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 navendubrajesh/context-management-for-agents --skill harness-engineeringgit clone --depth 1 https://github.com/navendubrajesh/context-management-for-agentsWrote 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/navendubrajesh/context-management-for-agents/harness-engineering)<a href="https://agentmods.dev/skills/navendubrajesh/context-management-for-agents/harness-engineering"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/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/navendubrajesh/context-management-for-agents/harness-engineering"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/harness-engineering.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 | $0.00074 | $0.01493 |
| Opus 5 | $0.00037 | $0.00746 |
| Sonnet 5 | $0.00015 | $0.00299 |
| Haiku 4.5 | $0.00007 | $0.00149 |
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 11d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harness Engineering for Autonomous Agents
Design the operating loop that wraps an autonomous agent — the harness that manages execution, evaluates outputs, enforces safety, and provides human oversight. A well-designed harness transforms an unreliable agent into a reliable system by adding deterministic guardrails around non-deterministic behavior. The harness is the engineering, the agent is the capability.
When to Activate
Activate this skill when:
- Designing autonomous agent execution loops
- Implementing rollback and recovery mechanisms for agent failures
- Building novelty gates to prevent repetitive agent behavior
- Defining human approval boundaries for high-risk agent actions
- Designing durable logging for post-hoc analysis of agent behavior
- Locking evaluation metrics to prevent metric gaming
Do not activate this skill for adjacent work owned by other skills:
- Building evaluation checks and metrics:
evaluation. - Implementing LLM-as-judge scoring:
advanced-evaluation. - Designing multi-agent coordination protocols:
multi-agent-patterns. - Building hosted sandbox infrastructure:
hosted-agents. - Inline destructive-command warnings during an active session: GStack
/careful,/freeze,/guard— use those for tactical safety; this skill designs the full harness loop.
Core Concepts
The harness is the deterministic wrapper around the non-deterministic agent. It manages five concerns:
- Execution control — Start, pause, resume, and terminate agent sessions. Enforce time limits, token budgets, and action counts.
- Evaluation gates — Check agent outputs against quality criteria before accepting them. Gate on deterministic checks (format, constraints) and statistical checks (quality scores).
- Safety boundaries — Define actions that require human approval (destructive operations, external communications, resource-intensive tasks). Block unauthorized actions.
- State management — Log all actions, tool calls, and decisions for debugging. Enable rollback to previous states when failures are detected.
- Novelty detection — Detect and prevent repetitive behavior loops where agents retry the same failed approach.
What ships with it
1 file 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.
- 11d ago First seen · 174 lines · 74 tokens per session scan A 72a9dd49cb19
harness-engineering is a skill published in the GitHub repository navendubrajesh/context-management-for-agents (2 stars, last pushed 2mo ago), licensed MIT. It adds 74 tokens to every session and 1,493 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 2026-08-31.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.