scientific-method: Скилл для структурированного решения задач

--- name: scientific-method description: "Apply the scientific method to any problem — define goal, hypothesise, experiment, measure, iterate. The meta-skill governing all structured investigation. Use when debugging, validating assumptions, or turning uncertainty into knowledge." argument-hint: "[problem to investigate or goal to achieve]" user-invocable: true allowed-tools: Read, Grep, Glob, Bash --- Apply the scientific method to $ARGUMENTS. This is the universal cycle for turning uncertainty into knowledge. ``` GOAL → OBSERVE → HYPOTHESISE → EXPERIMENT → MEASURE → ANALYSE → ITERATE ``` ## Step 1: Define the goal (mandatory) What does success look like? Without clear success criteria, you cannot judge results. ```markdown ### Goal definition **Goal:** [specific, measurable outcome] **Current state:** [where things are now — with numbers if possible] **Target state:** [where they need to be — with numbers] **How to measure:** [the specific metric or observation that proves success] ``` **Rules for goals:** - Bad: "Make it faster" — unmeasurable - Good: "Reduce p95 latency from 800ms to under 200ms on the /api/search endpoint" - Every goal must have a number or a binary pass/fail condition **Output:** Goal definition with current state, target state, and measurement method. ## Step 2: Observe (mandatory) Gather data about the current state BEFORE forming hypotheses. Observation without hypothesis prevents confirmation bias. ```markdown ### Observations | # | Observation | Source | Surprising? | |---|---|---|---| | 1 | [what you actually see — not what you expect] | [where you found it] | Yes/No | | 2 | [observation] | [source] | [surprise?] | **What has been tried before:** [previous attempts and their outcomes] **What measurements exist:** [available data, logs, metrics] **What's missing:** [data you wish you had but don't] ``` **Rules for observation:** - Record what you SEE, not what you EXPECT. The gap between these is where discoveries live - Surprising observations are the most valuable — flag them explicitly - "I don't know" is a valid and important observation **Output:** Observation table with sources and surprise flags. ## Step 3: Hypothesise (mandatory) Generate MULTIPLE hypotheses, not just one. A single hypothesis is confirmation bias waiting to happen. ```markdown ### Hypotheses | # | Hypothesis | If true, expect to see | If false, expect to see | Likelihood | |---|---|---|---|---| | H1 | [specific claim about what's happening and why] | [predicted observation] | [predicted observation] | High/Medium/Low | | H2 | [alternative explanation] | [prediction] | [prediction] | [likelihood] | | H3 | [another possibility] | [prediction] | [prediction] | [likelihood] | ``` **Rules for hypotheses:** - Minimum 3 hypotheses. If you can only think of one, you don't understand the problem yet - Every hypothesis must be falsifiable — if no observation could disprove it, it's not a hypothesis - The "if false" column is the most important — it tells you what would change your mind - Rank by likelihood but test the most likely first, not the most interesting **Output:** Hypothesis table with predictions and falsification criteria. ## Step 4: Design experiment (mandatory) For the highest-likelihood hypothesis, design the smallest test that would confirm or refute it: ```markdown ### Experiment design **Testing hypothesis:** H[N] **Variable (what changes):** [the one thing you're changing] **Control (what stays the same):** [everything else] **Measurement:** [how you'll know the result] **Expected result if hypothesis is correct:** [specific outcome] **Expected result if hypothesis is wrong:** [specific outcome] **Time budget:** [maximum time before concluding] ``` **Rules for experiments:** - Change ONE variable at a time. Changing multiple things makes results uninterpretable - Define the expected result BEFORE running the experiment. Post-hoc rationalisation is not science - Set a time budget. An experiment that runs indefinitely is not an experiment **Output:** Experiment design with single variable, control, and predictions. ## Step 5: Measure (mandatory) Run the experiment and record results: ```markdown ### Results **What happened:** [factual description of outcome] **Expected outcome matched:** Yes / No / Partially **Quantitative result:** [numbers if applicable] **Unexpected observations:** [anything you didn't predict] ``` **Rules for measurement:** - Record what ACTUALLY happened, not what you hoped for - Unexpected observations are often more valuable than the expected result — do not discard them - If the result is ambiguous, the experiment needs to be more precise, not re-interpreted **Output:** Results with comparison to predictions. ## Step 6: Analyse (mandatory) Compare results to the goal: ```markdown ### Analysis **Hypothesis H[N] status:** Confirmed / Refuted / Inconclusive **Distance from goal:** [how far current state is from target state] **What we learned:** [new knowledge gained — the actual value of this cycle] **What we still don't know:** [remaining uncertainty] ``` **Output:** Hypothesis verdict and remaining uncertainty. ## Step 7: Iterate (mandatory) Based on analysis, decide next action: - **Goal met** → Document what worked, why, and what would break it. Done. - **Goal not met, hypothesis confirmed** → The fix didn't work despite correct diagnosis. Revise the approach, return to Step 3 with new hypotheses. - **Goal not met, hypothesis refuted** → Move to next hypothesis. Return to Step 4. - **Stuck** → Challenge assumptions with `/first-principles`. The problem framing may be wrong. **Output:** Decision on next action with reasoning. ## Quick Diagnosis Mode For debugging (the 15-minute rule): 1. What exactly is the symptom? (Step 2 — observe) 2. What are 3 possible causes? (Step 3 — hypothesise) 3. Which is most likely? Test that ONE thing (Steps 4–5) 4. Did it fix it? (Step 6 — analyse) 5. If not, next hypothesis. Don't change multiple things at once (Step 7 — iterate) ## Rules - **Multiple hypotheses are mandatory.** A single hypothesis is not the scientific method — it's guessing with extra steps. Minimum 3. - **Change one variable at a time.** Changing two things simultaneously makes the result uninterpretable. If you're tempted to change multiple things, run multiple experiments. - **Record before interpreting.** Write down what you observe before forming an explanation. Observation and interpretation are separate steps. - **Falsification over confirmation.** Seek evidence that DISPROVES your hypothesis, not evidence that confirms it. Confirmation bias is the #1 enemy of investigation. - **Time-box everything.** An experiment without a time budget becomes an infinite loop. If you haven't learned something in the allotted time, the experiment design is wrong. - **"I don't know" is progress.** Eliminating a hypothesis is as valuable as confirming one. Three refuted hypotheses is not failure — it's three things you've ruled out. ## Output Format ```markdown ## Investigation: [problem] ### Goal [Goal definition from Step 1] ### Observations [Observation table from Step 2] ### Hypotheses [Hypothesis table from Step 3] ### Experiment [Design from Step 4] ### Results [Measurements from Step 5] ### Analysis [Verdict from Step 6] ### Next Action [Decision from Step 7] ``` ## Related Skills - `/first-principles` — when the scientific method reveals that the problem framing itself is wrong. Decompose and rebuild. - `/algorithm` — for systematic execution once you know WHAT to do. Scientific method figures out what's true; algorithm executes the plan. - `/learning` — capture experimental results as learnings for future reference.