Tech Debt Tracker
The agent identifies, scores, prioritizes, and tracks technical debt across codebases using AST parsing, cost-of-delay analysis, and trend dashboards.
Core Capabilities
- Detection — AST parsing (Python) and regex pattern matching (all languages) across six debt categories: code, architecture, test, documentation, dependency, infrastructure.
- Severity scoring — rate each item on velocity, quality, productivity, and business impact (1-10) plus effort sizing (XS-XL) and risk.
- Cost-of-delay — compute interest rate (
Impact x Frequency) and cost of delay (Interest x Sprints x Team Multiplier); also WSJF and RICE frameworks.
- Prioritization — plot on the Cost-of-Delay vs Effort matrix (Immediate / Planned / Opportunistic / Backlog).
- Sprint allocation — apply the Debt-to-Feature ratio by team velocity; reserve capacity for debt work.
- Refactoring strategies — Strangler Fig, Branch by Abstraction, Feature Toggles, Parallel Run.
- Reporting — executive and engineering dashboards, trend analysis, velocity tracking, and forecasts from scan snapshots.
When to Use
- Tracking and quantifying technical debt across a repository.
- Prioritizing refactoring work and calculating cost-of-delay.
- Planning sprint capacity allocation between debt and features.
- Reporting debt health, trends, and investment recommendations to execs.
Clarify First
Before scanning or reporting, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
| Tool |
Purpose |
Command |
debt_scanner.py |
Scan a directory for debt signals; output JSON inventory + text report |
python scripts/debt_scanner.py <dir> --output scan_results --format both |
debt_prioritizer.py |
Enrich inventory with cost-of-delay/WSJF/RICE and sprint allocation |
python scripts/debt_prioritizer.py scan_results.json --framework wsjf --team-size 8 |
debt_dashboard.py |
Trend analysis, velocity, forecasts, and exec summary across snapshots |
python scripts/debt_dashboard.py --input-dir ./debt_scans/ --period quarterly |
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/methodology.md — the 7-step workflow, debt-classification table, severity scoring framework, interest-rate/cost-of-delay formulas, prioritization matrix, WSJF, sprint allocation ratios, the debt-item JSON schema, refactoring strategies, and quarterly planning. Read when scoring, prioritizing, or planning.
- references/tool-reference.md — full parameter tables, examples, and output-format details for all three scripts plus the troubleshooting table. Read when running the scripts or debugging output.
- references/dashboards-and-examples.md — executive and engineering dashboard layouts, a worked Python-microservice scan example, and the success-criteria bar. Read when generating reports or validating quality.
- references/debt-classification-taxonomy.md — comprehensive taxonomy for classifying debt across dimensions with detection heuristics per category. Read when calibrating detection or labeling items.
- references/prioritization-framework.md — deep prioritization approaches based on business value, risk, effort, and strategic alignment. Read when designing a prioritization rubric.
- references/stakeholder-communication-templates.md — templates and guidelines for communicating debt status, impact, and recommendations to different stakeholder groups. Read when reporting to execs or product.
Also see the skill-root REFERENCE.md for the Technical Debt Quadrant (Fowler) and the implementation roadmap phases.
Scope & Limitations
This skill covers:
- Static detection of code-level, architecture, test, documentation, dependency, and infrastructure debt via AST parsing (Python) and regex pattern matching (all languages).
- Quantitative prioritization of debt items using cost-of-delay, WSJF, and RICE frameworks with configurable team size and sprint capacity.
- Historical trend analysis, health scoring, debt velocity tracking, and executive/engineering dashboard generation from multiple scan snapshots.
- Sprint allocation planning with capacity-aware backlog scheduling and effort estimation by debt type.
This skill does NOT cover:
- Runtime performance profiling or production monitoring -- see
engineering/performance-profiler and engineering/observability-designer for those concerns.
- Dependency vulnerability scanning (CVE detection) or software composition analysis -- see
engineering/dependency-auditor for security-focused dependency review.
- Automated refactoring or code transformation -- the skill identifies and prioritizes debt but does not modify source code.
- Database schema debt, API contract drift, or infrastructure-as-code drift detection -- see
engineering/database-schema-designer, engineering/api-design-reviewer, and engineering/migration-architect for those domains.
