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 agentmods add skills/nanparth/ai-skill-hub/tech-refactornpx skills add nanparth/ai-skill-hub --skill tech-refactorgit clone --depth 1 https://github.com/nanparth/ai-skill-hubWrote 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/nanparth/ai-skill-hub/tech-refactor)<a href="https://agentmods.dev/skills/nanparth/ai-skill-hub/tech-refactor"><img src="https://agentmods.dev/badge/skills/nanparth/ai-skill-hub/tech-refactor.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00067 | $0.00991 |
| Opus 5 | $0.00034 | $0.00495 |
| Sonnet 5 | $0.00013 | $0.00198 |
| Haiku 4.5 | $0.00007 | $0.00099 |
Grade A, and why
tech-refactor 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 6d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tech-refactor
Architectural overhaul workflow for existing systems. It produces an execution-ready roadmap before any high-risk code changes.
Core principle: deletion is the proof of centralization. Adding a new abstraction is not enough. The old access path must be removed before centralization is complete.
Not for: surgical cleanup, single-function improvements, extract-and-rename tasks, or small formatting cleanup.
Pipeline
Phase 1: Understand -> confidence-rated behavioral inventory
Phase 2: Audit -> risk findings and additive-bias detection
Phase 3: Clarify -> targeted questions when needed
Phase 4: Design -> target architecture, migration strategy, retirement plan
Phase 5: Roadmap -> execution-ready task list
Approval gate -> optional execution with tech-implement or manual TDD workflow
Workflow
Phase 1: Understand
- Read the target codebase: entrypoints, module structure, key files.
- Produce a confidence-rated inventory: high, medium, low.
- Include user-facing features, inputs, outputs, side effects, integrations, API shapes, wire formats, config keys, data models, duplicate implementations, tests, and protected behavior.
- Present the inventory to the user before proceeding.
Phase 2: Audit
Load references/architectural-risk-audit.md. Separate confirmed findings from plausible risks.
Check for additive refactor bias, competing sources of truth, partial refactor residue, runnable but wrong behavior, context drift, test coupling, unsafe operations, unused package dependencies, orphaned test infrastructure, and structure risks. Load shared/code-organization.md for structure risks.
Present findings to the user before design.
Phase 3: Clarify
Skip this phase if Phases 1 and 2 left no open unknowns. Ask only about low-confidence behavior, ambiguous business rules, non-code constraints, deployment environment, rollback tolerance, team constraints, or compliance.
Do not ask questions that are answerable from the codebase.
What ships with it
10 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.
- PORTABILITY.md 990 B
- references/architectural-risk-audit.md 7.4 KB
- references/code-smells.md 11 KB
- references/cross-boundary-refactoring.md 8.1 KB
- references/design-patterns.md 2.6 KB
- references/legacy-path-retirement.md 8.3 KB
- references/parse-dont-validate.md 8.7 KB
- references/test-patterns.md 1.5 KB
- shared/code-organization.md 2.5 KB
- tech-refactor-readme.md 4.7 KB
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.
- 6d ago First seen · 87 lines · 67 tokens per session scan A 336c1058cf80
tech-refactor is a skill published in the GitHub repository nanparth/ai-skill-hub (23 stars, last pushed 14d ago), licensed MIT. It adds 67 tokens to every session and 991 once invoked, about $0.0003 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.
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