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/verify-claims/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/verify-claims)<a href="https://agentmods.dev/skills/happier-dev/happier/verify-claims"><img src="https://agentmods.dev/badge/skills/happier-dev/happier/verify-claims.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.00601 |
| Opus 5 | $0.00036 | $0.00300 |
| Sonnet 5 | $0.00015 | $0.00120 |
| Haiku 4.5 | $0.00007 | $0.00060 |
Grade A, and why
verify-claims 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 yesterday.
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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify Claims
Take a report — a subagent's, a lane's, a plan's, or your own from earlier — and re-derive its load-bearing claims instead of trusting how they sound. Full doctrine: docs/agent-craft.md §4.
Procedure
- Extract the load-bearing claims — those whose falseness would change the decision being made. Ignore decoration; auditing everything dilutes the audit.
- Re-derive each from a primary source. Source hierarchy: running code > tests > docs > comments > memory. Each step down the ladder is a step toward hearsay.
- Use a different path than the claim arrived by. Claim from reading code → check with a runtime observation. Claim from a test → read the code the test exercises. Two derivations sharing a path share that path's blind spot.
- Re-measure every decision-material number. Re-run test results, coverage, timings, counts, or measurements only when that numeric claim changes the decision. Use the exact immutable commit/artifact when the claim names one; for current dirty work, measure the relevant current paths and acknowledge concurrency. Decorative counts are not evidence and should be removed from the conclusion.
- Treat plausibility as zero evidence. Narrative fit is what generated the claim, so "sounds right" is correlated with exactly the error being hunted. Check the best-fitting claims first, not last.
- Downgrade what you cannot verify. If re-derivation is too expensive, do not skip and do not trust: relabel the claim as an assumption and carry it labeled.
For claims of backward, forward, mixed-version, upgrade, or rollback compatibility, use .agents/skills/happier-compatibility. Re-derive the claim against the exact released tag/artifact or applicable predecessor worktree basis, the real old/new component roles, and every claimed direction. A current-code fixture or mock that merely agrees with the current implementation is not independent compatibility evidence.
Output
Each audited claim in one of three bins, with the evidence:
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.
- yesterday Changed baeb371117f6
- 3d ago First seen · 32 lines · 73 tokens per session scan A 7dcb7e2a3bbb
verify-claims is a skill published in the GitHub repository happier-dev/happier (1,624 stars, last pushed yesterday), licensed MIT. It adds 73 tokens to every session and 601 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
verify-change
变更校验关卡。分析代码变更，检测文档同步状态，评估变更影响范围。当用户提到变更检查、文档同步、代码审查、提交前检查、diff分析时使用。在设计级变更、重构完成时自动触发。.
verify-module
模块完整性校验关卡。扫描目录结构、检测缺失文档、验证代码与文档同步。当用户提到模块校验、文档检查、结构完整性、README检查、DESIGN检查时使用。在新建模块完成时自动触发。.
verify-quality
代码质量校验关卡。检测复杂度、重复代码、命名规范、函数长度等质量指标。当用户提到代码质量、复杂度检查、代码异味、重构建议、lint检查、代码规范时使用。在复杂模块、重构完成时自动触发。.
red-team-review
Unified adversarial review: v4.3 Strategic Matrix (MTA-004). 7-phase framework: Priors → Rubric → Adversarial Lenses → SWOT/TOWS → MCDA Decision Engine → Blind Spot/Kill Switch → Executive Summary. Absorbs: bias-detector.
decision-journal
Unified decision lifecycle: Pre-decision logging, post-decision review, failure classification, and calibration tracking. Absorbs: post-mortem-engine.
agtx-review
Self-review completed work. Check for correctness, edge cases, and code quality. Write review to .agtx/review.md and stop.