Integration Points
| Skill |
Integration |
Data Flow |
engineering/dependency-auditor |
Feed dependency audit findings into the scanner as dependency_debt items to unify all debt in one inventory. |
Dependency audit JSON -> scanner config or manual merge into debt_inventory.json |
engineering/performance-profiler |
Correlate performance hotspots with high-complexity debt items to prioritize refactoring that yields both quality and speed gains. |
Profiler hotspot report -> cross-reference with scanner output by file path |
engineering/ci-cd-pipeline-builder |
Add debt_scanner.py as a CI pipeline step to fail builds when health score drops below a threshold or critical debt count increases. |
Scanner JSON output -> CI gate condition on summary.health_score |
engineering/pr-review-expert |
Surface relevant debt items during code review by querying the debt inventory for files touched in a pull request. |
PR changed-files list -> filter debt_inventory.json by file_path |
engineering/observability-designer |
Map infrastructure debt items (missing monitoring, env inconsistencies) to observability gaps identified by the observability skill. |
Dashboard category_distribution -> observability gap analysis |
engineering/migration-architect |
Use the prioritized backlog to scope and sequence large-scale migration efforts, especially for architecture-category debt rated as planned initiatives. |
Prioritizer sprint_allocation -> migration planning timeline |
1---2name: tech-debt-tracker3description: Scan codebases for technical debt with AST parsing, prioritize by impact, and generate trend dashboards. Use when tracking tech debt, prioritizing refactoring, calculating cost-of- delay, planning sprint debt, or reporting debt to execs.4license: MIT + Commons Clause5---6# Tech Debt Tracker
7
8The agent identifies, scores, prioritizes, and tracks technical debt across codebases using AST parsing, cost-of-delay analysis, and trend dashboards.
9
10## Core Capabilities
11
12- **Detection** — AST parsing (Python) and regex pattern matching (all languages) across six debt categories: code, architecture, test, documentation, dependency, infrastructure.
13- **Severity scoring** — rate each item on velocity, quality, productivity, and business impact (1-10) plus effort sizing (XS-XL) and risk.
14- **Cost-of-delay** — compute interest rate (`Impact x Frequency`) and cost of delay (`Interest x Sprints x Team Multiplier`); also WSJF and RICE frameworks.
15- **Prioritization** — plot on the Cost-of-Delay vs Effort matrix (Immediate / Planned / Opportunistic / Backlog).
16- **Sprint allocation** — apply the Debt-to-Feature ratio by team velocity; reserve capacity for debt work.
17- **Refactoring strategies** — Strangler Fig, Branch by Abstraction, Feature Toggles, Parallel Run.
18- **Reporting** — executive and engineering dashboards, trend analysis, velocity tracking, and forecasts from scan snapshots.
19
20## When to Use
21
22- Tracking and quantifying technical debt across a repository.
23- Prioritizing refactoring work and calculating cost-of-delay.
24- Planning sprint capacity allocation between debt and features.
25- Reporting debt health, trends, and investment recommendations to execs.
26
27## Clarify First
28
29Before scanning or reporting, confirm these inputs. If any is unknown or vague, ASK — do not assume:
30
31- [ ] **Target codebase** — the directory to scan (the subject of the debt inventory)
32- [ ] **Prioritization framework & team size** — cost-of-delay / WSJF / RICE and headcount (`--framework`, `--team-size`; changes the ranking and sprint allocation)
33- [ ] **Report audience** — exec dashboard vs engineering inventory (sets the report format and altitude)
34
35Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
36
37## Tools
38
39| Tool | Purpose | Command |
40|------|---------|---------|
41| `debt_scanner.py` | Scan a directory for debt signals; output JSON inventory + text report | `python scripts/debt_scanner.py <dir> --output scan_results --format both` |
42| `debt_prioritizer.py` | Enrich inventory with cost-of-delay/WSJF/RICE and sprint allocation | `python scripts/debt_prioritizer.py scan_results.json --framework wsjf --team-size 8` |
43| `debt_dashboard.py` | Trend analysis, velocity, forecasts, and exec summary across snapshots | `python scripts/debt_dashboard.py --input-dir ./debt_scans/ --period quarterly` |
44
45## References
46
47Load the reference that matches the task — keep this file lean and pull detail on demand:
48
49- **[references/methodology.md](references/methodology.md)** — the 7-step workflow, debt-classification table, severity scoring framework, interest-rate/cost-of-delay formulas, prioritization matrix, WSJF, sprint allocation ratios, the debt-item JSON schema, refactoring strategies, and quarterly planning. Read when scoring, prioritizing, or planning.
