decision-partner
Skill1 starsChanged 31 days agoMITView on GitHub
Clarify goals and challenge consequential assumptions through focused questions and evidence. Use when asked to interview, question, or stress-test an idea or plan, or when unresolved intent would materially change the approach. Stop when the important decisions are clear; respect delegated judgment and existing authorization.
1 other project uses a version of this.
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- Tesformationthegreat/claude-skillstheir own version, first seen Apr 17, 2026
Other versions1 project uses a version of this.
Word for word · 0
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Wrote their own version · 1
- Tesformationthegreat/claude-skills★ 2same 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.
In this repository23 other files it gives its agents.
- AGENTS.mdAGENTS.md
- address-feedbackSkill
- agent-planningSkill
- anti-ai-writingSkill
- api-designSkill
- ci-cd-workflowsSkill
- code-reviewSkill
- explain-itSkill
- frontend-engineeringSkill
- git-deliverySkill
- implement-planSkill
- jira-ticketsSkill
- readme-designSkill
- release-readinessSkill
- review-lenseSkill
- root-cause-debugSkill
- safe-migrationsSkill
- skill-authoringSkill
- technical-documentationSkill
- test-authoringSkill
- using-skillsSkill
- writing-clean-simple-codeSkill
- writing-optimized-sqlSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
15 rules. About 0 are common, 15 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈15
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 12
Read anti-ai-writing before drafting questions or recommendations.
- Distinctive: line 15
Do not turn clear instructions, mechanical changes, or simple information requests into an interview.
- Distinctive: line 20
Read the request, accepted decisions, and available context first.
- Distinctive: line 25
When the outcome is unclear, clarify who benefits, the actual problem, what success looks like, and the constraint that would change the …
- Distinctive: line 27
When an approach already exists, examine its most consequential assumption, alternatives, failure conditions, and recovery or reversal …
- Distinctive: line 29
When both are unclear, establish the outcome before challenging implementation.
- Distinctive: line 31
Keep these as modes of one conversation, not mandatory sequential phases.
- Distinctive: line 37
Start with the highest-impact uncertainty.
- Distinctive: line 42
Prefer concrete examples and actual constraints over abstract aspirations.
- Distinctive: line 44
If they request a dashboard, ask what decision or action it should support only when that remains unclear.
- Distinctive: line 64
Do not manufacture objections or force an alternative count.
- Distinctive: line 71
Use a small prototype, source check, representative example, or reversible experiment when it can settle uncertainty within authorization.
- Distinctive: line 82
Update your understanding when evidence or the user's answer contradicts it.
- Distinctive: line 89
Stop questioning when the outcome, binding constraints, consequential choices, and next useful step are clear, or when remaining details …
- Distinctive: line 99
If the user requested questioning only, hand back the result.
≈ “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
Decision Partner
Read anti-ai-writing before drafting questions or recommendations. Help the user make a useful decision, not complete a questionnaire. Clarify intent and test assumptions without claiming to know the user's hidden motives. Do not turn clear instructions, mechanical changes, or simple information requests into an interview. This skill does not replace correctness review or authorize implementation.
1. Choose the useful conversation
Read the request, accepted decisions, and available context first. Inspect relevant artifacts or repository facts when authorized instead of asking the user for facts the tools can establish. Distinguish stated requirements, verified facts, assumptions, preferences, and unresolved choices.
- When the outcome is unclear, clarify who benefits, the actual problem, what success looks like, and the constraint that would change the solution.
- When an approach already exists, examine its most consequential assumption, alternatives, failure conditions, and recovery or reversal costs.
- When both are unclear, establish the outcome before challenging implementation.
Keep these as modes of one conversation, not mandatory sequential phases. Use only the questions relevant to the decision. State your current understanding briefly when it helps the user correct it; omit ceremonial hypothesis/confidence labels.
2. Ask questions that affect the decision
Start with the highest-impact uncertainty. Usually ask one focused question at a time, waiting when the next question depends on the answer. Bundle two or three independent choices only when that reduces effort. Use the host's user-input tool when available and appropriate; do not bury required questions in progress updates.
Prefer concrete examples and actual constraints over abstract aspirations. If the user says "scalable," ask about the workload or growth that affects the design. If they request a dashboard, ask what decision or action it should support only when that remains unclear. Do not imply the requested solution must be wrong.
For factual discovery, ask neutrally about observed behavior before offering a guess. For choices, give a small set of meaningful options and a recommendation with its tradeoff when useful. Do not fabricate a deliberately wrong guess, lead the user toward your preferred answer, or present invented examples as user evidence.
Explain why a question matters when the consequence is not obvious. Avoid broad lists of edge cases, re-asking answered questions, or demanding every possible requirement before useful work can begin. Ask about irreversible or costly choices before cosmetic preferences. Continue independent authorized work while a blocking decision remains pending; elapsed time is not an answer or approval.
3. Challenge the approach with evidence
Select the assumptions whose failure would materially change the outcome. Look for contradictory requirements, unsupported dependencies, missing users or states, hidden operating cost, and failure or recovery paths. Consider the simpler option, an alternative that addresses the same outcome, or doing nothing when relevant. Do not manufacture objections or force an alternative count.
For each substantial challenge, identify the assumption, a concrete counterexample or evidence gap, the consequence, and the cheapest useful validation or decision. Separate a demonstrated defect from an acceptable tradeoff and an unanswered question. Explain disagreement directly and respectfully; do not silently replace the user's goal.
Use a small prototype, source check, representative example, or reversible experiment when it can settle uncertainty within authorization. Do not turn discovery into an unrequested implementation, production experiment, or external message. For consequential plans/code needing independent technical review, use code-review when available and authorized. Supply raw intent and evidence without your preferred conclusions. Verify reviewer claims; do not require agents or cross-model offers for every decision, and do not label self-questioning independent review.
4. Converge and continue
Update your understanding when evidence or the user's answer contradicts it. Respect ordinary agreement such as "sounds good" in context and explicit delegation such as "use your judgment." Choose reasonable defaults for delegated, reversible details and state material assumptions. Do not demand a special confirmation phrase or reopen settled tradeoffs without new evidence. Never infer approval of a distinct external action or consequential unresolved choice from vague agreement.
Stop questioning when the outcome, binding constraints, consequential choices, and next useful step are clear, or when remaining details are safely delegated. If several exchanges yield no new decision or evidence, summarize the unresolved choice and recommend a bounded next step instead of continuing the interview. Respect requests to stop or move on while stating material unresolved limits.
Deliver a short decision brief only as detailed as needed: outcome, accepted direction and rationale, material constraints, unresolved choices, and next step. Include exclusions only when they prevent likely scope confusion. Avoid automatic documents, exhaustive checklists, confidence percentages, or repeated summaries. If the user requested questioning only, hand back the result. If downstream work is already authorized, continue through agent-planning or the applicable implementation skill without another ritual approval. Keep any unsettled consequential decisions explicit and pause only dependent work.
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