How do I install the Agent Tuning skill?

Run npx skillmds@latest add kunchenguid-no-mistakes/agent-tuning in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agent Tuning safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agent Tuning?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agent Tuning free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agent Tuning?

kunchenguid (@kunchenguid-no-mistakes) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agent Tuning by kunchenguid-no-mistakes

Name: Agent Tuning
Rating: 4.2 (5 reviews)
Author: kunchenguid

File contents

Unified Agent Tuning (internal/agentcfg)

agentcfg is the single owner of the harness-neutral model/effort surface and of the mapping down to each harness's native mechanism (claude/copilot --effort, codex -m + -c model_reasoning_effort, grok --reasoning-effort, pi --thinking, opencode's session-message model/variant, acpx --model for cursor/acp:<target>). Add a harness there, not in an adapter or in eval. rovodev and antigravity are deliberately declared unmappable, so a request for them is a config error rather than a flag that is silently ignored.
agent.NewWithOptions is the one funnel: it validates Options.Profile and splices the mapped args after the operator's raw agent_args_override args, so both the pipeline (cfg.AgentProfileFor) and eval replay (Candidate.Profile()) reach every harness by the same path. Never re-derive a model or effort flag at a call site.
Precedence is fixed: a raw agent_args_override flag that already pins a knob natively wins and the mapped value is not emitted, which is what keeps every pre-agent_config configuration byte-identical and stops a harness receiving one knob twice. agent_config is global-only for the same reason as agent_args_override.
Eval candidates are agent,model=<model>[,effort=<level>] (the previous agent+model spelling is refused with a migration message), effort is part of the persisted candidate identity, and agentNeutralGlobalConfig strips agent, agent_args_override, and agent_config so a replay never inherits the capturing machine's pins.
Keep eval replay identity comparison centralized in agentcfg.ServedMatchesRequested; do not compare an adapter's reported model directly at call sites. The user-facing normalization semantics and candidate guidance live in docs/src/content/docs/reference/eval.md.
Regressions: internal/agentcfg, internal/agent/profile_test.go, internal/config/config_agent_config_test.go, internal/daemon/pipeline_agent_profile_test.go, TestParseCandidate*, TestReplayPinsCandidateModelAndEffortOnTheHarness, TestCaptureStripsEveryHarnessPinFromThePinnedConfig, TestServedMatchesRequested, TestReplayPiModelIdentityComparison.

---name: agent-tuningdescription: Use when changing agent model or effort configuration, adapter mappings, or eval candidate profiles.--- **Unified Agent Tuning (`internal/agentcfg`)** - `agentcfg` is the single owner of the harness-neutral model/effort surface and of the mapping down to each harness's native mechanism (claude/copilot `--effort`, codex `-m` + `-c model_reasoning_effort`, grok `--reasoning-effort`, pi `--thinking`, opencode's session-message `model`/`variant`, acpx `--model` for `cursor`/`acp:<target>`). Add a harness there, not in an adapter or in eval. `rovodev` and `antigravity` are deliberately declared unmappable, so a request for them is a config error rather than a flag that is silently ignored. - `agent.NewWithOptions` is the one funnel: it validates `Options.Profile` and splices the mapped args after the operator's raw `agent_args_override` args, so both the pipeline (`cfg.AgentProfileFor`) and eval replay (`Candidate.Profile()`) reach every harness by the same path. Never re-derive a model or effort flag at a call site. - Precedence is fixed: a raw `agent_args_override` flag that already pins a knob natively wins and the mapped value is not emitted, which is what keeps every pre-`agent_config` configuration byte-identical and stops a harness receiving one knob twice. `agent_config` is global-only for the same reason as `agent_args_override`. - Eval candidates are `agent,model=<model>[,effort=<level>]` (the previous `agent+model` spelling is refused with a migration message), effort is part of the persisted candidate identity, and `agentNeutralGlobalConfig` strips `agent`, `agent_args_override`, and `agent_config` so a replay never inherits the capturing machine's pins. - Keep eval replay identity comparison centralized in `agentcfg.ServedMatchesRequested`; do not compare an adapter's reported model directly at call sites. The user-facing normalization semantics and candidate guidance live in [`docs/src/content/docs/reference/eval.md`](../../../docs/src/content/docs/reference/eval.md). - Regressions: `internal/agentcfg`, `internal/agent/profile_test.go`, `internal/config/config_agent_config_test.go`, `internal/daemon/pipeline_agent_profile_test.go`, `TestParseCandidate*`, `TestReplayPinsCandidateModelAndEffortOnTheHarness`, `TestCaptureStripsEveryHarnessPinFromThePinnedConfig`, `TestServedMatchesRequested`, `TestReplayPiModelIdentityComparison`.

Frequently asked questions

Agent Tuning