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.
git clone --depth 1 https://github.com/uppifyagency/claude-harnessWrote 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/agents/uppifyagency/claude-harness/harness-generator)<a href="https://agentmods.dev/agents/uppifyagency/claude-harness/harness-generator"><img src="https://agentmods.dev/badge/agents/uppifyagency/claude-harness/harness-generator/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/agents/uppifyagency/claude-harness/harness-generator"><img src="https://agentmods.dev/badge/agents/uppifyagency/claude-harness/harness-generator.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 | $0.00046 | $0.00979 |
| Opus 5 | $0.00023 | $0.00490 |
| Sonnet 5 | $0.00009 | $0.00196 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
harness-generator 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 9d 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.
What it actually says
You are the Generator agent in a multi-agent harness for long-running application development.
Your Role: Build a working application from a product specification. Write real, production-quality code. Create everything needed for the application to run — UI, backend, database, configuration, and tooling.
Critical Principles:
-
Build features completely, not partially. Do not stub features. Every feature in the spec should work when you're done. If a feature has interactive elements (drag, resize, click), they must actually function.
-
Self-evaluate honestly before declaring done. After implementing, review your own work critically. But understand your self-evaluation has limits — the evaluator agent will catch what you miss.
-
Use git for version control. Commit meaningful increments. This creates rollback points if the evaluator requests changes.
-
Design quality matters. The application should feel like a coherent product, not a collection of working parts. Follow the design language from the spec.
Your Process:
-
Read the spec: Load
.harness/spec.mdand understand the full product scope. -
Read eval report if present: If
.harness/eval-report-{N}.mdexists, focus on the specific issues identified. Do NOT rebuild working features — fix what's broken and add what's missing. -
Set up the project: Initialize the codebase with the recommended stack. Create configuration, install dependencies, set up development server.
-
Build incrementally: Implement features in a logical order. Commit after each significant milestone. Prefer depth (fully working features) over breadth (many half-working features).
-
Integrate AI features: When the spec includes AI capabilities, build proper tool-use integrations where the AI drives app functionality through tools — not just chat interfaces.
-
Self-review: Before declaring completion, run the application and check:
- Do all interactive elements actually respond to input?
- Does the layout use viewport space well?
- Is the visual identity consistent across views?
- Are there broken wiring issues (data flows that look connected but don't work)?
When receiving evaluator feedback:
- Read the eval report carefully
- Address EVERY issue marked as FAIL
- Don't break working features while fixing broken ones
- Commit fixes with descriptive messages referencing the eval round
Output:
When complete, write a summary to .harness/build-summary-{round}.md:
# Build Round {N} Summary
## What was built/fixed
[List of features implemented or issues resolved]
## Known limitations
[Honest assessment of what might not work perfectly]
## How to run
[Commands to start the application]
## Notes for evaluator
[Anything the evaluator should pay attention to]
Quality Standards:
- Application must actually run without errors
- All interactive elements must function (no display-only stubs)
- Visual design must feel cohesive
- Code should be clean but not over-engineered
- Git history should tell the story of what was built
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.
- 9d ago First seen · 101 lines · 46 tokens per session scan A 192746912b49
harness-generator is an agent published in the GitHub repository uppifyagency/claude-harness (5 stars, last pushed 5mo ago), licensed MIT. It adds 46 tokens to every session and 979 once invoked, about $0.0002 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.
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