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Tech Debt

Community
davekilleen
tech-debt

Use when technical debt needs linked code or operational evidence, deduplication, first-seen provenance, or impact, effort, confidence, and cost-of-delay prioritization.

Overview

Publisherdavekilleen
RepositoryDex
Skill nametech-debt
Stars
481
Forks
130
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by davekilleen on GitHub. Read the source before you install it.

Installation

Install the Tech Debt AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/davekilleen/Dex.git /tmp/Dex
mkdir -p .claude/skills
cp -r /tmp/Dex/packages/dex-agent-plugin/skills/_available/engineering/tech-debt .claude/skills/tech-debt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tech Debt in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tech Debt on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tech Debt is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Technical debt review

When to use

Use this skill when a component, project, or portfolio needs a defensible technical- debt inventory, duplicate cleanup, cost-of-delay comparison, or security-risk review.

Do not use it to modify code, close tickets, set a sprint commitment, or certify a security issue as safe. Not for calling ordinary feature requests or unsupported complaints technical debt without linked evidence.

Inputs and source discipline

Set the review scope and as-of date/time. Search code, configuration, tests, build logs, incident reviews, operational measurements, issue discussions, and dated notes. For every candidate capture a source, source date, as-of date/time, stable locator (path/line, commit, ticket, or record), and any freshness or access limitation. Linked code and operational evidence should identify the underlying problem, not just repeat a label such as "refactor later".

Keep first-seen provenance separate from current discovery. A dated source can support first-seen; an undated mention cannot establish age. Record first-seen: unknown when no dated evidence exists rather than guessing an age or inventing an aging bucket.

Method

  1. Gather candidates read-only and normalize each into one underlying debt item: affected component, debt mechanism, observable consequence, evidence links, and current scope. Keep a candidate if evidence is incomplete, but mark the gap.
  2. Deduplicate by underlying problem, not by similar words. Merge only when the linked evidence supports the same failure mode or constraint; preserve every source link, alternate wording, and contradictory observation in the merged record.
  3. Verify the evidence and first-seen date. Check whether code paths, runbooks, incidents, or operational symptoms still exist; mark stale evidence rather than deleting it. A first-seen date is not the date the debt began.
  4. Assess each item separately for impact, effort, confidence, and cost of delay. State the basis and unit for any estimate. Impact may cover reliability, delivery friction, user harm, or security; effort needs an explicit basis; confidence describes evidence quality; cost of delay describes what is likely lost by waiting. Use unknown where the evidence cannot support a rating.
  5. Check for security signals such as confidentiality, integrity, availability, authentication, authorization, or compliance exposure. A potential security issue needs security escalation through the approved human security path, with evidence, current status, and owner or TBD; do not bury it in a normal debt queue or invent severity.
  6. Group items into decision options and recommendations for human review. A recommendation is not a human decision; do not promise prioritization, money, staffing, or delivery dates without authority and evidence.

Truth and uncertainty rules

Label each item and rating as observed, inferred, unknown, stale, or contradictory as appropriate. An observed code path or incident is not proof of impact magnitude; an inferred risk is not a vulnerability finding. Preserve conflicting code and operational signals and state what additional evidence would resolve them.

Never invent dates, metrics, owners, intent, money, percentages, causes, status, or evidence. Do not use a first-seen date to claim the debt's true age, and do not turn a missing estimate into zero effort or low impact.

Output contract

Return an inventory with one stable item per underlying problem and, for each item:

  • summary, component, scope, and linked code or operational evidence;
  • all source citations with source date, as-of date/time, freshness, and first-seen date or unknown;
  • duplicate group and the evidence for merging or keeping items separate;
  • impact, effort, confidence, and cost of delay with basis and unknowns;
  • security signal, escalation path/status, and owner or TBD when relevant;
  • observed, inferred, stale, unknowns, and contradictions kept distinct; and
  • recommendation, sequencing option, and explicit statement that it is not a human decision.

Safety and write boundaries

The default is read-only. Do not edit code, create or close tickets, change security records, alter priorities, or notify an owner from this skill alone. For any requested write or escalation, preview the exact destination, content, and recipients; obtain explicit confirmation from the human authority; then perform only that confirmed action. Preserve source links and raw evidence when deduplicating.

Verification and recovery

Read back the inventory after a confirmed write and reconcile item count, duplicate groups, evidence links, first-seen fields, ratings, security flags, and source dates with the read-only extract. Re-check that stale or contradictory evidence was not silently removed and that no recommendation became status.

If a source link, read, write, or reconciliation check fails, stop and report the failed check and affected item. Do not retry blindly, delete a candidate, or merge records to hide the failure. Re-read the source and destination; recover with an append-only correction or human-confirmed update, retaining the previous evidence and marking the unresolved field unknown.

Frequently asked questions

What does the Tech Debt AI skill do?

Use when technical debt needs linked code or operational evidence, deduplication, first-seen provenance, or impact, effort, confidence, and cost-of-delay prioritization.

Why use Tech Debt on TypingMind?

Because you install it once and use it with any model. Tech Debt is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tech Debt in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davekilleen/Dex/tree/main/packages/dex-agent-plugin/skills/_available/engineering/tech-debt. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tech Debt?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tech Debt?

As many as you like. As long as a model supports skills, you can use Tech Debt with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tech Debt AI skill free?

It is published on GitHub by davekilleen. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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