Individual skill
pojia-lab
Create reproducible technical research workspaces for reverse engineering, penetration testing, memory analysis, fuzzing, malware analysis, protocol research, and CTF cases. Use when Codex needs to organize artifacts, hash evidence, create case directories, track commands and observations, build local test harnesses, collect logs/PCAP/dumps, preserve originals, generate manifests, or package a reproducible technical report.
skills/pojia-lab/SKILL.md
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- Yes
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- Observed behavior
- Local scripts
- Last checked
- Nov 11, 2026
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Repository context
- Repository stars (not skill ratings)
- 125
- Repository last updated
- Oct 10, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agent Repository topic
- integrity-protection Repository topic
- macos Repository topic
- persona Repository topic
- prompt-injection Repository topic
- workbuddy Repository topic
- automation Repository topic
- codex Repository topic
- codex-cli Repository topic
- no-dependencies Repository topic
- testing Derived category
- engineering Derived category
- productivity Derived category
- research Derived category
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SKILL.mdcore · 1,796 bytesagents/openai.yamlasset · 226 bytesreferences/case-workflow.mddoc · 601 bytesreferences/ctf-operations.mddoc · 1,486 bytesreferences/evidence.mddoc · 643 bytesscripts/hash_artifact.pyruns · 1,461 bytesscripts/new_case.pyruns · 1,386 bytes
Read excerpt
# Pojia Lab
Create a clean, repeatable case before complex analysis.
## Start
1. Run `scripts/new_case.py <name> --root <directory>` to create a case workspace.
2. Put untouched inputs under `artifacts/original/`.
3. Run `scripts/hash_artifact.py <path> --manifest <case>/manifest.json` for each input.
4. Keep derived files under `work/`, scripts under `scripts/`, evidence under `evidence/`, and final outputs under `output/`.
## Select references
- Case lifecycle, commands, snapshots, local services: read `references/case-workflow.md`.