Incident Postmortem Assistant
v1.0.0将事故线索整理成复盘草案，区分根因、诱因、放大器、影响与修复动作。；use for incident, postmortem, sre workflows；do not use for 归责个人, 篡改时间线.
⭐ 0· 98·0 current·0 all-time
byvx：17605205782@52yuanchangxing
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (postmortem assistant) align with the included resources and the single Python script. Requiring only python3 is proportional: the script produces structured Markdown reports and audits directories/files which fits incident analysis and repo-level reviews.
Instruction Scope
SKILL.md explicitly permits running the bundled script when shell/exec is available and also says to produce output from bundled template if execution is not possible. The script accepts a path or directory and will read and sample many text file types (md, json, py, sh, csv, etc.). Reading local incident logs and repo files is expected, but running the script with a path that points broadly (e.g., / or home) will cause it to read many local files — be deliberate about what path you pass and prefer dry-run or explicit files.
Install Mechanism
No install spec; instruction-only plus a local Python script. No external downloads or package installs are present.
Credentials
No environment variables or credentials required. The script only uses local file I/O and standard-library parsing; this matches the described purpose.
Persistence & Privilege
always is false and the skill does not request elevated or persistent platform privileges. The script can write an output file (normal); it does not modify other skills or system-wide agent settings.
Assessment
This skill appears to do what it says: it formats incident inputs into a structured postmortem and can optionally run a local Python script to audit files or directories. Before running the script, review scripts/run.py (it is included and readable) and: 1) pass only intended files or directories (avoid pointing it at your whole filesystem or sensitive folders); 2) use --dry-run first or run it against example inputs; 3) sanitize sensitive data before feeding it in; and 4) if you plan to let the agent run the script autonomously, be aware the agent could read any paths you direct it to — restrict inputs and review outputs before any external sharing.Like a lobster shell, security has layers — review code before you run it.
latestvk97dgw2z24hv4ybrheb3vek94x83546a
License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🚨 Clawdis
OSmacOS · Linux · Windows
Binspython3