tc-tracker

name	tc-tracker
description	Implement — Use when the user asks to track technical changes, create change records, manage TC lifecycles, or hand off work between AI sessions. Covers init/create/update/status/resume/close/
executor	HYBRID
skill_id	engineering.cs-engineering.tc-tracker
status	ADOPTED
security	{"level":"standard","pii":false,"approval_required":false}
anchors	["engineering"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true},{"name":"context","type":"string","description":"Additional context or background information","required":false}]
output_schema	[{"name":"result","type":"string","description":"Generated or refactored code output"},{"name":"explanation","type":"string","description":"Explanation of changes or implementation decisions"}]

TC 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.A or TC-NNN.A.1 (letter = revision, digit = sub-revision)
• NNN is sequential, MM-DD-YY is 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 done
• next_steps — ordered list of remaining actions
• blockers — anything preventing progress
• key_context — critical decisions, gotchas, patterns the next bot must know
• files_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)

1. State machine — only valid transitions are allowed.
2. Sequential IDs — revision_history uses R1, R2, R3...; test_cases uses T1, T2, T3....
3. Append-only history — revision entries are never modified or deleted.
4. Approval consistency — approved=true requires approved_by and approved_date.
5. TC ID format — must match TC-NNN-MM-DD-YY-slug.
6. Sub-TC ID format — must match TC-NNN.A or TC-NNN.A.N.
7. Atomic writes — JSON is written to .tmp then renamed.
8. 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-fix first then capture the result as a TC.
• project-management/decision-log — Architectural decisions made inside a TC's decisions_made block can also be promoted to a project-wide decision log.
• engineering-team/code-reviewer — Pre-merge review fits naturally into the tested -> deployed transition; capture the reviewer in approval.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.

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Why This Skill Exists

Implement —

When to Use

Use this skill when the user asks to track technical changes, create change records, manage TC lifecycles, or hand off work between AI sessions

What If Fails

If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.