skills/work-timeline-planner/SKILL.md
Build retrospective or forward-looking work timelines from git history, docs, notes, or chat records. Use for progress summaries, mentor reports, and phase planning.
npx skillsauth add a-green-hand-jack/ml-research-skills work-timeline-plannerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Turn evidence such as git history, project documents, user descriptions, and chat notes into a readable timeline report. The output can be Markdown, standalone HTML, or both.
Use this skill for two main cases:
<installed-skill-dir>/
├── SKILL.md
├── references/
│ ├── evidence-model.md
│ ├── output-formats.md
│ └── report-modes.md
└── templates/
├── timeline-report.html
└── timeline-report.md
references/evidence-model.mdreferences/report-modes.md when choosing between personal review, mentor report, planning report, or a hybridreferences/output-formats.md when choosing between Markdown, HTML, or both, or when deciding between Mermaid, Frappe Gantt, and Plotlytemplates/timeline-report.md and templates/timeline-report.html as the output skeletonsIdentify the primary mode before gathering evidence:
personal-review: review past work, often across multiple projectsmentor-report: summarize past work for one project and one audiencenext-phase-plan: plan upcoming work with milestones and dependencieshybrid: summarize recent work and then propose the next phaseAlso identify:
markdown, html, or bothmermaid, frappe-gantt, or plotlyIf the user did not specify a time window, infer a reasonable one from context and state it explicitly in the report.
Default output policy:
markdown + mermaid for shareable repo-native reportshtml + frappe-gantt for richer interactive reviewboth when the user wants an archiveable report plus a better local visualizationPrefer primary evidence first.
For each in-scope repo, gather:
git rev-parse --show-toplevel
git log --date=short --stat --reverse --since="<start-date>" --until="<end-date>"
git log --date=short --pretty=format:"%ad%x09%h%x09%s" --since="<start-date>" --until="<end-date>"
Then layer in:
README.md, docs/, PROJECT.md, decision-log, experiment logs, or progress notesIf the user wants a multi-project review, gather evidence per project and keep project boundaries explicit.
If one or more repos or notes are missing, ask only for the minimum missing inputs needed to avoid hallucinating the timeline.
Convert raw evidence into a small set of work blocks.
Each block should have:
Do not map one commit to one Gantt task unless the user explicitly wants that level of detail.
Instead:
When a date range is inferred rather than explicit, say so in the text summary.
Follow the guidance in references/report-modes.md.
As a default:
personal-review, group by project, then by workstreammentor-report, group by project phase or deliverable, not by every technical branchnext-phase-plan, group by milestone and dependencyThe final output should usually contain:
Follow references/output-formats.md.
For Markdown output, use templates/timeline-report.md as the base structure.
For HTML output, use templates/timeline-report.html as the base structure.
The chart engine should usually be chosen like this:
mermaid: default for lightweight Markdown reports kept in a repo or shared in chatfrappe-gantt: default for standalone HTML that the user wants to inspect locally with a better UIplotly: use only when you already have clean structured task data and want an interactive timeline, especially from Python-driven workflowsFor Markdown, the default Gantt should be Mermaid:
```mermaid
gantt
title Example
dateFormat YYYY-MM-DD
section Project A
Baseline experiments :done, a1, 2026-04-01, 5d
Model fixes :done, a2, after a1, 4d
Next ablation :active, a3, 2026-04-12, 6d
Guidelines:
done when usefulactive or plain future tasks only when the timing is justifiedIf the user wants forward planning:
For planning, include:
Before presenting the report, check:
The final output should usually be a document the user can keep or share.
If asked to save it into a repo, use a path that matches the user's purpose, for example:
docs/reports/work_timeline_YYYY_MM.mddocs/reports/mentor_update_YYYY_MM.mddocs/plans/next_phase_timeline.mddocs/reports/work_timeline_YYYY_MM.htmldocs/reports/mentor_update_YYYY_MM.htmlIf the user only wants a draft in chat, still structure it as if it could be saved directly to a file.
testing
Bootstrap project-local ml-research-skills. Use from global installs when creating a new ML research project, enabling this collection in an existing ML research repo, or deciding whether to install the full bundle locally. Route to project-init for new projects; do not handle paper or experiment work directly.
development
Route project operations tasks — git, memory, bootstrap, remote, workspace, code review, timeline, ops — to the correct skill. Use when the task involves commits, pushes, worktrees, project memory, enabling project-local skills, SSH/server coordination, sidecar runners, or audits. Do not solve the ops task directly.
testing
Route ML/AI paper writing tasks to the correct skill — contract planning, prose drafting, section writing, consistency editing, review simulation, rebuttal, submission, or citation work. Use when the task involves writing, revising, reviewing, or submitting a paper instead of guessing between paper-writing-assistant, paper-writing-contract-planner, paper-reviewer-simulator, auto-paper-improvement-loop, or citation skills. Do not draft prose directly.
data-ai
Project-local router for ML research skill selection. Use inside an initialized ML research project, or while maintaining this skill repo, when the user describes an ML research/paper/experiment/discovery/ops/release workflow and may not know the skill; route to a domain router or high-signal leaf. Do not use for generic non-ML projects.