CC performing-false-positive-reduction-in-siem
Reduces SIEM false positives through systematic rule tuning, threshold adjustment, correlation logic refinement, allowlisting, and threat intelligence enrichment. Use when SOC analysts are overwhelmed by alert noise, when tuning noisy detection rules, or during a quarterly SIEM rule review to cut alert fatigue.
Reduces SIEM false positives through systematic rule tuning, threshold adjustment, correlation logic refinement, allowlisting, and threat intelligence…
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
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
-
low Risky intent
intent-offensive-securityassets/template.md:27Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- [ ] Test with Atomic Red Team: ___
-
low Risky intent
intent-offensive-securitySKILL.md:197Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- Run Atomic Red Team tests to verify true positives still trigger
-
low Risky intent
intent-offensive-securitySKILL.md:211Offensive-security / dual-use content (legitimate for authorised testing; review intended use)# Run Atomic Red Team test after tuning to confirm detection still works
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "domain" - note
frontmatter-keyunknown frontmatter key "subdomain" - note
frontmatter-keyunknown frontmatter key "d3fend_techniques" - note
frontmatter-keyunknown frontmatter key "nist_csf" - note
frontmatter-keyunknown frontmatter key "mitre_attack"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 30 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1675 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
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
- -43 reference files, but SKILL.md never points to them: the model will not open them
- -32 of 2 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 312: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (10 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.