discipline

discipline is a command for coding agents from naimkatiman/continuous-improvement. It costs 15 tokens per session (908 once invoked), scanned A, original, MIT.

A quick-reference card listing seven rules for disciplined AI-assisted coding, from researching first to learning from each session.

In plain words
What is it for?
Use it as a checklist before, during, and after coding work to guide research, planning, verification, focused changes, and reflection.
Why use it?
It helps reduce unverified changes, uncontrolled scope, skipped checks, and repeated mistakes.

Command

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 commands/naimkatiman/continuous-improvement/discipline
Clone the repo
git clone --depth 1 https://github.com/naimkatiman/continuous-improvement

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 discipline

README.md
[![agentmods](https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/discipline.svg)](https://agentmods.dev/commands/naimkatiman/continuous-improvement/discipline)
Your own site
<a href="https://agentmods.dev/commands/naimkatiman/continuous-improvement/discipline"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/discipline.svg" alt="Measured on agentmods" height="20"></a>
Per session 15 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 908 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00015 $0.00908
Opus 5 $0.00008 $0.00454
Sonnet 5 $0.00003 $0.00182
Haiku 4.5 $0.00002 $0.00091

Measured 4d ago against content hash cc7174fb1bf5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

discipline 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 4d 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.

commands/discipline.md · 66 lines

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.

The 7 Laws — Quick Reference

Print this card and check yourself against each law.

The Laws

# Law Check Red Flag
1 Research Before Executing Did I search for existing solutions? "I'll just quickly..."
2 Plan Is Sacred Did I state WILL / WILL NOT / VERIFY? "Let me also add..."
3 One Thing at a Time Am I finishing before starting? "While I'm here..."
4 Verify Before Reporting Did I check the ACTUAL output? "This should work..."
5 Reflect After Sessions Did I note what worked/failed? "I'll remember..."
6 Iterate One Change Am I changing one thing at a time? "And also..."
7 Learn From Every Session Did I capture this as an instinct? "Next time I'll..."

Operator Stakes

The Laws above are the how. These five principles are the why: code ships from your account, the incident lands on your pager, the bill hits your budget. Each one pairs with the Law that prevents it from going wrong.

# Principle Vibe coder Engineer Law
1 Ownership Ships auth, moves on Adds rate limits, audit logs, password-reset flow, incident runbook before shipping 4
2 Reliability over cleverness Accepts a clever regex + heavy lib that breaks on ISO 8601 with millis Picks the boring tested API, writes tests for leap years and DST 1
3 Systems thinking Builds in-memory CSV export, works for 100 dev users, OOMs in prod Asks row count first, picks paginated background job + S3 link 2
4 Problem framing Builds the websocket chat the ticket asked for Finds out users wanted faster support replies, not chat 1
5 Constraints management Calls the $0.02/image model on every upload Does the math, adds client-side validation + caching + cheaper triage model 2

Code is a liability, not an asset. Speed without these five turns into someone else's incident at 3am — except the someone is you.

Read the full file on GitHub · 66 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. 4d ago First seen · 66 lines · 15 tokens per session scan A cc7174fb1bf5

Subscribe to this mod's changes

discipline is a command published in the GitHub repository naimkatiman/continuous-improvement (7 stars, last pushed 9d ago), licensed MIT. It adds 15 tokens to every session and 908 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.