SKILLEMALL.ai

AB premortem

Activate when: user says 'let's check what could go wrong before we commit', 'I want to stress-test this plan', 'we're about to launch and I'm worried we're missing something', 'premortem', or is about to make a hard-to-reverse decision with a team that has converged on one plan. Do NOT activate when: the decision is small and easily reversible (overhead exceeds value); the situation is time-critical and analysis would prevent timely response. More: deciqai.com/c/premortem

ClawHub Agent Skills author: deciqAI v1.0.4 MIT-0 5 files body ≈ 1 764 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
95
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security examples/klein-2007-mitchell-russo-pennington-1989-foundation.md:39
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (test fixture / example file; quoted — discussed, not commanded)
      **Military planning.** U.S. Army and U.K. Defence Forces include premortem-style exercises in operations planning. The Army's "Red Team University" trains officers in structured devil's-advocacy and p
      fixturequoted

    Files scanned: 5. 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 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 24 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1764 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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 477: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 24 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: clean
    This skill only teaches an agent to facilitate a premortem planning exercise and does not request sensitive access, persistence, or code execution.
    LLM: benign (high) · VirusTotal: · 16 Jul 2026