50- **[references/tool-reference.md](references/tool-reference.md)** — full parameter tables, examples, and output-format details for all three scripts plus the troubleshooting table. Read when running the scripts or debugging output.
51- **[references/dashboards-and-examples.md](references/dashboards-and-examples.md)** — executive and engineering dashboard layouts, a worked Python-microservice scan example, and the success-criteria bar. Read when generating reports or validating quality.
52- **[references/debt-classification-taxonomy.md](references/debt-classification-taxonomy.md)** — comprehensive taxonomy for classifying debt across dimensions with detection heuristics per category. Read when calibrating detection or labeling items.
53- **[references/prioritization-framework.md](references/prioritization-framework.md)** — deep prioritization approaches based on business value, risk, effort, and strategic alignment. Read when designing a prioritization rubric.
54- **[references/stakeholder-communication-templates.md](references/stakeholder-communication-templates.md)** — templates and guidelines for communicating debt status, impact, and recommendations to different stakeholder groups. Read when reporting to execs or product.
55
56Also see the skill-root `REFERENCE.md` for the Technical Debt Quadrant (Fowler) and the implementation roadmap phases.
57
58## Scope & Limitations
59
60**This skill covers:**
61- Static detection of code-level, architecture, test, documentation, dependency, and infrastructure debt via AST parsing (Python) and regex pattern matching (all languages).
62- Quantitative prioritization of debt items using cost-of-delay, WSJF, and RICE frameworks with configurable team size and sprint capacity.
63- Historical trend analysis, health scoring, debt velocity tracking, and executive/engineering dashboard generation from multiple scan snapshots.
64- Sprint allocation planning with capacity-aware backlog scheduling and effort estimation by debt type.
65
66**This skill does NOT cover:**
67- Runtime performance profiling or production monitoring -- see `engineering/performance-profiler` and `engineering/observability-designer` for those concerns.
68- Dependency vulnerability scanning (CVE detection) or software composition analysis -- see `engineering/dependency-auditor` for security-focused dependency review.
69- Automated refactoring or code transformation -- the skill identifies and prioritizes debt but does not modify source code.
70- Database schema debt, API contract drift, or infrastructure-as-code drift detection -- see `engineering/database-schema-designer`, `engineering/api-design-reviewer`, and `engineering/migration-architect` for those domains.
71
72## Integration Points
73
74| Skill | Integration | Data Flow |
75|-------|-------------|-----------|
76| `engineering/dependency-auditor` | Feed dependency audit findings into the scanner as `dependency_debt` items to unify all debt in one inventory. | Dependency audit JSON -> scanner config or manual merge into `debt_inventory.json` |
77| `engineering/performance-profiler` | Correlate performance hotspots with high-complexity debt items to prioritize refactoring that yields both quality and speed gains. | Profiler hotspot report -> cross-reference with scanner output by file path |
78| `engineering/ci-cd-pipeline-builder` | Add `debt_scanner.py` as a CI pipeline step to fail builds when health score drops below a threshold or critical debt count increases. | Scanner JSON output -> CI gate condition on `summary.health_score` |
79| `engineering/pr-review-expert` | Surface relevant debt items during code review by querying the debt inventory for files touched in a pull request. | PR changed-files list -> filter `debt_inventory.json` by `file_path` |
80| `engineering/observability-designer` | Map infrastructure debt items (missing monitoring, env inconsistencies) to observability gaps identified by the observability skill. | Dashboard `category_distribution` -> observability gap analysis |
81| `engineering/migration-architect` | Use the prioritized backlog to scope and sequence large-scale migration efforts, especially for architecture-category debt rated as planned initiatives. | Prioritizer `sprint_allocation` -> migration planning timeline |