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 vignesh2027/AI-AGENT-SKILLS --skill incremental-implementationgit clone --depth 1 https://github.com/vignesh2027/AI-AGENT-SKILLSWrote 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/vignesh2027/ai-agent-skills/incremental-implementation)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/incremental-implementation"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/incremental-implementation.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 | $0.00015 | $0.00600 |
| Opus 5 | $0.00008 | $0.00300 |
| Sonnet 5 | $0.00003 | $0.00120 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
incremental-implementation 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 7d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Incremental implementation is the practice of delivering working software in the smallest useful increments. Each increment is deployable, testable, and provides value or learning. The goal is to eliminate the "everything works or nothing works" state that characterizes big-bang development.
When to Use
- For any feature that will take more than 1 day to implement
- When you feel the urge to implement "the whole thing" before shipping any of it
- When designing a refactor that touches many files
Process
Step 1: Identify the minimum slice
Ask: what is the smallest version of this feature that proves the core hypothesis or delivers the core value? That is your first increment.
Step 2: Draw the vertical slice
A vertical slice cuts through all layers: UI → API → service → database → response. Implement all layers for the minimal slice before moving to the next feature.
Step 3: Build the walking skeleton
Implement the thinnest possible end-to-end version: returns hardcoded data, has no error handling, no edge cases. Make it work end-to-end first.
Step 4: Add tests for the skeleton
Write tests that cover the happy path of the walking skeleton. These tests will protect you as you add flesh to the skeleton.
Step 5: Flesh out the implementation
Add real data, error handling, and edge cases incrementally. Run tests after each addition.
Step 6: Keep each increment deployable
Every commit should leave the system in a deployable state. Use feature flags if necessary to hide incomplete work from end users.
Step 7: Review and ship each increment
Don't accumulate increments. Ship each one. Unreleased code is risk, not progress.
Anti-Rationalizations
"I need to build the whole thing before I can test it" This is the wrong architecture. If you can't test a slice without the whole system, redesign so you can.
"It's faster to build it all at once" It is faster to write code all at once. It is not faster to debug, review, and deploy code all at once.
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.
- 7d ago First seen · 66 lines · 15 tokens per session scan A 7d6ec69f08f2
incremental-implementation is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 9d ago), licensed MIT. It adds 15 tokens to every session and 600 once invoked, about $0.0001 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 2026-08-31.
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