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assess-technical-debt

Assess project-wide structural technical debt: complexity hotspots, deprecated API usage, duplication clusters, architecture rot, and low-value tests. Ranks findings by impact and refactor effort into a report at .turbo/technical-debt.md

Install / Use

npx skills add tobihagemann/turbo --skill assess-technical-debt

Installs into whichever agent you are using.

About this skill
πŸ“„

SKILL.md

Installable skill definition

Quality Score

86/100

Supported Platforms

Universal

Our assessment of assess-technical-debt

assess-technical-debt scores 86/100 on our quality scale, 2408th of 4,616 Development & Engineering skills we index.

Its SKILL.md is 11 KB long, well organised into 23 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

It has 405 GitHub stars, a meaningful sign that others use it.

Substance
29/30
Structure
17/20
Description
15/15
Adoption
11/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 12 days ago, so assess-technical-debt is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit β€” read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-10-05. Automated pattern scan on 2026-10-05. It catches known dangerous patterns, not every risk β€” read a skill before letting an agent act on it.

assess-technical-debt compared with similar skills

All 4 of these similar skills score higher than assess-technical-debt; compare them before choosing.

SkillScoreStarsUpdatedFormat
assess-technical-debt (this skill)by tobihagemann8640512d agoSKILL.md
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Frequently asked questions

How do I install assess-technical-debt?
Run npx skills add tobihagemann/turbo --skill assess-technical-debt. The install tabs above show the steps for each supported agent.
Which AI agents does assess-technical-debt work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is assess-technical-debt safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is assess-technical-debt still maintained?
The repository was last updated 12 days ago, so assess-technical-debt is actively maintained.

name: assess-technical-debt description: "Assess project-wide structural technical debt: complexity hotspots, deprecated API usage, duplication clusters, architecture rot, and low-value tests. Ranks findings by impact and refactor effort into a report at .turbo/technical-debt.md. Use when the user asks to "assess technical debt", "find technical debt", "review technical debt", "what should we refactor", "find refactoring candidates", "where is the code rot", "what's our worst code", "find low-value tests", or "which tests can we delete". Analysis-only β€” does not modify code."

Assess Technical Debt

Surface the structural debt that routine review keeps out of scope: long-lived complexity, deprecated APIs, duplication, tangled architecture, and low-value tests that need deliberate refactoring. Project-wide, analysis-only. Ranks each finding by impact and effort and writes .turbo/technical-debt.md and .turbo/technical-debt.html.

Task Tracking

At the start, use TaskCreate to create a task for each phase:

  1. Scope and partition
  2. Run debt analysis agents
  3. Run /evaluate-findings skill
  4. Resolve escalated findings
  5. Rank and write markdown report
  6. Generate HTML report

Step 1: Scope and Partition

If $ARGUMENTS specifies paths, assess those directly (skip the question).

Otherwise, use AskUserQuestion to confirm scope:

  • Whole codebase β€” assess all source and test files
  • Specific paths β€” user provides directories or file patterns

Once scope is determined:

  1. Glob for source and test files in the selected scope. Exclude generated and vendored directories (node_modules/, dist/, build/, vendor/, __pycache__/, .build/, DerivedData/, target/, .tox/, and others appropriate to the project).
  2. Partition files by top-level directory. If a single directory holds far more files than its siblings, sub-partition it by its immediate subdirectories.

Step 2: Run Debt Analysis Agents

Before dispatching, read the project's test configuration and CI workflow to identify any test tier that resets a shared external resource between tests, such as a database, a fixed port, or a cache. Such tiers have no cross-process interlock, so agents running them concurrently wipe each other's state and return failures that look like real defects. Name any such tier to every agent as off-limits.

Emit all Agent tool calls below in one assistant message. Each Agent call uses model: "opus" and no name. Wait for every agent to report before continuing. Do not begin the next step on a partial set, and do not relaunch an agent that has not yet reported. Every agent's prompt must direct it to omit name on any Agent tool call it makes itself. Each Agent's prompt instructs it to read references/debt-reviewer.md for the debt taxonomy, detection heuristics, the impact/effort rubric, and the finding output format before scanning, and to treat the shared working tree and its git index as read-only β€” any empirical check runs in an isolated git worktree created under $TMPDIR and discarded afterward. HEAD stays where it is: read other refs with git show <ref>:<path> rather than git checkout or git switch. Refer to that worktree by absolute path in every command and join chained steps with &&, so a failed step cannot leave the rest running in the shared checkout. Run teardown and verification as their own commands. Give that worktree its own dependency install rather than reaching the shared tree's install by any route: removing a worktree deletes through symlinks, and a redirected suite writes into the shared install. When its own install is not possible, the check is left unrun and reported as such. Every test runner the agent starts, in a worktree or in the shared checkout, runs in its own process group under a timeout enforced from outside the runner. Before teardown, the agent stops the process group of every runner it started, since stopping a runner can leave the processes it spawned alive. Afterward the agent verifies that git worktree list no longer shows the worktree, that git status --short is clean, that HEAD is still on the branch it started on, and that the shared tree's dependency directory still resolves (a destroyed install leaves git status clean, since it is gitignored). It also confirms that no process from those groups, and none whose command line names the worktree path, if any, is still running, and reports by PID any process it could not stop. When it cannot list processes, it reports that check as unrun and names those process groups and the worktree path, if any. Damage the agent cannot repair is reported with the exact repair command in place of findings.

