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 andr-ca/agentharness --skill refactoringgit clone --depth 1 https://github.com/andr-ca/agentharnessWrote 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/andr-ca/agentharness/refactoring)<a href="https://agentmods.dev/skills/andr-ca/agentharness/refactoring"><img src="https://agentmods.dev/badge/skills/andr-ca/agentharness/refactoring.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 | $0.00039 | $0.01500 |
| Opus 5 | $0.00019 | $0.00750 |
| Sonnet 5 | $0.00008 | $0.00300 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
refactoring 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 3d 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refactoring Safely
Safe restructuring, behavior-preservation, protected contracts. A refactoring changes code structure, not behavior. If behavior changes, that's a feature.
One Core Rule: Behavior Preservation
Before you refactor, decide: Am I changing code structure only, or am I changing behavior?
- Refactoring: Same inputs → same outputs. Same errors in same cases. Observable behavior identical. Only code structure changes.
- Feature: Behavior, performance, API, or error handling changes. Needs a separate PR with tests and design review.
If unsure, assume it's a feature (safer choice).
Document what must not change:
- Function/endpoint signatures
- Error modes (same exceptions in same conditions)
- Performance characteristics (if timing tests exist, they still pass)
- Database schemas (unless versioned migration)
Three-Step Safe Refactoring
1. Characterization Tests (Before Touching Code)
Write tests that capture behavior as it is today — your safeguard against accidentally changing something.
# ✅ Characterization test: lock down current behavior
def test_find_user_missing_returns_none():
"""Current behavior: missing user returns None, not exception."""
db = TestDatabase()
assert find_user(db, 999) is None
def test_retry_once_on_network_error():
"""Current behavior: network errors trigger exactly one retry."""
db = MockDB(fails_on_attempt=1)
user = find_user(db, 1)
assert user.id == 1 # Succeeds on retry
These tests answer: "What does this code actually do right now?" — not "what should it do?" They're your canary: if they break during refactoring, you've changed behavior (either a bug or a missed feature scope).
When required (Production tier): Large refactors (5+ functions, core algorithms), renaming public interfaces, extracting components.
When optional: Obvious renames (loop variables), extracting pure helpers, changes your existing test suite already covers.
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.
- 3d ago First seen · 212 lines · 39 tokens per session scan A 9a4e34881e01
refactoring is a skill published in the GitHub repository andr-ca/agentharness (1 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,500 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
security-audit
Security review and hardening workflow — root-cause analysis of vulnerabilities, authentication and authorization checks, least privilege, input handling, secret hygiene, and security regression tests. Use when reviewing code for security, fixing a vulnerability, hardening a feature, or handling auth, permissions…
test-gap-review
Review whether existing tests and verification credibly prove a scoped behavior contract. Use during validation when evidence may omit important behavior or provide false confidence.
review
Challenge a Hardproof implementation during REVIEW against its approved contract, code quality, tests, and risk boundaries.
crap-index-assess
Assess method-level change risk with the Change Risk Anti-Patterns (CRAP) index and recommend focused testing or complexity reduction. Use when a repository configures CRAP measurement, cyclomatic complexity and automated test coverage data are available, or a user asks what CRAP means, how to calculate or interpret…
behavior-implement
Implement behavior through a red-green-refactor cycle when focused automated tests are proportionate. Use for product behavior changes and for engineering tooling and infrastructure only when native checks are insufficient and concrete complexity or failure risk warrants dedicated tests.
test-design
Design or write reproducible tests matched to behavioral risk before implementation. Use for product behavior and for engineering tooling and infrastructure only when native checks are insufficient and concrete complexity or failure risk warrants proportionate dedicated tests.