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 agentmods add skills/hajekim/agentic-design-patterns-extension/reflectionnpx skills add hajekim/agentic-design-patterns-extension --skill reflectiongit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionWrote 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/hajekim/agentic-design-patterns-extension/reflection)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/reflection"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/reflection.svg" alt="Measured on agentmods" 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 | $0.00379 | $0.03115 |
| Opus 5 | $0.00189 | $0.01558 |
| Sonnet 5 | $0.00076 | $0.00623 |
| Haiku 4.5 | $0.00038 | $0.00312 |
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 4d 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.
This is a copy
100% identical to reflection — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflection Pattern
Overview
The Reflection Pattern enables an agent to critically evaluate its own outputs and iteratively improve them through self-assessment loops. Rather than accepting the first response as final, the agent acts as its own critic — identifying flaws, gaps, and inaccuracies — then generates a refined output based on that critique.
Core Principle: Generate → Critique → Refine — treat the first output as a draft, not a final answer.
When This Skill Applies
Activate this pattern when:
- Output quality must meet a high standard before delivery
- The task involves complex reasoning where first attempts are often suboptimal
- You need an agent to self-correct factual errors or logical inconsistencies
- A separate "critic" role would improve the overall quality of the response
- Tasks require iterative refinement (code writing, essay drafting, plan generation)
- The cost of external human review is high and automated quality checks are preferable
Rule of thumb: If the task is complex enough that a human expert would draft, review, and revise — apply the Reflection pattern.
DEFINE → PLAN → ACTION Workflow
DEFINE
Identify the reflection requirements:
- What is the quality bar for the final output?
- What specific dimensions should be critiqued (accuracy, completeness, clarity, safety)?
- How many refinement iterations are appropriate before accepting output?
- What constitutes a "good enough" output to exit the loop?
PLAN
Design the reflection architecture:
- Define the Generator: initial response producer with a focused prompt
- Define the Critic: evaluator that identifies specific weaknesses with explanations
- Define the Refiner: takes original + critique and produces improved output
- Set convergence criteria: max iterations, quality threshold, or "no more critique" signal
- Plan logging of each iteration for debugging and audit
ACTION
Implement the reflection loop:
- Generate initial output with the Generator prompt
- Apply the Critic prompt to evaluate the output on defined dimensions
- If critique finds issues, pass original + critique to the Refiner
- Repeat until convergence criteria met
- Return final refined output
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.
- 4d ago First seen · 340 lines · 379 tokens per session scan A d9b893f2bed7
reflection is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 5mo ago), licensed MIT. It adds 379 tokens to every session and 3,115 once invoked, about $0.0019 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to reflection, differing in 3 lines, and is treated as a copy.
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