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building-super-timelines-with-plaso

Generate forensic super-timelines with Plaso's log2timeline.py, pinfo.py, psort.py, and psteal.py CLI tools (fusing file-system MACB, registry, EVTX, browser history, prefetch, LNK, and more), then triage and filter the results in Timesketch. Use when reconstructing the full sequence of events on a compromised or forensically imaged host during a DFIR investigation.

69

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a lean, highly actionable workflow: every step is a concrete, executable command, sequenced clearly with an inspection step and a validation checklist. The main structural weakness is progressive disclosure — the references/ bundle files are orphaned (never linked from the body) and their content is partially duplicated inline in the Key Commands and MITRE tables.

Suggestions

Replace the inline '## Key Commands' table with a pointer to references/api-reference.md (e.g. 'Full flag reference: see references/api-reference.md'), removing the duplicated command rows from the body.

Signal the standards mapping file from the MITRE ATT&CK Mapping section (e.g. 'Full NIST CSF / ATT&CK rationale: see references/standards.md') so the bundle file is discoverable rather than orphaned.

Add a short feedback loop after the pinfo step (e.g. 'If parsers report warnings or event counts are zero, re-run extraction with an explicit --parsers list') to upgrade workflow validation from end-checklist to inline checkpoints.

DimensionReasoningScore

Conciseness

Quotes: "Plaso (Plaso Langar Að Safna Öllu) is the open-source engine behind log2timeline, the standard for building forensic super timelines — a single chronological, normalized view fusing hundreds of artifact types", and the "## Key Commands" table ("log2timeline.py --storage-file out.plaso <source>" etc.) which restates commands already shown in the workflow. Mostly efficient and command-dense, but the tool-list overview and the duplicated Key Commands table are instances that could be trimmed. Not 5 due to this redundancy; not 3 because there is no concept-explaining padding of the kind Claude already knows beyond a brief justified overview.

4 / 5

Actionability

Quotes: "docker pull log2timeline/plaso", "log2timeline.py --parsers "win7,!filestat" --storage-file timeline.plaso /cases/image.E01", "psort.py --output-time-zone 'UTC' -o l2tcsv -w supertimeline.csv timeline.plaso \"date > datetime('2026-01-01T00:00:00') AND ...\"", "psteal.py --source /cases/greendale/image.E01 -o l2tcsv -w supertimeline.csv", "timesketch_importer --host http://127.0.0.1:5000 --username admin --timeline_name "greendale-host01" --sketch_id 1 timeline.plaso". Every step is a copy-paste-ready, fully-qualified command covering the common cases (CSV, JSONL, one-step, import).

5 / 5

Workflow Clarity

Quotes: the 7-step workflow ("1. Extract events into a storage file", "2. Inspect the storage file — pinfo.py reports source, parsers used, event counts, and any warnings", ... "7. Hunt for anti-forensics") plus the "## Validation Criteria" checklist ("[ ] pinfo confirms expected parsers ran and event counts are non-zero"). The sequence is clear with checkpoints present (pinfo inspection step, final checklist), satisfying the batch-operation validation requirement. Not 5 because there are no feedback loops — no guidance on what to do when pinfo reports warnings or zero events — and the checklist lives at the end rather than gating each step.

4 / 5

Progressive Disclosure

The body is well-sectioned, but the provided bundle files are never signaled: references/api-reference.md and references/standards.md are not mentioned anywhere in the body, while the body inlines near-duplicate content — the "## Key Commands" table overlaps references/api-reference.md ("psort.py -o l2tcsv -w out.csv out.plaso \"<filter>\"" appears in both) and the "## MITRE ATT&CK Mapping" table duplicates references/standards.md (both carry the T1070 row with the same rationale). Per the guideline to score against the actual bundle structure, this matches anchor 3: references exist but are not clearly signaled, and content that should live in them is inline. Not 4 because two orphaned, unreferenced files plus duplicated inline content is more than a minor organization gap.

3 / 5

Total

16

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete tools and actions, an explicit 'Use when' trigger clause, third-person voice, and a well-defined DFIR niche. The only weakness is a few missing natural trigger synonyms (e.g. 'incident response', 'timeline analysis', 'E01').

DimensionReasoningScore

Specificity

Quotes: "Generate forensic super-timelines with Plaso's log2timeline.py, pinfo.py, psort.py, and psteal.py CLI tools (fusing file-system MACB, registry, EVTX, browser history, prefetch, LNK, and more), then triage and filter the results in Timesketch." It names multiple concrete tools, specific actions (generate, fuse, triage, filter), and the artifact types fused — comprehensive, third-person, no fluff. Not 4 because coverage of actions is comprehensive rather than having minor gaps; not below since no generic filler exists.

5 / 5

Completeness

The 'what' is explicit ("Generate forensic super-timelines with Plaso's ... CLI tools ..., then triage and filter the results in Timesketch") and the 'when' is an explicit trigger clause ("Use when reconstructing the full sequence of events on a compromised or forensically imaged host during a DFIR investigation."). Both are answered clearly and concretely, matching the 5 anchor exactly; a 4 would require the 'when' to be less explicit.

5 / 5

Trigger Term Quality

Quotes: "forensic super-timelines", "Plaso", "log2timeline", "psort", "Timesketch", "DFIR", "compromised", "forensically imaged host". Strong natural-term coverage for the DFIR niche, but common variations a user might say — "incident response", "timeline analysis", "super timeline" (unhyphenated), evidence formats like "E01" — are absent. Not 5 because a few natural synonyms/extensions are missing; not 3 because coverage goes well beyond 'some relevant keywords'.

4 / 5

Distinctiveness Conflict Risk

Quotes: "Plaso's log2timeline.py, pinfo.py, psort.py, and psteal.py", "triage and filter the results in Timesketch", "during a DFIR investigation". This is a clear niche (Plaso/Timesketch super-timeline forensics) with distinct named-tool triggers; minimal overlap risk with generic timeline or log-analysis skills.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
mukul975/Anthropic-Cybersecurity-Skills
Reviewed

Table of Contents

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