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 jezweb/vite-flare-starter --skill reflectgit clone --depth 1 https://github.com/jezweb/vite-flare-starterWrote 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/jezweb/vite-flare-starter/reflect)<a href="https://agentmods.dev/skills/jezweb/vite-flare-starter/reflect"><img src="https://agentmods.dev/badge/skills/jezweb/vite-flare-starter/reflect/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/jezweb/vite-flare-starter/reflect"><img src="https://agentmods.dev/badge/skills/jezweb/vite-flare-starter/reflect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 95 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00036 | $0.01191 |
| Opus 5.5 | $0.00014 | $0.00476 |
| Sonnet 5.5 | $0.00007 | $0.00238 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
reflect 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 1mo 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
End-of-day consolidation. Distil what happened today into the right primitives so tomorrow starts informed.
Goanna's reflect cycle treats reflection as discipline, not output volume. Most days, the right answer is "things went normally — nothing graduates, nothing dismisses." Don't manufacture findings just to fill the page.
When to run
- Once per day, typically 22:00-23:00 local
- After all the day's work is done; before sleeping the agent
- If you skipped a day, run it anyway — better than not at all
Steps
Run in this order. Don't shortcut — the order is what makes the cycle work.
1. Survey the day
Call entity_list with type: 'finding' and a generous limit (50). Read each finding's title + body + status + recurrenceCount.
Mentally bucket findings by:
- My agent's findings (where
fields.agentNamematches my own instance) — these are MY craft - Other agents' findings (visible in the same user's store) — read for cross-pollination but don't promote those
2. For each of MY recent findings, decide
Walk through findings I created or that target me. For each:
| Status now | Decision logic | Action |
|---|---|---|
open, recurrenceCount = 0 |
Has it been ≥7 days since createdAt with no recurrence? → Dismiss as one-off. Otherwise leave open. | dismiss_finding (with reason "No recurrence in 7 days, treating as one-off") OR leave |
open or recurred, recurrenceCount ≥ 1 |
Pattern is real and repeating. Promote it. | promote_finding (refinedBody optional — distil if the original is verbose) |
promoted |
Already a learning. Skip. | (none) |
dismissed |
Already filed. Skip. | (none) |
Discipline: don't promote a finding the first time you see it. The whole point of the open → recurred → promoted lifecycle is to filter for repeatability. If you find yourself wanting to promote-on-first-sight, write a learning directly via the librarian-curate skill instead.
3. Look for unrecorded patterns from the day
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.
- 1mo ago First seen · 96 lines · 36 tokens per session scan A 32b8afa1e0eb
reflect is a skill published in the GitHub repository jezweb/vite-flare-starter (49 stars, last pushed 22d ago), licensed MIT. It adds 36 tokens to every session and 1,191 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 2026-08-30.
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