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 rjmurillo/ai-agents --skill reflectgit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/reflect)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/reflect"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/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/rjmurillo/ai-agents/reflect"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/reflect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00095 | $0.01421 |
| Opus 5.5 | $0.00038 | $0.00568 |
| Sonnet 5.5 | $0.00019 | $0.00284 |
| Haiku 4.5 | $0.00010 | $0.00142 |
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 9d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect Skill
Critical learning capture system that prevents repeating mistakes and preserves successful patterns across sessions.
Analyze the current conversation and propose improvements to skill-based memories based on what worked, what didn't, and edge cases discovered. Every correction is a learning opportunity - invoke proactively to build institutional knowledge.
Triggers
| Trigger Phrase | Operation |
|---|---|
reflect on this session |
Extract learnings from conversation |
learn from this mistake |
Capture correction patterns |
capture what we learned |
Document session insights |
improve skill {name} |
Target specific skill memory |
what did we learn |
Review and store patterns |
Three priority tiers drive invocation timing: HIGH (corrections and immediate fixes, invoke immediately), MEDIUM (praise, preferences, edge cases, and questions, invoke when the table threshold is met), LOW (repeated patterns and session-end consolidation). Invoke proactively, not only on explicit request.
See references/triggers.md for the full priority-tiered trigger tables, the original trigger phrases, and the proactive-invocation reminder.
When to Use
Use this skill when:
- User corrects your output ("no", "wrong", "not like that")
- User praises specific output ("perfect", "exactly")
- Edge cases are discovered during work
- Session end after skill-heavy work
- Want to capture learnings before they are lost
Use retrospective instead when:
- Conducting a full session retrospective (broader scope)
- Analyzing multi-session patterns across the project
Process
Phase 1: Identify the Target Skill
Locate the skill-based memory to update:
- Check Serena memories: Look for files ending with
-observations.mdin.serena/memories/ - Infer from context: Identify which skill(s) were used in the conversation
- Create if needed: If missing, propose
{skill-name}-observations.md(skill observations pattern)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago Changed 590d63ec124c
- 20d ago Changed · +12 lines 45686415e239
- 22d ago Changed · +5 lines 8d96011da3e6
- 1mo ago Changed 45a7cd6b49a1
- 1mo ago Changed · -1 lines d992638e08e4
- 1mo ago First seen · 140 lines · 95 tokens per session scan A 645403addd01
reflect is a skill published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 1,421 once invoked, about $0.0004 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 2026-09-03.
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