Build agent harnesses
Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/build-agent-harnesses
Runs checklists and workflows for designing, hardening, and operating agent harnesses — guardrails, verify steps, tool loops, durable sessions, eval, quotas, coding/deep-research/voice harnesses. Use when the user builds agent runtimes, asks what a harness is, scopes agent vs workflow, or says "agent harness", "guardrails", "verify step", "FOMAT", "durable session".From its SKILL.md
npx -y skills add hiteshbandhu/skills-i-use --skill build-agent-harnessesAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
- runs commandsInstructs the agent to run 3 commands, including `cp -r skills/build-agent-harnesses ~/.claude/skills/` and 2 more.
SKILL.md
3.4 KB, 788 tokens by cl100k_base, as published. Nobody here has run it
Build agent harnesses
Action playbook from twenty-two AI Engineer talks on agent & harness engineering. Do not summarize talks — pick a workflow and execute it.
Supporting files (read when needed):
- workflows.md — workflows A–L (steps, deliverables, stop conditions)
- source-index.md — src-NNN → talk learnings in ingest-into-skills
Optional deliverables: {SKILL_OUTPUT_DIR}/build-agent-harnesses/ — see skills-i-use OUTPUT.md.
Step 0 — Pick workflow
Use the decision tree below. Open the matching section in workflows.md.
What is the user trying to do?
├─ Define harness vs agent loop + guardrails/verify → A
├─ Decide agent vs workflow for a use case → B
├─ Maturity path: framework → state machine → scale → C
├─ Enterprise: quotas, skills, multi-agent platform → D
├─ Agent evaluation + anti-hype discipline → E
├─ Long-horizon research / deep research harness → F
├─ Coding agent / computer-use / IDE harness → G
├─ Production chat UX: resume, steer, multi-device → H
├─ Operate many parallel coding agents (FOMAT) → I
├─ Regulated / domain-vertical knowledge agents → J
├─ Delete scaffold; maximize LLM compute in backend → K
└─ Post-training / finetune / RL for tool agents → L
Stop summarizing once a workflow is identified — run its checklist.
Install
cp -r skills/build-agent-harnesses ~/.claude/skills/
cp -r skills/build-agent-harnesses ~/.cursor/skills/
cp -r skills/build-agent-harnesses ~/.codex/skills/
From skills-i-use or ingest-into-skills after sync.
Source corpus: ingest-into-skills playlists/agent-harness-engineering-ai-engineer/.
Cross-cutting rules
| Rule | Source |
|---|---|
| Harness = tools + context + guardrails + verify around the model | [src-001 @ 4:36] |
| Do not prompt harder when verify shows failure — fix harness | [src-001 @ 9:37] |
| Agents only when ambiguity/value/error profile justify cost | [src-002 @ 2:57] |
| Every agent is a state machine; prune prompts as models improve | [src-003 @ 4:37] |
| Agent eval needs environments + actions, not I/O strings only | [src-006 @ 7:52] |
| Decouple clients via durable sessions for resume/steer | [src-015 @ 5:25] |
Disputed steps: read talk in source-index.md.
Output to user
- Name the workflow (A–L) and what you are producing
- Save artifacts under
./skill-outputs/build-agent-harnesses/when the user wants files - Do not auto-commit
Invocation examples
@build-agent-harnesses design a harness for our browser agent
agent vs workflow for this support bot?
we need quotas and verify steps for production agents
What ships with it: 3 files
14.2 KB alongside SKILL.md
- README.md1.3 KB
- source-index.md4.6 KB
- workflows.md8.3 KB
Gives 0 of the 12 instructions most context ai engineering skills give in 788 tokens
Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06
- Dispatch a fresh subagent for each taskin 76 of 1328, across 59 files
- Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
- Dispatch a final code reviewer after all tasksin 38 of 1328, across 26 files
- Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
- Use the least powerful model capable of the taskin 33 of 1328, across 26 files
- Create a TodoWrite list for all tasksin 32 of 1328, across 22 files
- Perform a task review after each implementationin 31 of 1328, across 24 files
- Extract all tasks and context from the planin 29 of 1328, across 20 files
- Provide full task text to subagentsin 28 of 1328, across 20 files
- Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
- Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
- Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files
Said here and by no other author read
- Pick a workflow from the decision tree
- execute the workflow checklist
- read supporting files when needed
- fix the harness when verify steps fail
- decouple clients using durable sessions
- name the workflow and output produced
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.