Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jaktestowac/awesome-copilot-for-testers --skill tech-debt-analysisgit clone --depth 1 https://github.com/jaktestowac/awesome-copilot-for-testersWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jaktestowac/awesome-copilot-for-testers/tech-debt-analysis)<a href="https://agentmods.dev/skills/jaktestowac/awesome-copilot-for-testers/tech-debt-analysis"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/tech-debt-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jaktestowac/awesome-copilot-for-testers/tech-debt-analysis"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/tech-debt-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.01613 |
| Opus 5 | $0.00039 | $0.00807 |
| Sonnet 5 | $0.00015 | $0.00323 |
| Haiku 4.5 | $0.00008 | $0.00161 |
Grade A, and why
tech-debt-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Debt Analysis
Use this skill when a user needs to understand the technical debt in a codebase, test suite, architecture, or delivery process. It is designed for evidence-driven analysis that surfaces the real drivers of delivery drag, quality risk, and maintenance cost, rather than relying on intuition or vague impressions. The output is a structured report that prioritizes debt items based on severity, remediation risk, effort, and ROI, with actionable recommendations for next steps.
When to Use
Use this skill when the user asks for things like:
- "audit this repository for technical debt"
- "why is delivery slowing down in this codebase?"
- "identify the biggest quality risks before we refactor"
- "analyze why our tests are slow, flaky, or hard to maintain"
- "prepare a technical debt roadmap for the next quarter"
- "what should we fix now versus consciously defer?"
Typical scenarios:
- repo-wide health assessment
- module-specific debt audit
- test-suite debt analysis
- pre-migration or pre-upgrade assessment
- roadmap planning for refactoring and quality work
Analysis Principles
- Evidence before verdict - observable signals come first; conclusions come second
- Debt is a trade-off, not a moral failure - explain the cost, not just the flaw
- Root cause over symptom - identify why the debt exists, not only where it hurts
- Risk-aware, not risk-averse - high-risk debt should be surfaced, not politely ignored
- Incremental remediation - prefer staged fixes with safety nets over heroic rewrites
- Test debt is first-class debt - unreliable tests damage delivery as much as bad production code
- Explicit uncertainty - if context is missing, state assumptions rather than bluffing
Analysis Workflow
Follow these phases in order.
Phase 0: Frame the analysis
Before scanning the codebase, clarify:
- scope: whole repository, subsystem, module, or test layer
- audience: engineering team, tech lead, QA lead, staff engineer, or management
- goal: awareness, prioritization, roadmap planning, release risk, or refactor planning
- constraints: legacy boundaries, compliance, time pressure, "no refactor now", or team capacity
- pain signals: regressions, slow CI, flaky tests, upgrade blockers, onboarding friction, or architecture sprawl
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 188 lines · 77 tokens per session scan A 08fe64153346
tech-debt-analysis is a skill published in the GitHub repository jaktestowac/awesome-copilot-for-testers (113 stars, last pushed 13d ago), licensed MIT. It adds 77 tokens to every session and 1,613 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
gentle-ai-collab-perfect
Trigger: contributing to Gentleman-Programming/gentle-ai as an external collaborator. Strict issue-first workflow, honest PR bodies, contributor-vs-maintainer scope, chained-PR strategy, verification protocol, docstring coverage. Load whenever the active repo is Gentleman-Programming/gentle-ai and any part of the…
issue-creation
Trigger: issue creation, bug reports, feature requests, or issue approval. Create and triage GitHub issues from repository evidence.
sdd-tasks
Break an SDD change into implementation tasks. Trigger: orchestrator launches task planning for a change.
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
cognitive-doc-design
Design docs that reduce cognitive load. Trigger: writing guides, READMEs, RFCs, onboarding, architecture, or review-facing docs.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.