ShaheerKhawaja/session-validate
skills/session-validate/SKILL.md
End-of-session self-training — captures session metrics, extracts patterns via metaclaw-learner, updates instincts, and generates optimization hypotheses for the next run.
$ install --global
skillsauthnpx skillsauth add ShaheerKhawaja/ProductionOS session-validateInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 23, 2026, 2:13 AM20.7s1 file scanned
SKILL.md
- name:
- session-validate
- description:
- End-of-session self-training — captures session metrics, extracts patterns via metaclaw-learner, updates instincts, and generates optimization hypotheses for the next run.
- argument-hint:
- [mode or session scope]
session-validate
Overview
Use this as the Codex-first end-of-session learning workflow. It should summarize the session, extract useful lessons, and prepare the next run to be better instead of letting the session context evaporate.
Source references:
.claude/commands/session-validate.mdagents/metaclaw-learner.md
Inputs
- optional
mode:quick,standard, ordeep
Codex Workflow
- Read the session artifacts and metrics.
- Summarize what happened.
- Extract lessons and patterns worth keeping.
- In deeper mode, generate optimization hypotheses for future runs.
- Write a session report and update the learning store.
Expected Output
- session metrics
- extracted lessons
- optimization hypotheses where applicable
- end-of-session report
Guardrails
- do not modify source code
- keep the learning artifacts additive and traceable
- if the signal is weak, avoid pretending there were meaningful lessons
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