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 KaimingWan/oh-my-kiro --skill omk-self-reflectgit clone --depth 1 https://github.com/KaimingWan/oh-my-kiroWrote 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/kaimingwan/oh-my-kiro/omk-self-reflect)<a href="https://agentmods.dev/skills/kaimingwan/oh-my-kiro/omk-self-reflect"><img src="https://agentmods.dev/badge/skills/kaimingwan/oh-my-kiro/omk-self-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/kaimingwan/oh-my-kiro/omk-self-reflect"><img src="https://agentmods.dev/badge/skills/kaimingwan/oh-my-kiro/omk-self-reflect.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 | $0.00083 | $0.00912 |
| Opus 5.5 | $0.00033 | $0.00365 |
| Sonnet 5.5 | $0.00017 | $0.00182 |
| Haiku 4.5 | $0.00008 | $0.00091 |
Grade A, and why
omk-self-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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trigger Examples
- "记住，以后都要这样做"
- "always use jq for JSON, never sed"
- "@reflect 把这个教训沉淀一下"
- "I told you this before, capture it"
- "这个错误犯了好几次了，升级成规则"
Self-Reflect — Agent Self-Learning System
Scope (v3)
-
Promotion execution: When hook outputs 🔥 or ⬆️, read episodes.md, distill into 1-2 line rule, propose to user, write to rules.md if approved. Mark source episodes as
promoted. -
Complex insight capture: When hook outputs 🚨 (complex) and the correction is too complex for auto-capture (no simple DO/DON'T pattern), help user articulate and write to episodes.md via the same format.
NOT responsible for: daily capture (hook does it), dedup (hook does it), quality reporting (hook does it).
Sync Targets
| Scenario | Target |
|---|---|
| Top-level principle (user approved) | AGENTS.md / CLAUDE.md (Principles section) |
| Promotion (≥3 same pattern) | knowledge/rules.md (matching keyword section) |
| Complex insight | knowledge/episodes.md |
| Code-enforceable rule | .kiro/rules/enforcement.md |
Writing to Protected Files (AGENTS.md / CLAUDE.md / .claude/rules/)
When user explicitly approves writing to a protected instruction file, agent MUST autonomously execute the full three-step flow — do NOT wait for the human to do it manually:
touch .skip-instruction-guard- Write the change
rm -f .skip-instruction-guard
This bypasses the pre-write hook temporarily. Only do this when user has clearly approved the content in the current conversation.
Episode Format
DATE | STATUS | KEYWORDS | SUMMARY
- DATE: YYYY-MM-DD
- STATUS: active / resolved / promoted
- KEYWORDS: 1-3 english technical terms, ≥4 chars, comma-separated
- SUMMARY: ≤80 chars, no
|character, actionable DO/DON'T
Promotion Process
- Read episodes.md, find keywords appearing ≥3 times in active episodes
- Distill into 1-2 line rule with DO/DON'T + trigger scenario
- Read knowledge/rules.md section headers (
## [keywords]) - Clustering — choose target section by semantic match:
- Compare episode keywords with each section's keyword list
- Pick the section with most keyword overlap + semantic relevance
- If no section matches → create new
## [episode-keywords]section at end of file - If placing in existing section → append new keywords to section header if they add value
- Propose to user for approval (show target section)
- If approved: append rule to chosen section, change source episodes status to
promoted - Output: ⬆️ Promoted to rules.md [section]: 'RULE'
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 · 92 lines · 83 tokens per session scan A cff2d97672e1
omk-self-reflect is a skill published in the GitHub repository KaimingWan/oh-my-kiro (107 stars, last pushed 6mo ago), licensed MIT. It adds 83 tokens to every session and 912 once invoked, about $0.0003 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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