BC forecast-premortem
Stress-tests predictions by assuming failure and working backward to identify blind spots, tail risks, and overconfidence. Applies Gary Klein's premortem technique to probabilistic forecasting. Use when confidence is high (>80% or <20%), need to identify tail risks and unknown unknowns, want to widen overconfident intervals, or when user mentions premortem, backcasting, what could go wrong, stress test, or black swans.
Stress-tests predictions by assuming failure and working backward to identify blind spots, tail risks, and overconfidence.
As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 3
✓ No critical or high findings
Medium and low: 3
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low Risky intent
intent-offensive-securityresources/premortem-principles.md:176Offensive-security / dual-use content (legitimate for authorised testing; review intended use)### Premortem vs Red Teaming
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low Risky intent
intent-offensive-securityresources/premortem-principles.md:178Offensive-security / dual-use content (legitimate for authorised testing; review intended use)**Red Teaming:**
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low Risky intent
intent-offensive-securityresources/premortem-principles.md:188Offensive-security / dual-use content (legitimate for authorised testing; review intended use)**Use both:** Red team attacks the plan, premortem explains the failure.
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 58/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 85Steps. 65 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3694 tokens
- low 10 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 422: enough signal without eating the budget
- +4Structure: 41 headings
- +3Step-by-step instructions: 65 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.