incremental-implementation

incremental-implementation is a skill for Claude Code, Codex from Djtony707/TITAN. It costs 52 tokens per session (1,940 once invoked), scanned A, a copy of incremental-implementation, MIT.

A coding workflow for making large changes in small, working pieces. Each piece is tested and checked before the next one is added.

In plain words
What is it for?
Use it for features, refactors, or other changes spread across several files. It helps plan each slice, test it, verify the result, and then continue.
Why use it?
It reduces the risk of building too much code before discovering a mistake. Smaller steps make failures easier to find and fix.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/djtony707/titan/incremental-implementation
Any agent
npx skills add Djtony707/TITAN --skill incremental-implementation
Clone the repo
git clone --depth 1 https://github.com/Djtony707/TITAN

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for incremental-implementation

README.md
[![agentmods](https://agentmods.dev/badge/skills/djtony707/titan/incremental-implementation.svg)](https://agentmods.dev/skills/djtony707/titan/incremental-implementation)
Your own site
<a href="https://agentmods.dev/skills/djtony707/titan/incremental-implementation"><img src="https://agentmods.dev/badge/skills/djtony707/titan/incremental-implementation.svg" alt="Measured on agentmods" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,940 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00052 $0.01940
Opus 5 $0.00026 $0.00970
Sonnet 5 $0.00010 $0.00388
Haiku 4.5 $0.00005 $0.00194

Measured 5d ago against content hash 5f16adf54233, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 5d 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.

Origin

This is a copy

89% identical to incremental-implementation — 18 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

assets/agent-skills/incremental-implementation/SKILL.md · 246 lines

How it starts

The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Incremental Implementation

Overview

Build in thin vertical slices — implement one piece, test it, verify it, then expand. Avoid implementing an entire feature in one pass. Each increment should leave the system in a working, testable state. This is the execution discipline that makes large features manageable.

When to Use

  • Implementing any multi-file change
  • Building a new feature from a task breakdown
  • Refactoring existing code
  • Any time you're tempted to write more than ~100 lines before testing

When NOT to use: Single-file, single-function changes where the scope is already minimal.

The Increment Cycle

┌──────────────────────────────────────┐
│                                      │
│   Implement ──→ Test ──→ Verify ──┐  │
│       ▲                           │  │
│       └───── Commit ◄─────────────┘  │
│              │                       │
│              ▼                       │
│          Next slice                  │
│                                      │
└──────────────────────────────────────┘

For each slice:

  1. Implement the smallest complete piece of functionality
  2. Test — run the test suite (or write a test if none exists)
  3. Verify — confirm the slice works as expected (tests pass, build succeeds, manual check)
  4. Commit -- save your progress with a descriptive message (see git-workflow-and-versioning for atomic commit guidance)
  5. Move to the next slice — carry forward, don't restart

Slicing Strategies

Vertical Slices (Preferred)

Build one complete path through the stack:

Slice 1: Create a task (DB + API + basic UI)
    → Tests pass, user can create a task via the UI

Slice 2: List tasks (query + API + UI)
    → Tests pass, user can see their tasks

Slice 3: Edit a task (update + API + UI)
    → Tests pass, user can modify tasks

Slice 4: Delete a task (delete + API + UI + confirmation)
    → Tests pass, full CRUD complete

Each slice delivers working end-to-end functionality.

Read the full file on GitHub · 246 lines

Changes

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.

  1. 5d ago First seen · 246 lines · 52 tokens per session scan A 5f16adf54233

Subscribe to this mod's changes

incremental-implementation is a skill published in the GitHub repository Djtony707/TITAN (19 stars, last pushed 2d ago), licensed MIT. It adds 52 tokens to every session and 1,940 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to incremental-implementation, differing in 18 lines, and is treated as a copy.

Related

Other skills, from other repositories

edgeone skill scanner

Scan any agent skill for security risks before you install or use it. Powered by Tencent Zhuque Lab A.I.G (AI-Infra-Guard). 100% local static analysis — no file contents or credentials leave your device. Compatible with CodeBuddy, Cursor, Windsurf, Claude Code, OpenClaw and more. Triggers on: 这个 skill 安全吗, skill 安全扫描…

Tencent/AI-Infra-Guard · 148 tokens

continual-learning

Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt…

langchain-ai/open-swe · 89 tokens

nano-banana-pro-openrouter

Deterministic OpenRouter image generation adapter for Nano Banana Pro / Gemini image models. Use as skillexec when a meta-skill needs local image files and structured IMAGEREADY records without spawning an LLM agent.

opensquilla/opensquilla · 49 tokens

skill-creator-linter

Internal tool (not user-invocable). Called by meta-skill-creator as a DAG step (kind: agent) to lint a candidate meta-skill SKILL.md against G1 (parse + reference check + xmlescape grep + structural lint) and G2 (scheduler dry-run with stub executors). Deterministic, sub-second, no LLM. Returns JSON diagnostics.

opensquilla/opensquilla · 84 tokens

paper-abstract-author

Write the abstract after the paper body has been revised, using the final claims and evidence.

opensquilla/opensquilla · 23 tokens

statsmodels

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use…

synthetic-sciences/openscience · 65 tokens