
TC Tracker
FreeEfficiently manage and document technical changes in your code.
Free · Opens the source repo
What TC Tracker does
TC Tracker is designed for developers and teams who need to meticulously track technical changes in their codebases. It enables the creation and management of Technical Change (TC) records, which are structured JSON documents that capture essential information about code modifications. Each TC record includes details on what changed, the reasons behind the change, the person responsible, the timing, testing methods, and the current status of the work. This structured approach ensures that all changes are documented systematically, making it easier to maintain an audit trail and facilitate collaboration across AI sessions.
The skill operates through a series of Python scripts that allow users to initialize tracking, create new records, update existing ones, check statuses, and validate the integrity of the records. The commands are straightforward and designed to integrate seamlessly into your workflow. For instance, initializing tracking in a project sets up the necessary directory structure and configuration files, while creating a new TC record generates a unique identifier and populates the record with essential information. Updates to records are managed through a state machine that enforces valid transitions, ensuring that the documentation remains accurate and reliable.
One of the standout features of TC Tracker is its session handoff capability. When an AI session ends, the handoff format allows for a smooth transition to a new session, preserving the context and progress of ongoing work. This is particularly useful in collaborative environments where multiple AI agents may be involved in a project over time. The handoff block includes summaries of progress, next steps, and critical decisions made, ensuring that continuity is maintained without loss of information.
TC Tracker is ideal for teams that require detailed documentation of code changes for compliance, onboarding, or project management purposes. It is particularly beneficial when working on larger projects where tracking changes meticulously can prevent miscommunication and enhance productivity. However, it may not be necessary for smaller changes or projects where a simple changelog suffices.
When to use it
Use TC Tracker when you need to document changes in code systematically, especially in larger projects or when handing off work between AI sessions.
When not to use it
Avoid using TC Tracker for simple changelog needs or trivial changes that do not impact functionality, as it is designed for more structured and significant code modifications.
What you can build with it
Onboarding a New Project
When starting a new project, use TC Tracker to create a structured documentation of all technical changes from the outset.
Handoff Between AI Sessions
If an AI session is about to expire, use TC Tracker to document progress and next steps, ensuring continuity for the next session.
Creating Release Notes
Generate structured release notes from TC records that provide more detail than standard commit messages, enhancing communication with stakeholders.
How to install TC Tracker
View source1. Install with the skills CLI
npx skills add alirezarezvani/claude-skills/tc-tracker --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by alirezarezvaniTC Tracker
Track every code change with structured JSON records, an enforced state machine, and a session handoff format that lets a new AI session resume work cleanly when a previous one expires.
Overview
A Technical Change (TC) is a structured record that captures what changed, why it changed, who changed it, when it changed, how it was tested, and where work stands for the next session. Records live as JSON in docs/TC/ inside the target project, validated against a strict schema and a state machine.
Use this skill when the user:
- Asks to "track this change" or wants an audit trail for code modifications
- Wants to hand off in-progress work to a future AI session
- Needs structured release notes that go beyond commit messages
- Onboards an existing project and wants retroactive change documentation
- Asks for
/tc init,/tc create,/tc update,/tc status,/tc resume, or/tc close
Do NOT use this skill when:
- The user only wants a changelog from git history (use
engineering/changelog-generator) - The user only wants to track tech debt items (use
engineering/tech-debt-tracker) - The change is trivial (typo, formatting) and won't affect behavior
Storage Layout
Each project stores TCs at {project_root}/docs/TC/:
docs/TC/
├── tc_config.json # Project settings
├── tc_registry.json # Master index + statistics
├── records/
│ └── TC-001-04-05-26-user-auth/
│ └── tc_record.json # Source of truth
└── evidence/
└── TC-001/ # Log snippets, command output, screenshots
TC ID Convention
- Parent TC:
TC-NNN-MM-DD-YY-functionality-slug(e.g.,TC-001-04-05-26-user-authentication) - Sub-TC:
TC-NNN.AorTC-NNN.A.1(letter = revision, digit = sub-revision) NNNis sequential,MM-DD-YYis the creation date, slug is kebab-case.
State Machine
planned -> in_progress -> implemented -> tested -> deployed
| | | | |
+-> blocked -+ +- in_progress <-------+
| (rework / hotfix)
+-> planned
See references/lifecycle.md for the full transition table and recovery flows.
Workflow Commands
The skill ships five Python scripts that perform deterministic, stdlib-only operations on TC records. Each one supports --help and --json.
1. Initialize tracking in a project
python3 scripts/tc_init.py --project "My Project" --root .
Creates docs/TC/, docs/TC/records/, docs/TC/evidence/, tc_config.json, and tc_registry.json. Idempotent — re-running reports "already initialized" with current stats.
2. Create a new TC record
python3 scripts/tc_create.py \
--root . \
--name "user-authentication" \
--title "Add JWT-based user authentication" \
--scope feature \
--priority high \
--summary "Adds JWT login + middleware" \
--motivation "Required for protected endpoints"
Generates the next sequential TC ID, creates the record directory, writes a fully populated tc_record.json (status planned, R1 creation revision), and updates the registry.
