Routes ambiguous requests to the question actually being decided — and refuses to fake convergence when the evidence isn't there.
- Detects when the real deliverable is judgment rather than output, and switches into structured thinking without being asked.
- Separates what you said, what the evidence shows, what it infers, and what is still unknown — so you can disagree with a specific claim instead of a vibe.
- Keeps plausible alternatives with a test that would distinguish them; the number depends on the decision.
- When convergence is earned, gives one recommendation, its main trade-off, and why the alternatives lost; when it isn't, names the next learning step.
- Handles dictated, messy, mixed-language input — corrections and decisions arriving in one stream.
- "Help me think this through."
- "I'm torn between X and Y."
- "Am I even asking the right question?"
- "What am I missing here?"
- 「你帮我想想」
- 「我在纠结要不要…」
No magic words — it triggers on meaning. It stays out of the way for research, building, review, and status work, and applies only to the unresolved fork in mixed requests.
npx skills add m1nga/thinking-partner
Authored demonstration, not a real project diagnosis.
Input: “I maintain the sync module alone and need to avoid downtime. Should I rewrite it or patch it? I have no regression history to share. Diagnosis only.”
Possible output: “Start with a bounded diagnosis of the failing path. A patch is preferable if the defect can be isolated and checked; a rewrite becomes plausible if failures cross the module boundary and a safe cutover can be demonstrated. The regression record is missing, so neither path has earned a reliability claim. Inspect the recent failure and its direct dependencies before committing.”
The solo-maintainer and downtime constraints come from the input. No files change because the request is diagnosis only. If the same request omits those constraints, they remain unknown; the skill cannot borrow them from this example.
Recovery: If the user later confirms a local defect and authorizes a patch, continue into the patch and its checks rather than reopening the rewrite debate.
- grilling — this skill frames the decision; grilling interrogates the resulting build plan step-by-step.
- prompt-distill — once thinking converges, distill the outcome into a reusable prompt.
Written by a solo builder after real incidents where an assistant "found" a deeper need that wasn't there and the work went confidently sideways. That produced the two hard rules: accurate surface understanding before depth (an eloquent sentence is not evidence of an underlying need), and convergence must be earned (a recommendation without a distinguishing test is a guess in a suit).
Perspectives are selected, not enumerated — a viewpoint only enters when its disagreement would change the verdict, and the skill must say what that viewpoint has no authority to decide. Low-risk reversible defaults are decided by the agent; only identity, values, irreversible commitments, and material external consequences go back to the user. The skill deliberately does not stop work: once the fork is resolved, it continues into whatever was already authorized.
Historical simulation: 6 scenarios across 5 personas · 4 fired correctly · 2 correctly stayed quiet.
"这个项目我现在有两个方向嘛……呃不对,是先把底座锁死再切,你帮我想想,我有点纠结" → Fired. Untangled the dictated self-correction (later wording overrides earlier), kept both paths alive, and named the one test that would settle them — no fake convergence.
"帮我想想办法把这个 CSV 转成 JSON" → Stayed quiet. "想想" alone isn't a thinking request when the deliverable is a file — the execution exclusion held, and even on a misfire the body routes straight to "build it, no thinking prelude."
"方案我大概想好了,帮我想想还有没有漏洞,然后我们开工" → Deferred to grilling. This skill frames the decision; interrogating a committed build plan is explicitly someone else's seat.
Probe method: scenario-probe
An independent agent simulation checked a scoped usage scenario after the instruction cleanup. This checks instruction behavior, not human adoption or measured time savings.
Built by Ming. The design notes above explain the real problem and tradeoffs that shaped this skill.