Use when a selective defect needs IS/IS-NOT difference analysis or a consequential option choice needs must/want weighting and adverse-consequence comparison.
SkillScore95Maintained
On-demandOn-demand: invoked explicitly — never occupies your agent's context until called.
◆Project Management pack
Last verified 8/14/2026
What it changes
Passed 21 of 22 test cases with the skill, and 16 of 22 without it.
Without skill73%
With skill95%
Checks it passed that the model alone missed
The response includes residual risks and next verification steps in the final output.
The response includes an IS/IS-NOT comparison matrix across WHAT, WHERE, WHEN, and EXTENT dimensions.
The response directly addresses certificate remediation without running a comparative diagnostic matrix.
and 5 more in the full report
5 fixed16 passed either way1 still failing0 broke
Pass-rate lift
+23 pp
Tokens
+13%
Agent turns
0%
Response time
-24%
Model gemini-3.6-flashMeasured 8/14/2026v121 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
After selecting PostgreSQL as the winning database candidate with a score of 88/100, what final output components must be provided along with the selection?
What was checked, without and with the skill
Checked
Without skill
With skill
The response includes residual risks and next verification steps in the final output.
Missed
Passed
case-20 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md38 linessingle file
Kepner-Tregoe Analysis
Core rule: Diagnose deviations by testing causes against both IS and IS-NOT. Compare consequential choices by screening MUSTs, weighting WANTs, and exposing adverse consequences before selecting.
When to Use
A defect affects some objects, places, times, or cohorts but not comparable others.
Several candidate causes remain and the contrast boundary can discriminate them.
A consequential option choice has explicit non-negotiables, competing objectives, and risks that should be compared consistently.
When NOT to Use
A uniform failure has no meaningful IS-NOT contrast, or the cause is already confirmed.
One cheap observation settles the cause or one option plainly dominates every requirement.
The criteria cannot be made operational; clarify them before assigning weights.
The task is forward failure discovery for a planned change rather than diagnosis or option selection.
Procedure
Choose the mode. Use Problem Analysis for a deviation from expected behavior; use Decision Analysis for a choice among options. State the target and do not mix scores with causal evidence.
Frame the target. For a deviation, record object, defect, location, time, extent, and impact. For a choice, state the decision, alternatives, constraints, and deadline.
Problem Analysis — build IS/IS-NOT. For WHAT, WHERE, WHEN, and EXTENT, record IS, closest comparable IS-NOT, and the distinction unique to the IS side. List changes near the first occurrence.
Problem Analysis — difference-test causes. Generate candidates from distinctions and changes. A candidate survives only if it explains both IS and IS-NOT. Run the cheapest discriminating check; stop when one verified cause explains the full boundary.
Decision Analysis — screen and score. Define pass/fail MUSTs and weighted WANTs (1–10 importance) before scoring. Eliminate options that fail any MUST; score survivors against each WANT and calculate weighted totals using the same scale.
Decision Analysis — test downside and sensitivity. For leading options, list adverse consequences with probability × impact and identify assumptions or weight changes that would reverse the ranking. Do not let a high total conceal a ruinous failure mode.
Decide or expose the gap. Return the verified cause or highest-ranked acceptable option, the evidence/score behind it, residual risk, and next verification. If no cause verifies or no option passes MUSTs, return open/none rather than force a winner.
Output
Return one mode-specific decision artifact:
Problem Analysis: problem statement; IS/IS-NOT matrix with distinctions; nearby changes; candidate-vs-boundary tests; confirmed cause or next discriminating check.
Decision Analysis: decision statement; alternatives; MUST screen; weighted WANT matrix; adverse-consequence table; sensitivity/reversal conditions; selected option or none.
Verification
Falsify/stop: reject a cause that cannot explain both sides of the boundary. Reject a choice if it fails a MUST, depends on inconsistent scoring, or loses under a plausible weight/risk change that was hidden.
Over-application guard: skip the full matrix for an obvious cause, trivial choice, or one-shot check. Stop when the cause verifies or the option is robust enough for the stated stakes; extra rows are ceremony.
When one mental model leaves a material blind spot on a multi-domain or high-stakes problem, sequence complementary models with named roles and a conflict rule.