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 TimoBakx/ai-journaling-template --skill reflectiongit clone --depth 1 https://github.com/TimoBakx/ai-journaling-templateWrote 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/timobakx/ai-journaling-template/reflection)<a href="https://agentmods.dev/skills/timobakx/ai-journaling-template/reflection"><img src="https://agentmods.dev/badge/skills/timobakx/ai-journaling-template/reflection/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/timobakx/ai-journaling-template/reflection"><img src="https://agentmods.dev/badge/skills/timobakx/ai-journaling-template/reflection.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 | $0.00034 | $0.01970 |
| Opus 5.5 | $0.00014 | $0.00788 |
| Sonnet 5.5 | $0.00007 | $0.00394 |
| Haiku 4.5 | $0.00003 | $0.00197 |
Grade A, and why
reflection 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 17d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Daily Reflection
Daily reflections make visible where the user worked above expectations and where a ball was dropped, weighed against the expectation per component in work-evaluation/expectations.md. Each point gets a backlink in that file, so it can be found again for a weekly review, a check-in or a performance review.
When
- End of day, during the "🔍 End of Day - Reflection" section of the daily journal
- Fill in together: the user shares (text, fragments, answers); the AI coach asks questions, mirrors, structures and makes connections. The user keeps final editorial control: what goes into the journal has to fit them.
- Brief but meaningful; not extensive (that is for weekly reviews)
Division of roles (fixed)
The AI fills in most of it based on the Daily Flow; the user has final editorial control.
| Section | Who fills it | Notes |
|---|---|---|
| What I Am Proud Of | User | Entirely the user's field. The AI never fills this in, not even as a suggestion or example. May stay empty; empty is honest and counts as data. |
| Follow-up questions when unclear | AI | When something is unclear, always ask a short follow-up question, also on the pride field if that is needed to sharpen it. |
| What You Can Be Proud Of (outside view) | AI | Outside mirror; stay critical, do not exaggerate. On days when the user is a bit too negative about themselves: actively make positive moments visible without flattering. |
| Who Saw You Today? | AI | Fill in based on the journal, meetings and connected sources. Positive interaction in a meeting counts just as much as an explicit compliment. Do not invent compliments; truly empty is data. The user may add to it. |
| Short Observation | AI | The AI's perspective on the day in 1 to 3 sentences. |
| Above Expectations | AI | Weigh strictly against the expectation per component. Empty is a fine outcome. |
| Dropped Balls | AI | Genuinely critical, each point with a fix. Candidates that rest on an assumption are presented as a question; the user decides. |
| Carry Into Tomorrow | AI | Fill directly from the journal: what came in today that applies tomorrow. |
| Ongoing Reminder | AI | Evolving insight for the rest of the week relative to the week kickoff. No day-specific aftermath. |
| Backlinks | AI | Pick up directly after the user confirms the reflection text, including work-evaluation/expectations.md. |
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.
- 17d ago First seen · 80 lines · 34 tokens per session scan A 8386f25678e3
reflection is a skill published in the GitHub repository TimoBakx/ai-journaling-template (34 stars, last pushed 18d ago), licensed MIT. It adds 34 tokens to every session and 1,970 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 2026-10-15.
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