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capability to parse Simplicité logs from a raw `.txt` file, filter fields to reduce noise, and output the result as structured JSON.
Based on SOC occupation classification
Expert on the history, origin, and correct use of the em dash. Use when writing or reviewing code, comments, or data files to avoid em and en dashes, defaulting to never using them and replacing any found with a hyphen (-). Includes strong knowledge of punctuation marks and the proper usage of punctuation characters when writing comments.
Use when an outage, production incident, or significant service degradation has occurred and the team needs to write a structured blameless post-mortem. Triggers on phrases like "write a post-mortem", "incident review", "what went wrong", "outage report", "root cause analysis", or "RCA". Covers timeline reconstruction, contributing factor analysis, impact quantification, and action item generation with owners.
Create, set up, or update the personal context portfolio: structured markdown files describing who you are, how you work, your teams, and your tool/ADO configuration. Runs the interview workflow for first-time setup and targeted edits for updates. Trigger this skill when the user asks to: set up their context, create or update their context portfolio, "create my IQ", "set up my IQ", edit their profile, add/remove a stakeholder, update ADO config, change team info, update pillars, or set up any plugin configuration. Trigger when another skill fails to find context (missing files or TODO markers) and needs context populated. Also trigger when the user mentions a context change in passing (e.g., "my manager changed", "we added someone to the team") to offer a context file update. Do NOT trigger for read-only questions like "who's on my team?" or "what's my ADO config?". Those are answered directly from the context files referenced in the loaded custom instructions; no skill is needed.
Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.
Security hardening reviewer for GitHub Actions workflow files (.github/workflows/*.yml). Reasons about the Actions threat model that pattern matchers and general code linters miss — untrusted-input script injection, privileged triggers running fork code, mutable action references, and over-scoped tokens. Use this skill when asked to review, audit, harden, or secure a GitHub Actions workflow, when writing a new workflow, or for any request like "is this workflow safe?", "review my CI for security issues", "why is pull_request_target dangerous here?", "pin my actions", or "lock down GITHUB_TOKEN permissions". Covers script injection via ${{ }} interpolation, pull_request_target / workflow_run privilege escalation, SHA-pinning of third-party actions, least-privilege permissions, GITHUB_ENV/GITHUB_OUTPUT injection, secret exposure, OIDC over long-lived credentials, and self-hosted runner exposure on public repositories.
Build GitHub Copilot workflows with Xquik X API SDKs, REST endpoints, MCP tools, signed webhooks, tweet search, user lookup, follower exports, media actions, and agent automation.
| name | optimize-simplicite-logs |
| description | capability to parse Simplicité logs from a raw `.txt` file, filter fields to reduce noise, and output the result as structured JSON. |
This skill provides the capability to parse Simplicité logs from a raw .txt file, filter fields to reduce noise, and output the result as structured JSON. This is critical for optimizing AI context size (saving ~56% of tokens) and providing structured, predictable data for troubleshooting.
Use this skill when you need to:
.txt format.timestamp, level, body) from verbose multi-line log output.IMPORTANT: Instead of directly reading a raw .txt log file provided by the user using file read tools, you must use one of the log converter scripts (PowerShell or Python) to parse the file into a JSON format first, optionally extracting only the fields needed.
/scripts/SimpliciteLog2Json.ps1) or the Python script (/scripts/simplicite-log2json.py).Reduces the tokens consumed by large Simplicité logs by extracting only relevant log fields (e.g. body, timestamp, level) and discarding non-relevant structural log data (like app, endpoint, contextPath).
Properly captures stack traces and multiline errors inside the body field of the JSON structure, which a simple text search might miss.
If no output path is provided for the JSON file (e.g. omitting --output or -Output), the parsed JSON will be printed directly to stdout, allowing you to pipe the output to other tools.
After processing, the tool prints a summary to stderr (or console):
Processed: 123 entries, Skipped: 2 entries
Convert a log file to JSON, keeping only the most important fields:
python /absolute/path/to/skills/optimize-simplicite-logs/scripts/simplicite-log2json.py <input.txt> --include timestamp,level,body --output <output.json>
/python /absolute/path/to/skills/optimize-simplicite-logs/scripts/SimpliciteLog2Json.ps1 -InputPath "<input.txt>" -Output "<output.json>" -Include "body,timestamp,level"
After generating the <output.json>, you can safely read the resulting file to perform your analysis.
.txt log files from Simplicité using standard text reading tools. Always convert them to JSON using the available scripts.--include (Python) or -Include (PowerShell) to restrict fields to what is absolutely necessary to diagnose the issue (usually timestamp,level,body).timestamp, app, level, endpoint, contextPath, event, user, class, function, rowId, body.# 1. Run the script to generate a minified JSON output in the current directory
python /absolute/path/to/skills/optimize-simplicite-logs/scripts/simplicite-log2json.py logs.txt --include timestamp,level,body --output logs_minified.json
# 2. Then read logs_minified.json to understand the context.