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 JayRHa/AgentSkills --skill habit-buildergit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/habit-builder)<a href="https://agentmods.dev/skills/jayrha/agentskills/habit-builder"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/habit-builder/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/jayrha/agentskills/habit-builder"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/habit-builder.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00095 | $0.01123 |
| Opus 5.5 | $0.00038 | $0.00449 |
| Sonnet 5.5 | $0.00019 | $0.00225 |
| Haiku 4.5 | $0.00010 | $0.00112 |
Grade A, and why
habit-builder 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 1mo 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Habit Builder
Overview
This skill turns a vague intention ("I want to read more / stop doomscrolling") into a concrete behavior-change plan grounded in habit science — the cue→routine→reward loop, making good habits obvious/easy/attractive/satisfying and bad ones the opposite, habit stacking, environment design, and a tracking system that survives slip-ups.
Keywords: habit, routine, behavior change, consistency, habit stacking, atomic habits, morning routine, break a habit, accountability, streak, willpower, motivation.
When to use vs. not
Use this for everyday behavior change (exercise, reading, sleep, focus, screen time, diet habits). This is coaching, not clinical treatment — for addiction, self-harm, eating disorders, or compulsions causing real harm, recommend a qualified professional rather than a habit tracker.
Inputs to gather first
- The target behavior — specific and observable ("read 10 pages", not "be smarter").
- Build or break? And the why behind it (the identity/goal it serves).
- Current context: when/where it would happen, what's blocking it, past attempts.
- Existing routines to anchor onto (habit stacking).
- Realistic capacity — time, energy, competing demands.
Workflow
- Define the behavior tiny and specific. Shrink it until it's almost impossible to fail ("one push-up", "read one page"). Consistency first, scale later. The goal is to cast a vote for an identity ("I'm a reader"), not hit a number.
- Find the cue. Anchor the new habit to an existing one — habit stacking: "After I pour my morning coffee, I will write one sentence." Time + location + preceding action are the most reliable cues. See
references/habit-loop.md. - Design the environment. Make good habits obvious and easy (book on the pillow, gym clothes laid out) and bad habits invisible and hard (phone in another room, delete the app, unplug the TV). Environment beats willpower. See
references/environment-design.md. - Engineer the reward. Make it immediately satisfying — a tracker checkmark, a tiny celebration, pairing the habit with something enjoyable (temptation bundling). The brain repeats what feels good now.
- For breaking a habit, invert the loop. Make the cue invisible, the routine hard/unattractive, and remove the reward; replace the routine with a substitute that serves the same craving.
- Set up tracking + the never-miss-twice rule. A simple visual streak; missing once is fine, missing twice is the start of a new (bad) habit. Plan in advance for the obstacle that will break the streak (an if-then plan). See
references/tracking.md. - Schedule a review. Weekly check: did it happen? what got in the way? Adjust the cue, size, or environment — not your self-worth. Scale the habit up only once it's automatic.
What ships with it
3 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.
- 1mo ago First seen · 60 lines · 95 tokens per session scan A 8a11b6346f31
habit-builder is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 95 tokens to every session and 1,123 once invoked, about $0.0004 per session on Opus 5.5. 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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