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.
npx skills add jtsang4/efficient-coding --skill harnessgit clone --depth 1 https://github.com/jtsang4/efficient-codingWrote 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/jtsang4/efficient-coding/harness)<a href="https://agentmods.dev/skills/jtsang4/efficient-coding/harness"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/harness/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/jtsang4/efficient-coding/harness"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/harness.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 | $0.00198 | $0.02677 |
| Opus 5.5 | $0.00079 | $0.01071 |
| Sonnet 5.5 | $0.00040 | $0.00535 |
| Haiku 4.5 | $0.00020 | $0.00268 |
Grade A, and why
harness 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harness Engineering
Transform any code project so AI agents can work in it reliably and autonomously.
The bottleneck is never the agent's coding ability — it's the environment the agent operates in. This skill builds that environment.
Core Principles
These govern every decision:
-
Declarative over procedural — Define what "correct" looks like (verification criteria), not step-by-step procedures. Agents determine how to verify on their own and generate temporary scripts if needed.
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Recognition over recall — Extract knowledge by showing users detected patterns and asking them to react (choose, confirm, reject). Never ask open-ended "describe your principles" questions.
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Minimal context footprint — Every generated document must be as concise as possible. Agents read actual code for details. Only externalize knowledge that CANNOT be inferred from code: architectural rules, canonical pattern choices, prohibited actions, business domain terms.
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Persist invariants, not procedures — Commit architectural rules, lint configs, structural tests (permanent invariants). Never commit task-specific verification scripts — agents create those on-the-fly per task and discard them after.
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Language-agnostic — Adapt to the project's tech stack. Never assume a specific language, framework, or toolchain.
Workflow
Four phases. On re-runs (when docs/harness/.state.json exists), skip to gap analysis.
Phase 0: Automated Scan
No user interaction. Scan the codebase to build a baseline understanding.
What to detect:
| Category | How to detect |
|---|---|
| Language & framework | Glob for config files: package.json, Cargo.toml, go.mod, pyproject.toml, pom.xml, build.gradle, Makefile, CMakeLists.txt, etc. |
| Directory structure | ls top-level, identify src/, lib/, app/, tests/, docs/ etc. |
| Module boundaries | Top-level directories under source root; look for clear separation |
| Import/dependency graph | Grep for import statements. Language-specific patterns — see below |
| Convention files | Glob for CLAUDE.md, AGENTS.md, .cursorrules, .github/copilot-instructions.md, .windsurfrules |
| Test infrastructure | Glob for test config: jest.config.*, vitest.config.*, pytest.ini, pyproject.toml [tool.pytest], etc. |
| Lint configuration | Glob for lint config: .eslintrc.*, biome.json, .rubocop.yml, clippy.toml, ruff.toml, .golangci.yml, etc. |
| CI/CD | Glob for .github/workflows/, .gitlab-ci.yml, Jenkinsfile, etc. |
| Type system | Check for strict mode: tsconfig.json strict flag, mypy.ini, type annotations density |
| Existing harness | Check for docs/harness/ and .state.json |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 227 lines · 198 tokens per session scan A 0aebf15f91d1
harness is a skill published in the GitHub repository jtsang4/efficient-coding (2 stars, last pushed 8d ago), licensed MIT. It adds 198 tokens to every session and 2,677 once invoked, about $0.0008 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 2026-08-31.
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