ethical-thinking
Skill20 starsChanged 28 days agoMITView on GitHub
Use for ethical thinking, or when a plan could harm or unfairly burden people: "should we do this?", "is this fair?", "privacy or bias risk", "who gets hurt?". Maps stakeholders, harms, power and consent. Skip for legal advice, neutral fact lookups and implementation-only work.
In this repository17 other files it gives its agents.
- AGENTS.mdAGENTS.md
- analytical-thinkingSkill
- creative-thinkingSkill
- critical-thinkingSkill
- design-thinkingSkill
- fermi-estimationSkill
- first-principles-thinkingSkill
- five-whysSkill
- lateral-thinkingSkill
- pre-mortemSkill
- prioritizationSkill
- six-thinking-hatsSkill
- strategic-thinkingSkill
- swot-analysisSkill
- systems-thinkingSkill
- thinking-method-selectorSkill
- tradeoff-analysisSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 26
Gather first: Read the design doc, data fields, policy text or code to learn who and what is actually affected before assuming.
- Distinctive: line 53
Do not demonize actors; focus on structures, incentives and foreseeable effects.
- Distinctive: line 54
If values truly clash, say so and recommend a process (deliberation, oversight) instead of fake unanimity.
- Distinctive: line 55
Never invent sensitive personal facts about real people.
- Distinctive: line 56
Not legal advice. Where law may bind, write "legal review needed" and do not predict legal outcomes.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Ethical Thinking
Ethics is about conflicts between legitimate goods. End with transparent tradeoffs, not false certainty.
When to use
- A product, policy or data decision that could affect people: privacy, fairness, consent, manipulation, safety.
- "Should we?" questions that go beyond "can we" or "is it legal".
- AI and data ethics reviews, stakeholder harm scans, moral review of a plan.
Skip: legal advice as such, neutral fact-finding with no values review requested, implementation-only work.
Before you start
- State in one block: Focal action (what is being considered) and Pass (Stakeholders, Values, Harms and Benefits, Justice and Power, Options, Recommendation).
- Gather first. Read the design doc, data fields, policy text or code to learn who and what is actually affected before assuming. Ask up to 3 questions only for affected parties or red lines that tools cannot answer.
- Light path. Small ask: 2-3 lines per lens, 2 options. For a pure harm scan you may compress Values, but still cover Justice and Power before Options.
Lenses
1. Stakeholders
Who is affected: direct, indirect, future, non-human where ecology matters. For vulnerable groups, describe dependence, cognitive load or marginalization in plain words and one sentence on why that raises caution. Justify from the context; never stereotype.
2. Values
Which values are in play (autonomy, beneficence, non-maleficence, justice, dignity, solidarity, others)? Name at least one value tension: A vs B, and why both matter here.
3. Harms and Benefits
Concrete harms and benefits. For each: how plausible, under what conditions, and how reversible. Separate predicted from observed when the user supplies history.
4. Justice and Power
Who carries the burdens and who gets the benefits? Who can say no, and who bears the cost of errors? Check procedural fairness: voice, consent, appeal.
5. Options
Two or more ethically distinct paths, including "do not proceed" when plausible:
Option: ... - Value fit: ... - Residual harm: ... - Safeguards: ...
6. Recommendation
A preferred option if the analysis supports one, or conditional guidance. Include the strongest reason against your recommendation and what to monitor if the plan proceeds.
Rules
- Do not demonize actors; focus on structures, incentives and foreseeable effects.
- If values truly clash, say so and recommend a process (deliberation, oversight) instead of fake unanimity.
- Never invent sensitive personal facts about real people; use only what the user gave.
- Not legal advice. Where law may bind, write "legal review needed" and do not predict legal outcomes.
Pitfalls
- A stakeholder list that stops at "users". Include the people who bear the cost without using the product.
- Harm stated with no plausibility or conditions, which reads as either alarmist or empty.
- A Recommendation with no dissent. If you cannot name the strongest objection, the analysis is unfinished.
- Safeguards that are only intentions ("we will be careful"). Name an owner or a mechanism.
Worked example: references/example.md.
Checklist
- Focal action and Pass stated
- Stakeholders include indirect or future parties when relevant
- At least one explicit value tension
- Harms and benefits give plausibility; options list safeguards
- Justice and Power covers distribution, voice and consent
- Recommendation names residual harm and the strongest dissent
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

