Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Asaiuta/reverse-workbench-skill --skill competition-forensic-timelinegit clone --depth 1 https://github.com/Asaiuta/reverse-workbench-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/asaiuta/reverse-workbench-skill/competition-forensic-timeline)<a href="https://agentmods.dev/skills/asaiuta/reverse-workbench-skill/competition-forensic-timeline"><img src="https://agentmods.dev/badge/skills/asaiuta/reverse-workbench-skill/competition-forensic-timeline.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.00674 |
| Opus 5 | $0.00055 | $0.00337 |
| Sonnet 5 | $0.00022 | $0.00135 |
| Haiku 4.5 | $0.00011 | $0.00067 |
Grade A, and why
competition-forensic-timeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to competition-forensic-timeline — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competition Forensic Timeline
Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.
Use this skill when the hard part is not finding one artifact, but turning many artifacts into one replayable chronology.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Pick the smallest reliable anchor: first execution, first logon, first network session, first file write, or first mailbox action.
- Normalize timestamps, time zones, hostnames, users, process IDs, message IDs, and file paths before correlating.
- Build one minimal chain from foothold to persistence, execution, access, or exfiltration.
- Separate confirmed event order from inferred gaps.
- Reproduce the decisive timeline segment that yields the artifact or privilege conclusion.
Workflow
1. Establish Timeline Anchors
- Collect only the active surfaces: EVTX, Sysmon, registry, Amcache, prefetch, browser artifacts, mail traces, PCAPs, memory, or filesystem metadata.
- Record clock source, timezone, and any drift or truncation that could reorder events.
- Link shared identifiers across sources: PID, logon ID, GUID, message ID, hostname, username, IP, or hash.
2. Correlate The Execution Graph
- Track process tree, service or task creation, network sessions, file writes, registry changes, mailbox rules, or token use as one path.
- Distinguish causal edges from coincidence by matching identifiers and adjacency, not just nearby timestamps.
- Keep raw artifact and parsed summary side by side so every step can be traced back.
3. Compress To The Decisive Story
- Reduce the timeline to the smallest sequence that proves initial access, persistence, lateral movement, collection, or artifact recovery.
- Call out missing validation steps separately instead of mixing them into confirmed chronology.
- If the task becomes mainly about malware config extraction or a Windows pivot edge, switch to the tighter specialized skill.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 52 lines · 109 tokens per session scan A 552d9c4759b7
competition-forensic-timeline is a skill published in the GitHub repository Asaiuta/reverse-workbench-skill (2 stars, last pushed 24d ago), licensed MIT. It adds 109 tokens to every session and 674 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to competition-forensic-timeline, differing in 0 lines, and is treated as a copy.
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