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 agentmods add skills/muratcankoylan/agent-skills-for-context-engineering/harness-engineeringnpx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill harness-engineeringgit clone --depth 1 https://github.com/muratcankoylan/Agent-Skills-for-Context-EngineeringWhat 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 | $0.00047 | $0.02297 |
| Opus 5 | $0.00023 | $0.01149 |
| Sonnet 5 | $0.00009 | $0.00459 |
| Haiku 4.5 | $0.00005 | $0.00230 |
Grade A, and why
harness-engineering 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- harness-engineering — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harness Engineering
Harness engineering designs the control system around an agent: what it may edit, how it receives feedback, where it writes state, how failures recover, and who can approve irreversible actions. The harness is the difference between a helpful agent session and an autonomous loop that can run for days without corrupting its objective.
When to Activate
Activate this skill when:
- Building autonomous research or experimentation loops
- Designing an agent environment with locked metrics and editable code or content
- Creating PR-producing or background agents
- Evaluating whether an agent can safely run without frequent human prompts
- Adding novelty, ablation, pruning, rollback, or durable logging to an agent workflow
- Preventing agents from gaming benchmarks, weakening rubrics, or losing state across compaction
Do not activate this skill for adjacent work owned by other skills:
- General quality gates, regression suites, or outcome metrics without autonomous control surfaces:
evaluation. - Tool schemas, response formats, and recovery errors for harness tools:
tool-design. - Project-level task-model fit, pipeline shape, and cost planning:
project-development. - Remote sandbox, warm-pool, and hosted session infrastructure:
hosted-agents.
Core Concepts
Harness Boundary
Separate the agent from the environment it operates inside. The agent proposes actions; the harness defines allowed surfaces, feedback, persistence, and promotion rules.
Use four surface classes:
| Surface | Examples | Rule |
|---|---|---|
| Locked | Eval metric, rubric, validation script, merge policy | Agent may read and propose changes, but cannot score itself with modified rules |
| Editable | Skill draft, experiment file, prompt, config under test | Agent may mutate during the loop |
| Append-only | Results log, research thread, rejected ideas | Agent may append, not rewrite |
| Human-controlled | Merge, production deploy, credentials, destructive operations | Requires explicit human approval |
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 First seen · 235 lines · 47 tokens per session scan A dde3d8f46d4d
harness-engineering is a skill published in the GitHub repository muratcankoylan/Agent-Skills-for-Context-Engineering (17,868 stars, last pushed 13d ago), licensed MIT. It adds 47 tokens to every session and 2,297 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…