Agent Skills for Context Engineering is a collection of reusable instructions that teach AI agents how to manage their context, coordinate multi-agent systems, and evaluate behavior. Developers use it when building or debugging production agent systems, and the catalogue entries are skills, agents, instructions, and a plugin from this collection.
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 muratcankoylan/Agent-Skills-for-Context-Engineering --skill harness-engineeringgit clone --depth 1 https://github.com/muratcankoylan/Agent-Skills-for-Context-EngineeringWrote 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/muratcankoylan/agent-skills-for-context-engineering/harness-engineering)<a href="https://agentmods.dev/skills/muratcankoylan/agent-skills-for-context-engineering/harness-engineering"><img src="https://agentmods.dev/badge/skills/muratcankoylan/agent-skills-for-context-engineering/harness-engineering/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/muratcankoylan/agent-skills-for-context-engineering/harness-engineering"><img src="https://agentmods.dev/badge/skills/muratcankoylan/agent-skills-for-context-engineering/harness-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 182 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 144 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00047 | $0.02297 |
| Opus 5.5 | $0.00019 | $0.00919 |
| Sonnet 5.5 | $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 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- harness-engineering — 100% identical, 0 lines differ
- harness-engineering — 100% identical, 0 lines differ
- 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.
- 1mo ago 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 (18,180 stars, last pushed 9d ago), licensed MIT. It adds 47 tokens to every session and 2,297 once invoked, about $0.0002 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 2026-08-30.
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