Incident Replay
v1.0.6Post-mortem analysis for AI agent failures. Capture state, reconstruct timelines, identify root causes. When your agent breaks, know what happened, why, and...
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byShadow Rose@theshadowrose
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 (post-mortem forensics) align with the code and SKILL.md. The package only uses filesystem, hashing, regex, and JSON storage to capture snapshots, build timelines, classify root causes, and generate reports — all coherent with forensic intent.
Instruction Scope
SKILL.md instructs the agent to read workspace files, take snapshots, diff them, scan logs for patterns (including API key/password patterns), and write incident data and reports locally. This is expected for a forensic tool, but it means the skill will capture the contents of included files (by default *.py, *.md, *.txt, *.json, logs). Review and tighten include/exclude patterns and WORKSPACE_ROOT before use to avoid capturing unrelated sensitive files.
Install Mechanism
No install spec; it's an instruction-and-code skill relying on Python stdlib. Nothing is downloaded or executed from remote URLs, and no third-party packages are pulled in.
Credentials
The skill requests no environment variables or external credentials. The default config looks broad (captures many text file types), which is reasonable for forensic analysis but should be tuned to avoid unnecessary exposure of secrets.
Persistence & Privilege
The skill persists snapshots, incidents, and reports under a configurable DATA_DIR (defaults to incident_data). It is not 'always' enabled and does not modify other skills. Because it can read and store file contents locally, run it with a safe WORKSPACE_ROOT and tuned include/exclude patterns; ensure appropriate filesystem permissions and backups for the incident_data directory.
Assessment
This skill appears to do what it says: local forensic snapshots, diffs, trigger detection, analysis, and reporting using only the Python standard library. Before installing or running it: (1) set WORKSPACE_ROOT to the smallest useful directory (avoid running from '/', your home dir, or other broad roots), (2) tighten INCLUDE_PATTERNS/EXCLUDE_PATTERNS so you don't accidentally capture secrets or unrelated files, (3) confirm the DATA_DIR location and secure its permissions (incident data contains captured file contents), (4) review the code if you need assurance there are no outbound network calls (the provided files use only stdlib file/regex/json operations), and (5) run first in a sandbox or test workspace to validate behavior. If you need the agent to run this autonomously, remember autonomous invocation plus the ability to read the workspace increases the potential blast radius—only permit that if you trust the agent's policies and inputs.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.