Expect (one per partition, plus one project-wide architecture agent) Agent tool calls total. State the count explicitly when emitting the calls.

  • Partition agents β€” one per partition from Step 1. Each scans its files for complexity hotspots, deprecated API usage, duplication, and low-value tests, and notes coupling it observes reaching outside the partition. Pass the partition's file list and the full project root path.
  • Architecture agent β€” one project-wide pass over the scoped tree for architecture rot: tangled module boundaries, circular dependencies, layering violations, and refactor candidates that span modules. Pass the partition map and the full project root path.

If more partitions exist than fit a single fan-out, group related directories so the partition agents stay within a manageable batch, and note the grouping in the report.

Step 3: Run /evaluate-findings Skill

Aggregate all findings from all agents. Deduplicate items that surface in more than one agent (e.g., duplication a partition agent and the architecture agent both flag). Run the /evaluate-findings skill once on the combined set to verify each finding against the actual code and weed out false positives.

Step 4: Resolve Escalated Findings

Skip when Step 3 assigned no Escalate verdict.

For each finding with an Escalate verdict, output its technical detail as text first, including the fact that forces the choice. Then use AskUserQuestion to state the question as the decision the user owns, offering the report outcomes the finding leaves open:

  • Rank as debt β€” the finding enters the priority matrix with its recommended refactor. When the finding carries competing refactors, offer each as its own option.
  • Record as intentional β€” the finding stays out of the priority matrix and is listed with the decision
  • Get a second opinion β€” run the /consult-codex skill for a second opinion on the choice, then ask again with that answer in hand. Offer it when the choice is costly to reverse (it establishes a pattern others will follow, defines an interface, or commits to a data shape), and whenever no option earns (Recommended) with conviction.

Place the strongest option first and append (Recommended) to its label. When the choice hinges on product intent or domain knowledge you lack, say so instead of forcing a pick. Keep the question within four options: when the outcomes exceed that, offer the ones that fit the finding best, with the consultation option among them when it applies.

Step 5: Rank and Write Markdown Report

Assign each surviving finding an impact (maintenance drag, change risk, blast radius) and an effort (rough refactor size) per the rubric in references/debt-reviewer.md. Sort findings into priority tiers:

  • Quick wins β€” high impact, low effort
  • Strategic refactors β€” high impact, high effort
  • Incremental β€” low-to-medium impact, low effort
  • Defer β€” low impact, high effort

Leave a finding recorded as intentional in Step 4 out of the Summary counts and the Priority Matrix, and list it under its dimension in Detailed Findings with that decision. Record the decision beside each escalated finding ranked as debt as well.

Output the summary and priority matrix as text. Then write .turbo/technical-debt.md using the template below.

Report Template

# Technical Debt Assessment

**Date:** <date>
**Scope:** <what was assessed>

## Summary

| Dimension | Findings | High impact |
|---|---|---|
| Complexity hotspots | <N> | <N> |
| Deprecated API usage | <N> | <N> |
| Duplication clusters | <N> | <N> |
| Architecture rot | <N> | <N> |
| Low-value tests | <N> | <N> |

## Priority Matrix

Ranked by impact against refactor effort. Take quick wins first; schedule strategic refactors deliberately.

### Quick Wins (high impact, low effort)
| Item | Dimension | Location | Recommended refactor |
|---|---|---|---|

### Strategic Refactors (high impact, high effort)
| Item | Dimension | Location | Recommended refactor |
|---|---|---|---|

### Incremental (low–medium impact, low effort)
| Item | Dimension | Location | Recommended refactor |
|---|---|---|---|

### Defer (low impact, high effort)
| Item | Dimension | Location | Recommended refactor |
|---|---|---|---|

## Detailed Findings

### Complexity Hotspots
<findings: location, description, impact, effort, recommended refactor, and the recorded decision for an escalated finding>

### Deprecated API Usage
<findings>

### Duplication Clusters
<findings>

### Architecture Rot
<findings>

### Low-Value Tests
<findings>

---
This assessment covers in-code structural debt. For dependency freshness and diff-scoped bugs, run `/review-dependencies` and `/review-code`.

Step 6: Generate HTML Report

Convert the markdown report into a styled, interactive HTML page.

  1. Run the /frontend-design skill to load design principles.
  2. Read .turbo/technical-debt.md for the full report content.
  3. Write a self-contained .turbo/technical-debt.html (single file, no external dependencies beyond Google Fonts) that presents all findings from the markdown report with:
    • Summary grid with per-dimension finding counts
    • Priority matrix laid out as an impact-by-effort quadrant, color-coded by tier (quick wins highlighted)
    • Sticky navigation between sections
    • Collapsible dimension sections
    • [hidden] { display: none !important; } in the base styles, so a section whose own CSS sets a display value still hides
    • Finding cards with location, impact, effort, recommended refactor, and the recorded decision where one exists
    • Impact and effort badges with color-coding
    • Entrance animations and hover states
    • Print-friendly styles via @media print
    • Responsive layout for mobile

Then use the TaskList tool and proceed to any remaining task.

Rules

  • Analysis-only: do not modify source code, stage files, or commit.
  • If no significant debt is found, report that explicitly and note any scope limitations or analysis caveats.

Related Skills

View on GitHub
GitHub Stars405
CategoryDevelopment
Updated12d ago
Forks31

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit β€” see the Safety scan above for what the skill file itself contains.

No cautions
assess-technical-debt β€” Universal Skill: Install & Safety Check | SkillAgent