no-mistakes is a local Git proxy that validates changes in an isolated worktree before forwarding a push to the real remote and opening a pull request. It is for developers and coding agents that want automated checks, safe fixes, CI repair, and human review before changes are published.
Borrowing it
Nothing to install: this file belongs to kunchenguid/no-mistakes. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/kunchenguid/no-mistakes/main/.agents/skills/agent-tuning/SKILL.mdgit clone --depth 1 https://github.com/kunchenguid/no-mistakesWrote 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/kunchenguid/no-mistakes/agent-tuning)<a href="https://agentmods.dev/skills/kunchenguid/no-mistakes/agent-tuning"><img src="https://agentmods.dev/badge/skills/kunchenguid/no-mistakes/agent-tuning/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/kunchenguid/no-mistakes/agent-tuning"><img src="https://agentmods.dev/badge/skills/kunchenguid/no-mistakes/agent-tuning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00021 | $0.00567 |
| Opus 5 | $0.00010 | $0.00283 |
| Sonnet 5 | $0.00004 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
agent-tuning 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 2d 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.
What it actually says
Unified Agent Tuning (internal/agentcfg)
agentcfgis 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-messagemodel/variant, acpx--modelforcursor/acp:<target>). Add a harness there, not in an adapter or in eval.rovodevandantigravityare deliberately declared unmappable, so a request for them is a config error rather than a flag that is silently ignored.agent.NewWithOptionsis the one funnel: it validatesOptions.Profileand splices the mapped args after the operator's rawagent_args_overrideargs, 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_overrideflag that already pins a knob natively wins and the mapped value is not emitted, which is what keeps every pre-agent_configconfiguration byte-identical and stops a harness receiving one knob twice.agent_configis global-only for the same reason asagent_args_override. - Eval candidates are
agent,model=<model>[,effort=<level>](the previousagent+modelspelling is refused with a migration message), effort is part of the persisted candidate identity, andagentNeutralGlobalConfigstripsagent,agent_args_override, andagent_configso 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 indocs/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.
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.
- 2d ago Changed · +1 lines f6a24f09e3e6
- 9d ago First seen · 16 lines · 21 tokens per session scan A 33630e1157a9
agent-tuning is a skill published in the GitHub repository kunchenguid/no-mistakes (8,349 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 567 once invoked, about $0.0001 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.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
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…
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…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…