Norn: Skill for Claude Code

.agents/skills/full-output-enforcement/SKILL.md

full-output-enforcement is a skill for Claude Code, Codex from samad001z/Norn. It costs 44 tokens per session (568 once invoked), scanned A, a copy of full-output-enforcement, MIT.

A writing rule for producing complete code and other exhaustive output without placeholders or omitted sections. It also defines how to split large answers across token limits.

In plain words
What is it for?
Use it for tasks that require full files, several complete components, or other output where missing code would make the result unusable.
Why use it?
It helps prevent partial implementations, skipped repeated sections, and placeholder comments from being mistaken for finished work.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is samad001z/Norn's own configuration. It tells Claude Code and Codex how to work on Norn itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Norn configures →

Reuse

Borrowing it

Nothing to install: this file belongs to samad001z/Norn. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/samad001z/Norn/main/.agents/skills/full-output-enforcement/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/samad001z/Norn

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 full-output-enforcement

README.md
[![agentmods](https://agentmods.dev/badge/skills/samad001z/norn/full-output-enforcement/github.svg)](https://agentmods.dev/skills/samad001z/norn/full-output-enforcement)
Your own site
<a href="https://agentmods.dev/skills/samad001z/norn/full-output-enforcement"><img src="https://agentmods.dev/badge/skills/samad001z/norn/full-output-enforcement/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.

agentmods 80×15 button for full-output-enforcement

Your own site · 80×15
<a href="https://agentmods.dev/skills/samad001z/norn/full-output-enforcement"><img src="https://agentmods.dev/badge/skills/samad001z/norn/full-output-enforcement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 568 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% 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 5.1 $0.00044 $0.00568
Opus 5 $0.00022 $0.00284
Sonnet 5 $0.00009 $0.00114
Haiku 4.5 $0.00004 $0.00057

Measured 12d ago against content hash 7b2275b591af, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

full-output-enforcement 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 12d 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

100% identical to full-output-enforcement — 98 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.

.agents/skills/full-output-enforcement/SKILL.md · 50 lines

How it starts

The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Full-Output Enforcement

Baseline

Treat every task as production-critical. A partial output is a broken output. Do not optimize for brevity — optimize for completeness. If the user asks for a full file, deliver the full file. If the user asks for 5 components, deliver 5 components. No exceptions.

Banned Output Patterns

The following patterns are hard failures. Never produce them:

In code blocks: // ..., // rest of code, // implement here, // TODO, /* ... */, // similar to above, // continue pattern, // add more as needed, bare ... standing in for omitted code

In prose: "Let me know if you want me to continue", "I can provide more details if needed", "for brevity", "the rest follows the same pattern", "similarly for the remaining", "and so on" (when replacing actual content), "I'll leave that as an exercise"

Structural shortcuts: Outputting a skeleton when the request was for a full implementation. Showing the first and last section while skipping the middle. Replacing repeated logic with one example and a description. Describing what code should do instead of writing it.

Execution Process

  1. Scope — Read the full request. Count how many distinct deliverables are expected (files, functions, sections, answers). Lock that number.
  2. Build — Generate every deliverable completely. No partial drafts, no "you can extend this later."
  3. Cross-check — Before output, re-read the original request. Compare your deliverable count against the scope count. If anything is missing, add it before responding.

Handling Long Outputs

When a response approaches the token limit:

  • Do not compress remaining sections to squeeze them in.
  • Do not skip ahead to a conclusion.
  • Write at full quality up to a clean breakpoint (end of a function, end of a file, end of a section).
  • End with:
[PAUSED — X of Y complete. Send "continue" to resume from: next section name]

On "continue", pick up exactly where you stopped. No recap, no repetition.

Read the full file on GitHub · 50 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. 12d ago First seen · 50 lines · 44 tokens per session scan A 7b2275b591af

Subscribe to this mod's changes

full-output-enforcement is a skill published in the GitHub repository samad001z/Norn (3 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 568 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to full-output-enforcement, differing in 98 lines, and is treated as a copy.

Related

Other skills, from other repositories

dashboard

Open OwnMem Console, the local dashboard for this repository's memory. Use when the user asks to open the dashboard, see memory metrics, check adoption or recall quality, or set up the optional embedding lane. Requires a repository initialized with the dashboard layer.

grpcer/ownmem · 53 tokens

recall

Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.

grpcer/ownmem · 66 tokens

init

Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.

grpcer/ownmem · 43 tokens

qdrant-clients-sdk

Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.

qdrant/skills · 28 tokens

qdrant-hybrid-search-prefetches

Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'which sparse embedding model to…

qdrant/skills · 81 tokens

qdrant-relevance-feedback

Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense search relevance/recall', 'how to discover/get more relevant results from vector search', 'cheaper/better alternative…

qdrant/skills · 146 tokens