MIRROR
Skill1 starsChanged 8 months agoMITView on GitHub
Force counterarguments to your own recommendations. Use when: you've just made a recommendation and want to stress-test it, before committing to an approach, when you suspect confirmation bias, or any time "what am I missing?" needs answering. A cognitive debiasing tool that challenges your own thinking.
We split this skill into the rules it gives an agent
9 rules. 0 are common, 8 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
8
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 54
When you invoke /mirror:
- Distinctive: line 75
When this skill is invoked:
- Distinctive: line 82
If the recommendation holds up → proceed with confidence
- Distinctive: line 83
If concerns were found → flag them before continuing
- Distinctive: line 119
No external packages or services required
- Distinctive: line 125
After making any significant technical recommendation
- Distinctive: line 126
Before committing to an architecture or framework choice
- Distinctive: line 127
When you catch yourself being too certain
- Distinctive: line 128
After a CONCLAVE debate, to stress-test the conclusion
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
MIRROR
Challenge your own recommendations. Find the blind spots.
Usage
/mirror - Challenge the last recommendation made
/mirror [topic] - Challenge thinking on a specific topic
/mirror reflect "statement" - Examine a specific statement for weaknessesWhat This Is
MIRROR is a self-check tool. After making a recommendation, invoke MIRROR to force genuine counterarguments against your own position. It selects from 8 challenge frameworks and applies the most relevant ones to your current context.
This isn't devil's advocacy for fun — it's a structured way to catch blind spots before they become mistakes.
The 8 Challenge Frameworks
| Framework | Question It Asks |
| Devil's Advocate | What are the strongest arguments AGAINST this approach? |
| Alternative Paths | What completely different approaches were not considered? |
| Hidden Costs | What are the non-obvious downsides or maintenance burdens? |
| Failure Modes | How could this fail? What's the worst-case scenario? |
| Premature Optimization | Is this solving a problem that doesn't exist yet? |
| Reversibility | How hard is it to undo this decision? What are we locking in? |
| Second Order Effects | What will this cause downstream? Unintended consequences? |
| Expertise Blind Spots | What would someone with a different background see? |
How It Works
Basic Mirror
When you invoke /mirror:
- Select 3 challenge frameworks (contextually relevant or random)
- Apply each framework to the most recent recommendation
- Present the challenges clearly
- Reflect honestly — if the challenges reveal genuine concerns, voice them
Targeted Reflect
When you invoke /mirror reflect "statement":
- Analyze keywords in the statement
- Select the most relevant frameworks:
- "best/optimal/should" → Alternative Paths
- "simple/quick/easy" → Hidden Costs
- "future/scalable" → Premature Optimization
- "never/always/definitely" → Devil's Advocate
- Apply selected frameworks
- Present counterarguments
AUTO-EXECUTE Protocol
When this skill is invoked:
- Identify the target — what recommendation or statement is being challenged
- Select 3 frameworks — contextually relevant or semi-random
- Apply each framework genuinely — don't softball the challenges
- Present the challenges with clear formatting
- Conclude honestly:
- If the recommendation holds up → proceed with confidence
- If concerns were found → flag them before continuing
Output Format
══════════════════════════════════════════════════════
MIRROR — Counterargument Mode
══════════════════════════════════════════════════════
Challenging: [recommendation or statement]
[1] HIDDEN COSTS
What are the non-obvious downsides or maintenance burdens?
[2] ALTERNATIVE PATHS
What completely different approaches were not considered?
[3] REVERSIBILITY
How hard is it to undo this decision? What are we locking in?
──────────────────────────────────────────────────────
Reflect on these angles before proceeding.
If concerns are genuine, raise them.
If the recommendation holds, proceed with confidence.
══════════════════════════════════════════════════════Prerequisites
- Claude Code (any model)
- No external packages or services required
When to Use
- After making any significant technical recommendation
- Before committing to an architecture or framework choice
- When you catch yourself being too certain
- After a CONCLAVE debate, to stress-test the conclusion
- Any time "what am I missing?" is a useful question
The mirror doesn't lie. Use it before you ship.
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- team-attention/hoyeon★ 173same name
- nanxiaoyao/network-huawei-skills★ 2same name
- rezzminator/buddy★ 1same name
- stevemarksava/superteam★ 1same name
- yiyaw-lab/agent-armor★ 1same name
- Citability/claude-code-adhd-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
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Now do this for your own.
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