrough on the caching layer today"
- A mood expression: "feeling overwhelmed — too many threads open"
- A gratitude note: "grateful for Sarah's help debugging that race condition"
- A reflection: "reflect: I notice I default to building instead of asking for help"
Workflow
Step 1: Parse the Entry
Extract the journal content from the user's message.
- Strip the trigger prefix ("journal:", "reflect:", etc.)
- Preserve the user's exact words — do not rewrite, summarize, or polish
- If the entry is just a trigger phrase with no content (e.g., "journal:"), ask: "What's on your mind?"
Step 2: Detect Mood
Analyze the entry for mood signals. Assign one primary mood:
| Mood | Signals |
|---|---|
| Energized | excitement, momentum, breakthrough, shipped, crushed it |
| Focused | deep work, flow state, locked in, making progress |
| Neutral | status updates, factual observations, no strong emotion |
| Overwhelmed | too much, scattered, can't focus, drowning, behind |
| Tired | drained, exhausted, low energy, long day, need a break |
| Frustrated | stuck, blocked, annoyed, broken, doesn't work, ugh |
| Grateful | thankful, grateful, appreciate, lucky, helped by |
- If mood is ambiguous, default to Neutral
- Do not ask the user to confirm the mood — just detect it quietly
Step 3: Detect Tags
Assign 1-3 tags based on content:
| Tag | Signals |
|---|---|
| Work | code, meeting, ticket, PR, deploy, team, project |
| Personal | family, health, hobby, weekend, home, friends |
| Reflection | I notice, pattern, thinking about, wondering, realized |
| Gratitude | grateful, thankful, appreciate, helped by |
| Learning | learned, TIL, figured out, discovery, insight |
Step 4: Check for Today's Daily Page
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.
- 2mo ago First seen · 135 lines · 16 tokens per session scan A 15fdc30faa3a
journal is a skill published in the GitHub repository mpaarating/ai-workflow-kit (2 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 1,125 once invoked, about $0.0001 per session on Opus 5.5. 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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