work-chronicler

CLI and MCP server that turns your GitHub PRs and JIRA tickets into a searchable
markdown work log for performance reviews and resumes.

Review season arrives and you have to reconstruct a year of work from memory.
work-chronicler pulls what you already wrote down (PR descriptions and ticket
history) into plain markdown files on your machine, then gives AI assistants
structured access to it.

How it works

Data flows one way: fetch from the sources, enrich locally, then read it with
AI skills or the MCP server.

flowchart LR
  accTitle: work-chronicler pipeline
  accDescr: GitHub and JIRA are fetched into a local markdown work log, linked and analyzed, then read by AI skills and the MCP server to produce resume bullets and reviews.
  GH[GitHub PRs] --> F[fetch]
  JI[JIRA tickets] --> F
  F --> WL[(Markdown work log)]
  WL --> LA[link + analyze + filter]
  LA --> WL
  WL --> SK[AI skills]
  WL --> MCP[MCP server]
  SK --> OUT[Resume bullets, self-reviews, summaries]
  MCP --> OUT

Each PR becomes one file at
work-log/pull-requests/<org>/<repo>/<date>_<number>.md. This example (invented
data) shows a PR after link and analyze --tag-prs filled in jiraTickets
and impact:

---
title: "feat(billing): add usage-based invoice line items"
prNumber: 1482
repository: billing-service
org: acme
author: jdoe
state: merged
createdAt: 2026-03-04T17:22:10Z
mergedAt: 2026-03-06T15:41:02Z
closedAt: 2026-03-06T15:41:02Z
url: https://github.com/acme/billing-service/pull/1482
additions: 612
deletions: 148
changedFiles: 19
jiraTickets:
  - BILL-311
labels:
  - billing
impact: flagship
---

## Summary

Adds metered line items to monthly invoices so usage-plan customers see
per-feature charges. Replaces the flat overage fee.

Quick start

Requires Node.js 22.17 or later, a GitHub personal access token and, optionally,
a JIRA API token.

npm install -g work-chronicler

work-chronicler init            # guided setup: profile, sources, repos, tokens
work-chronicler fetch all       # fetch PRs and tickets, then link them
work-chronicler status          # see what was fetched
work-chronicler analyze --all   # tag impact, detect projects, build a timeline
work-chronicler skills install  # add the AI skills to Claude Code, Cursor, Codex or Gemini

Then, in your AI coding assistant, run a skill such as
/work-chronicler-write-self-review. Skills are assistant slash commands; they
do not run in a terminal.

Data lives in ~/.work-chronicler/profiles/<profile>/. Each profile has its own
config.yaml, .env (tokens, mode 600) and work-log/. Set
WORK_CHRONICLER_HOME to use another root.

What you can do

Goal	Use
Pull PRs and tickets into markdown	`fetch github`, `fetch jira`, `fetch all`
Connect PRs to the tickets they reference	`link` (run automatically by `fetch all`)
Classify PRs as minor, standard, major or flagship	`analyze --tag-prs`
Group related work into projects and a timeline	`analyze --projects --timeline`
Keep only the work that matters for a review	`filter` (for example `--min-impact major --merged-only`)
Draft resume bullets, self-reviews and summaries	AI skills (`skills install`)
Let an assistant query your history	`mcp` (stdio MCP server)
Keep work and personal history apart	profiles (`profile list\|switch\|delete`, global `--profile`)
Track direct reports as a manager	manager mode (`init --mode manager`, `reports ...`)

The command reference lists every command and flag.

Scope

What exists today:

Sources: GitHub pull requests you authored (by org and repo, or every
repo in an org) and JIRA Cloud tickets assigned to you (one or more
instances and projects). No other sources are supported.
Local enrichment: PR-to-ticket linking, impact classification, project
detection from shared ticket references, a week/month timeline, and filtered
subsets. None of these call an AI model.
IC and manager modes: an individual work log per profile, or per-report
work logs plus team-level project rollups for people managers (team
timeline aggregation is not implemented yet).
MCP server: seven read-only query tools over the work log.
AI skills: ten skills (six IC, four manager) that read the work log and
write documents to the profile's outputs/ directory.

The CLI itself never calls an LLM; writing happens in your assistant through
the skills or the MCP server.

Documentation

Start at the documentation index:

Configuration: profiles, config.yaml,
tokens, environment variables and the workspace layout.
Command reference: every command and flag.
MCP server: client setup and the available tools.
AI skills: what each skill does and how
skills find your data.
Manager mode: tracking reports and team analysis.

Development

pnpm install
pnpm build
pnpm test

Run the CLI from source with pnpm cli <command>. See
CONTRIBUTING.md for checks, commit conventions and the
release process, and CHANGELOG.md for release history.

License

MIT