n8n:linear-issue — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited n8n:linear-issue (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Start work on Linear issue $ARGUMENTS
This skill depends on external tools. Before proceeding, verify availability:
Required:
gh): Must be installed and authenticated. Run gh auth status to verify. Used to fetch linked PRs and issues.Optional (graceful degradation):
If a required tool is missing, stop and tell the user what needs to be set up before continuing.
Follow these steps to gather comprehensive context about the issue:
Use the Linear MCP tools available in the active harness to fetch the issue details and comments together:
Both calls should be made together in the same step to gather the complete context upfront.
After fetching the issue, immediately check its labels:
a. Run git remote -v (via Bash) to list all configured remotes. b. If any remote URL contains n8n-io/n8n without the -private suffix (i.e. matches the public repo), stop immediately and tell the user:
This issue is marked `n8n-private` and must be developed in a clean clone of the private repository.
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One or more of your remotes point to the publicn8n-io/n8nrepo. Mixed remotes are not allowed — you must work in a separate local clone ofn8n-io/n8n-privatewith no references to the public repo. For the full process, see: https://www.notion.so/n8n/Processing-critical-high-security-bugs-vulnerabilities-in-private-2f45b6e0c94f803da806f472111fb1a5
Do not continue with any further steps — return after showing this message.
n8n-io/n8n-private, continue normally.IMPORTANT: This step is NOT optional. You MUST scan and fetch all visual content from BOTH the issue description AND all comments.
Screenshots/Images (ALWAYS fetch):
<img> tagscurl -sL "url" -o /path/to/image.png (GitHub URLs require following redirects) OR the linear mcpRead tool on the downloaded file to view itLoom Videos (ALWAYS fetch transcript):
Related Linear Issues:
GitHub PRs and Issues:
gh CLI to fetch PR/issue details:gh pr view <number> for pull requestsgh issue view <number> for issuescurl -H "Authorization: token $(gh auth token)" -L <image-url> -o image.pngNotion Documents:
Comments were already fetched in Step 1. Review them for:
Determine whether this issue is specific to a particular n8n node (e.g. a trigger, action, or tool node). Look for clues in:
node:linear, node:slack)If the issue is node-specific:
Grep to search for the node's display name (or keywords from it) in packages/frontend/editor-ui/data/node-popularity.json to find the exact node type ID. For reference, common ID patterns are:n8n-nodes-base.<camelCaseName> (e.g. "HTTP Request" → n8n-nodes-base.httpRequest)n8n-nodes-base.<name>Trigger (e.g. "Gmail Trigger" → n8n-nodes-base.gmailTrigger)n8n-nodes-base.<name>Tool (e.g. "Google Sheets Tool" → n8n-nodes-base.googleSheetsTool)@n8n/n8n-nodes-langchain.<camelCaseName> (e.g. "OpenAI Chat Model" → @n8n/n8n-nodes-langchain.lmChatOpenAi)Primary: Check for Flaky's assessment in Linear comments. Flaky is an auto-triage agent that posts issue analysis as a comment. Search the comments already fetched in Step 1 for a comment from a user named "Flaky" (or containing "Flaky" in the author name) — do not re-fetch comments. If found, extract the popularity score and level directly from Flaky's analysis and use those values.
Fallback (if no Flaky comment exists): Look up the node's popularity score from packages/frontend/editor-ui/data/node-popularity.json. Use Grep to search for the node ID in that file. The popularity score is a log-scale value between 0 and 1. Use these thresholds to classify:
| Score | Level | Description | Examples |
|---|---|---|---|
| ≥ 0.8 | High | Core/widely-used nodes, top ~5% | HTTP Request (0.98), Google Sheets (0.95), Postgres (0.83), Gmail Trigger (0.80) |
| 0.4–0.8 | Medium | Regularly used integrations | Slack (0.78), GitHub (0.64), Jira (0.65), MongoDB (0.63) |
| < 0.4 | Low | Niche or rarely used nodes | Amqp (0.34), Wise (0.36), CraftMyPdf (0.33) |
Include the raw score and the level (high/medium/low) in the summary, and note whether it came from Flaky or the popularity file.
Primary: Check for Flaky's effort estimate in Linear comments. Search the comments already fetched in Step 1 for a Flaky comment — do not re-fetch. If found, extract the effort/complexity estimate directly from it and use that as your assessment.
Fallback (if no Flaky comment exists): After gathering all context, assess the effort required to fix/implement the issue. Use the following T-shirt sizes:
| Size | Approximate effort |
|---|---|
| XS | ≤ 1 hour |
| S | ≤ 1 day |
| M | 2-3 days |
| L | 3-5 days |
| XL | ≥ 6 days |
To make this assessment, consider:
Provide the T-shirt size along with a brief justification explaining the key factors that drove the estimate. Note whether it came from Flaky or your own assessment.
Before presenting, verify you have completed:
gh CLIAfter gathering all context, present a comprehensive summary including:
n8n-nodes-base.xxx), popularity score with level (e.g. 0.64 — medium popularity)AI-1975, node-1975, or just 1975 (will search)~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.