3. Update a TC record
# Status transition (validated against the state machine)
python3 scripts/tc_update.py --root . --tc-id TC-001-04-05-26-user-auth \
--set-status in_progress --reason "Starting implementation"
# Add a file
python3 scripts/tc_update.py --root . --tc-id TC-001-04-05-26-user-auth \
--add-file src/auth.py:created
# Append handoff data
python3 scripts/tc_update.py --root . --tc-id TC-001-04-05-26-user-auth \
--handoff-progress "JWT middleware wired up" \
--handoff-next "Write integration tests" \
--handoff-next "Update README"
Every change appends a sequential R<n> revision entry, refreshes updated, and re-validates against the schema before writing atomically (.tmp then rename).
4. View status
# Single TC
python3 scripts/tc_status.py --root . --tc-id TC-001-04-05-26-user-auth
# All TCs (registry summary)
python3 scripts/tc_status.py --root . --all --json
5. Validate a record or registry
python3 scripts/tc_validator.py --record docs/TC/records/TC-001-.../tc_record.json
python3 scripts/tc_validator.py --registry docs/TC/tc_registry.json
Validator enforces the schema, checks state-machine legality, verifies sequential R<n> and T<n> IDs, and asserts approval consistency (approved=true requires approved_by and approved_date).
See references/tc-schema.md for the full schema.
Slash-Command Dispatcher
The repo ships a /tc slash command at commands/tc.md that dispatches to these scripts based on subcommand:
| Command | Action |
|---|---|
/tc init | Run tc_init.py for the current project |
/tc create <name> | Prompt for fields, run tc_create.py |
/tc update <tc-id> | Apply user-described changes via tc_update.py |
/tc status [tc-id] | Run tc_status.py |
/tc resume <tc-id> | Display handoff, archive prior session, start a new one |
/tc close <tc-id> | Transition to deployed, set approval |
/tc export | Re-render all derived artifacts |
/tc dashboard | Re-render the registry summary |
The slash command is the user interface; the Python scripts are the engine.
Session Handoff Format
The handoff block lives at session_context.handoff inside each TC and is the single most important field for AI continuity. It contains:
progress_summary— what has been donenext_steps— ordered list of remaining actionsblockers— anything preventing progresskey_context— critical decisions, gotchas, patterns the next bot must knowfiles_in_progress— files being edited and their state (editing,needs_review,partially_done,ready)decisions_made— architectural decisions with rationale and timestamp
See references/handoff-format.md for the full structure and fill-out rules.
Validation Rules (Always Enforced)
- State machine — only valid transitions are allowed.
- Sequential IDs —
revision_historyusesR1, R2, R3...;test_casesusesT1, T2, T3.... - Append-only history — revision entries are never modified or deleted.
- Approval consistency —
approved=truerequiresapproved_byandapproved_date. - TC ID format — must match
TC-NNN-MM-DD-YY-slug. - Sub-TC ID format — must match
TC-NNN.AorTC-NNN.A.N. - Atomic writes — JSON is written to
.tmpthen renamed. - Registry stats — recomputed on every registry write.
Non-Blocking Bookkeeping Pattern
TC tracking must NOT interrupt the main workflow.
- Never stop to update TC records inline. Keep coding.
- At natural milestones, spawn a background subagent to update the record.
- Surface questions only when genuinely needed ("This work doesn't match any active TC — create one?"), and ask once per session, not per file.
- At session end, write a final handoff block before closing.
Retroactive Bulk Creation
For onboarding an existing project with undocumented history, build a retro_changelog.json (one entry per logical change) and feed it to tc_create.py in a loop, or extend the script for batch mode. Group commits by feature, not by file.
Anti-Patterns
| Anti-pattern | Why it's bad | Do this instead |
|---|---|---|
Editing revision_history to "fix" a typo | History is append-only — tampering destroys the audit trail | Add a new revision that corrects the field |
| Skipping the state machine ("just set status to deployed") | Bypasses validation and hides skipped phases | Walk through in_progress -> implemented -> tested -> deployed |
| Creating one TC per file changed | Fragments related work and explodes the registry | One TC per logical unit (feature, fix, refactor) |
| Updating TC inline between every code edit | Slows the main agent, wastes context | Spawn a background subagent at milestones |
Marking approved=true without approved_by | Validator will reject; misleading audit trail | Always set approved_by and approved_date together |
Overwriting tc_record.json directly with a text editor | Risks corruption mid-write and skips validation | Use tc_update.py (atomic write + schema check) |
Putting secrets in notes or evidence | Records are committed to the repo | Reference an env var or external secret store |
| Reusing TC IDs after deletion | Breaks the sequential guarantee and confuses history | Increment forward only — never recycle |
Letting next_steps go stale | Defeats the purpose of handoff | Update on every milestone, even if it's "nothing changed" |
Cross-References
engineering/changelog-generator— Generates Keep-a-Changelog release notes from Conventional Commits. Pair it with TC tracker: TC for the granular per-change audit trail, changelog for user-facing release notes.engineering/tech-debt-tracker— For tracking long-lived debt items rather than discrete code changes.engineering/focused-fix— When a bug fix needs systematic feature-wide repair, run/focused-fixfirst then capture the result as a TC.project-management/decision-log— Architectural decisions made inside a TC'sdecisions_madeblock can also be promoted to a project-wide decision log.engineering-team/code-reviewer— Pre-merge review fits naturally into thetested -> deployedtransition; capture the reviewer inapproval.approved_by.
References in This Skill
- references/tc-schema.md — Full JSON schema for TC records and the registry.
- references/lifecycle.md — State machine, valid transitions, and recovery flows.
- references/handoff-format.md — Session handoff structure and best practices.
Frequently asked questions about TC Tracker
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