Happier is an open-source, end-to-end encrypted client that lets people run AI coding-agent sessions on a computer and continue controlling them from a phone, browser, or desktop app. It is for developers who use agents such as Claude Code, Codex, Gemini, or OpenCode across multiple devices, with self-hosting available. The catalogue entries are add-ons for configuring or extending this client and its supported coding-agent workflows.
Borrowing it
Nothing to install: this file belongs to happier-dev/happier. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/happier-dev/happier/dev/.agents/skills/happier-instruction-eval/SKILL.mdgit clone --depth 1 https://github.com/happier-dev/happierWrote 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/happier-dev/happier/happier-instruction-eval)<a href="https://agentmods.dev/skills/happier-dev/happier/happier-instruction-eval"><img src="https://agentmods.dev/badge/skills/happier-dev/happier/happier-instruction-eval/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/happier-dev/happier/happier-instruction-eval"><img src="https://agentmods.dev/badge/skills/happier-dev/happier/happier-instruction-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00065 | $0.00940 |
| Opus 5.5 | $0.00026 | $0.00376 |
| Sonnet 5.5 | $0.00013 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
happier-instruction-eval 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Happier Instruction Evaluation
Evaluate whether instruction wording changes agent behavior without telling the agents they are being evaluated. This workflow is advisory: it produces evidence for a human decision and never edits the canonical instruction owner by itself.
1. Establish the decision
Name:
- the instruction owner and variants being compared, including the current baseline;
- the concrete behavior the change is meant to improve or failure it is meant to prevent;
- one organic task or a small risk-selected set of tasks that can expose that difference;
- a rubric of observable outcomes fixed before any run;
- the user decision the evidence will inform.
Do not evaluate prose elegance in isolation. A useful task forces the instruction to affect routing, investigation, ownership, implementation shape, validation, stopping, or reporting. Skip evaluation when a source inspection or deterministic check can decide the question directly.
2. Control the comparison
Hold constant everything except the instruction variant when practical:
- use the same task prompt, repository basis, allowed tools, permissions, time/effort budget, and available evidence;
- give each run only the ordinary context an agent would receive for that task;
- label variants and output locations neutrally so neither runner nor judge sees “baseline,” “preferred,” model identity, or another run's existence;
- isolate writes in separate temporary directories or authorized worktrees; never switch, clean, reset, stash, or overwrite the primary shared checkout;
- prevent external mutations, destructive actions, secrets, and sensitive-data access unless the user separately authorizes that exact evaluation surface.
Do not freeze or package a release representation. Temporary instruction variants and isolated outputs are test inputs, not release artifacts.
3. Keep runners blind
Each runner receives an organic-looking engineering request, not an evaluation brief. Do not mention the rubric, competing variants, expected lesson, or favored outcome. Do not ask the runner whether it followed the instruction.
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 Changed 408b9b639176
- 1mo ago First seen · 81 lines · 65 tokens per session scan A 6d6d488ade90
happier-instruction-eval is a skill published in the GitHub repository happier-dev/happier (2,072 stars, last pushed yesterday), licensed MIT. It adds 65 tokens to every session and 940 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
agtx-oneshot
One-shot a whole project on an agtx board: decompose the goal, run every task unattended, unblock the workers, and merge each one. Use when the user wants a long autonomous run rather than a single session.
agtx-sweep
Sweep this conversation into agtx tasks and push them to the kanban board. Use when the user wants to capture, decompose, or hand off conversation results to the agtx board.
agtx-execute
Execute an approved implementation plan. Implement the changes, then write a summary to .agtx/execute.md and stop.
agtx-plan
Plan a task implementation. Analyze the codebase, create a detailed plan, write it to .agtx/plan.md, then stop and wait for user approval before making any changes.
agtx-research
Explore the codebase to understand a task before planning. Write findings to .agtx/research.md and stop. This is a read-only exploration — do not modify any files.
agtx-review
Self-review completed work. Check for correctness, edge cases, and code quality. Write review to .agtx/review.md and stop.