CB codeql-local-triage
Answers "why did CodeQL flag this, and which line must change for it to stop" — reproducible causal triage of ONE specific alert via variant bisection. Triggers on "确认 taint 源 / 复现这个 CodeQL 告警 / 为什么 CodeQL 报这个 / CodeQL 误报 / 这个告警是不是误报 / 本地跑 CodeQL / 验证安全告警是否修好 / confirm the taint source / why does CodeQL flag this / is this alert a false positive / reproduce this CodeQL alert / verify a security-alert fix", or when handed a .sarif file and asked where the taint starts. NOT for whole-repo alert audits or bulk dismissal — it never closes alerts for you and never replaces the Code Scanning suite. NOT for Java/JS/Go (Python only today). The prefilter lists candidate sensitive NAMES in files/dirs you point at; it draws no conclusion and produces no audit report. Keywords — CodeQL, Code Scanning, taint source, data flow, SARIF, codeFlows, false positive, py/clear-text-storage-sensitive-data, CWE-312.
Answers "why did CodeQL flag this, and which line must change for it to stop" — reproducible causal triage of ONE specific alert via variant bisection.
As a process B 74/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Read Grep Glob Bash
Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "permissions"
Process rating: all ten parameters 74/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 40Result and completion. Does not say what the result is
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 33 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3566 tokens
- 100Running it twice. Mutating operations check current state
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (5 tags): a typed call is more reliable
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
- +3Description length 906: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- -31 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +4Structure: 16 headings
- +3Step-by-step instructions: 33 items
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.