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Digs beneath logic to inspect unchecked premises, rating each assumption by collapse severity.
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Based on SOC occupation classification
Designs API contracts for consumer experience first — making correct usage obvious and incorrect usage impossible.
Replaces adjectives with evidence, claims with demonstrations, and promises with proof for conversion-oriented writing.
Forces the model to build a genuine prosecution case against its own answer before delivering it.
Detects and corrects objective drift during extended tasks by periodically checking work against the original request.
Decides whether to ask clarifying questions or proceed with an answer, optimizing for information value vs. delay cost.
Self-selects and sequences the right depth-skills proportional to task consequence, preventing both under- and over-analysis.
| name | excavate |
| codename | EXCAVATE |
| internal | Assumption Archaeology |
| version | 1.1 |
| tier | excavation |
| trigger | ["what am I assuming","is this safe","high-stakes plan","any answer that feels solid but hasn't been tested at its roots"] |
| description | Digs beneath logic to inspect unchecked premises, rating each assumption by collapse severity. |
| author | Kshitijpalsinghtomar |
| tags | ["assumptions","foundations","premises","risk","validation"] |
| artifacts | ["assumption-list","deep-layer-questions","collapse-ratings","resolution-log"] |
| composable_with | ["invert","negative-space","threshold","adversary"] |
Every answer stands on ground you haven't inspected. The logic is flawless. The conclusion follows from the premises. No one checked the premises.
"Valid" means "correct IF the premises are true." You never verified that they are. The premises were absorbed — from the problem description, from pattern matching, from your model of the user's situation. Each is a silent load-bearing wall. Remove one, and everything above it collapses.
This skill digs beneath the logic to inspect the ground.
You are about to deliver an answer that:
Surface assumptions are not the threat. The threat is the assumptions you don't know you're making.
List everything this answer takes as given. Write each one as a clear, falsifiable statement.
Categories to scan — write at least two per category:
Input assumptions (about the data, system, and context):
Pattern assumptions (inherited from your pattern match):
Scope assumptions (what's in and out — who decided?):
User assumptions (about what they know, want, and have):
Absence assumptions (what you assume is NOT present):
Minimum ten. Fewer than ten means you stopped at the surface. The dangerous assumptions are always below the comfortable ones.
Artifact: Numbered assumption list. Step 2 and 3 reference these by number.
These are harder. They challenge the framing of the problem itself, not just the solution.
Write your honest answer to each:
DEEP LAYER QUESTIONS
────────────────────────────────────────
Q1: Am I solving the right problem, or is the user describing
a symptom of a different problem?
My answer: [write it — don't skip]
Q2: Which of my assumptions from Step 1 did I
add from pattern matching, not from the actual problem statement?
Candidates: [list assumption numbers from Step 1]
Q3: What context am I filling in that the user
never actually stated? Write the specifics.
I'm assuming: [list specific filled-in context]
Q4: What factor might exist that wasn't mentioned in the problem
and would change my entire approach?
Candidate factors: [list at least two]
Q5: If I showed my assumption list (Step 1) to the user and they
said "actually, #[N] is wrong" — which number would
most surprise me and most damage my answer?
Most surprising wrong: Assumption #[N] — because [why]
────────────────────────────────────────
Artifact: Five answered deep questions. These surface the unconscious assumptions the surface layer misses.
For each assumption from Steps 1 and 2, assign a collapse rating:
COLLAPSE RATINGS
────────────────────────────────────────
C0 — Cosmetic: If wrong, answer needs minor rewording. Core survives.
C1 — Structural: If wrong, significant parts must change. Core might survive.
C2 — Foundation: If wrong, entire answer must be rebuilt from scratch.
C3 — Ontological: If wrong, the PROBLEM was wrong. Wrong category entirely.
────────────────────────────────────────
Assumption #1: [statement] — C[rating]
Assumption #2: [statement] — C[rating]
...
Assumption #N: [statement] — C[rating]
C2/C3 assumptions requiring resolution:
#[N]: [statement]
#[N]: [statement]
Artifact: Full assumption list with collapse ratings. Step 4 processes every C2 and C3.
For each C2/C3 assumption, choose one resolution strategy and execute it:
Verify — Find evidence it's true. Not "probably true." Actual evidence: a documented fact, a stated requirement, a known constraint. Write the evidence.
Flag — Cannot verify? Write this sentence in your output: "This answer assumes [X]. If [X] is false, the answer changes to [Y]." The user now knows the dependency.
Design around — Cannot verify AND highly critical? Redesign the answer so it does not depend on this assumption. Most expensive strategy, most robust result.
RESOLUTION LOG
────────────────────────────────────────
Assumption #[N] (C2): [statement]
Strategy: [verify / flag / design-around]
Result: [evidence found / flagged in output / redesigned to avoid]
Assumption #[N] (C3): [statement]
Strategy: [verify / flag / design-around]
Result: [evidence found / flagged in output / redesigned to avoid]
────────────────────────────────────────
Artifact: The resolution log. This proves every critical assumption was explicitly addressed — not left to hope.
Most wrong answers are not wrong in their logic. They are wrong in their foundations — flawless reasoning built on unchecked premises. The model assembles correct chains of reasoning on top of assumptions it absorbed unconsciously during pattern matching. The logic looks right. The conclusion is valid. And it fails in production because premise #3 was never true. This skill forces the premises into the open where they can be inspected, rated, and addressed before the answer ships.