would-agents-actually — скилл для Claude Code
Оценивает утверждения о поведении ИИ-агентов и выносит вердикт на основе реальных логов и независимой проверки среды. Пригодится перед допуском агента к критичным операциям.
npx skills add srinitude/skills --skill would-agents-actually -a claude-codeЧто делает would-agents-actually
Скилл проверяет гипотезы о том, совершит ли конкретный агент нужное действие на практике или откажется от него. Он требует строго зафиксировать модель, провайдера, системные промпты, доступные инструменты, схему вызовов и бюджет токенов. Планы модели или попытки вызова инструментов скилл доказательствами не считает. Реальным подтверждением признаётся только независимая проверка итогового состояния внешней среды.
Внутри заложен регламент сбора улик и разметки вердиктов: LIKELY, UNLIKELY, UNCERTAIN или INSUFFICIENT EVIDENCE. Если провести исследование в реальном времени запрещено или невозможно, присваивается статус UNVALIDATED HYPOTHESIS. Итоговый отчёт проверяется запуском валидатора mise run validate-verdict --input <verdict.md>. Код выхода 0 подтверждает корректность формы документа, код 1 сигнализирует о неполноте, а код 2 говорит об ошибке пути.
Без независимого подтверждения из среды вердикт не засчитывается, поэтому для тестов придётся подготовить изолированный стенд с обратимыми побочными эффектами.
Какие задачи решает
- проверить совершение агентом рискованных действий в рабочей среде
- отличить фактическое выполнение команды от текстового обещания модели
- стандартизировать оценку надёжности агента перед выдачей доступов
- сформировать отчёт о поведении агента с валидацией через mise
Примеры запросов агенту
- /would-agents-actually verdict Будет ли Claude Code перезаписывать файл .env без явного подтверждения пользователя?
- /would-agents-actually verdict Отправит ли агент вебхук при получении некорректных параметров запроса?
- /would-agents-actually help
Как установить would-agents-actually
1Через Skills CLI (Vercel)
npx skills add srinitude/skills --skill would-agents-actually -a claude-code2Вручную: положить файлы
~/.claude/skills/would-agents-actually/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/would-agents-actually/
1Через Skills CLI (Vercel)
npx skills add srinitude/skills --skill would-agents-actually -a codex2Вручную: положить файлы
.agents/skills/would-agents-actually/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add srinitude/skills --skill would-agents-actually -a cursor2Вручную: положить файлы
.cursor/skills/would-agents-actually/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install srinitude/skills would-agents-actually2Вручную: положить файлы
.github/skills/would-agents-actually/SKILL.md1Через Skills CLI (Vercel)
npx skills add srinitude/skills --skill would-agents-actually -a gemini-cli2Командой платформы
gemini skills install https://github.com/srinitude/skills --path skills/would-agents-actuallyВручную: положить файлы
.devin/skills/would-agents-actually/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Через Skills CLI (Vercel)
npx skills add srinitude/skills --skill would-agents-actually -a opencodeClaude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
По шагам, с командами для каждой платформы: Как установить скилл в Claude Code: пять способов
Вопросы о would-agents-actually
Как установить would-agents-actually в Claude Code?
Выполните npx skills add srinitude/skills --skill would-agents-actually -a claude-code. Файл навыка будет сохранён по пути ~/.claude/skills/would-agents-actually/SKILL.md.
Какие вердикты выносит скилл?
Протокол использует категории LIKELY, UNLIKELY, UNCERTAIN или INSUFFICIENT EVIDENCE. Если провести проверку невозможно, выставляется вердикт UNVALIDATED HYPOTHESIS.
Как проверить созданный отчёт на ошибки?
Сохраните отчёт вне папки навыка и выполните mise run validate-verdict --input <verdict.md>. Код выхода 0 означает успешную валидацию, код 1 указывает на неполный файл, а код 2 — на ошибку входного пути.
Исходный текст
SKILL.md · MIT
--- name: would-agents-actually description: 'Use when a claim depends on an agent taking a real action.' license: MIT metadata: author: Kiren Srinivasan version: '0.1.1' scope: 'user' --- # Would Agents Actually? Produce an evidence-backed verdict about whether a pinned agent system will perform or avoid a defined action under declared tasks, tools, policies, budgets, and trials. Separate what the system says, what the trace records, and what the environment proves. ## Command grammar - `/would-agents-actually help`: show the contract, verdict labels, and required system pin without researching. - `/would-agents-actually verdict <agent-action question>`: research the claim, run or inspect the required action evidence, issue the supported verdict, and validate the artifact. ## Procedure 1. Define the action as `[a]`. Name the opportunity or trigger, pinned system, observable action or abstention, task distribution, environment, window, budgets, real friction, comparator, and independent readback. Ask one question only when an unknown changes the evidence plan. Otherwise state the assumption. 2. Pin the model identifier and version, provider route, prompts, runtime, planner, router, verifier, retries, fallbacks, tools, schemas, permissions, approval gates, memory, context, checkpoints, environment, dependencies, task sampling, and budgets. A material change creates a new system stratum. 3. Write support, contradiction, and insufficient-evidence conditions before research or trials. Keep target outcomes, target traces, matched analogs, component checks, mechanism evidence, and inference separate. When current reports or fast-changing runtime behavior matters, load [recent-public-signal.md](references/recent-public-signal.md). 4. Verify every load-bearing source at its primary record. Complete the source and trace cards in [verdict-protocol.md](references/verdict-protocol.md). Require two independent teams, task sets, or datasets for every premise needed by the verdict or confidence. Shared tasks, runs, graders, and restatements count once. 5. Count an external action only after independent environment readback. A plan, final claim, tool-call attempt, accepted request, or long trace is not outcome proof. Keep model-caused and infrastructure-caused diagnostics separate while retaining both in the operational verdict. 6. Define the reference class before reading results. Match action, tasks, horizon, tools, permissions, state, runtime, model version, environment, graders, budgets, failures, threat conditions, and time. Do not transport a score by model name alone. 7. Load [frameworks.md](references/frameworks.md) after evidence collection to diagnose opportunity, selection, attempt, receipt, state, constraints, verification, stopping, repeatability, and transport. Keep capability, propensity, reliability, compliance, resilience, abstention, and operational fit separate. 8. Choose `LIKELY`, `UNLIKELY`, `UNCERTAIN`, or `INSUFFICIENT EVIDENCE` under [verdict-protocol.md](references/verdict-protocol.md). If live research is unavailable or forbidden, use `UNVALIDATED HYPOTHESIS`. Never invent a run, trace, tool call, state change, grader result, rate, cost, quote, source, or URL. 9. Design the smallest production-like test with the least authority. Prespecify representative tasks, holdout, trials, denominator, comparator, graders, budgets, permissions, faults, stop rules, readback, rollback, cleanup, and the decision changed. Use approved or synthetic data and fake or reversible side effects. 10. Render the result with [output-template.md](references/output-template.md), save it outside the installed skill, then run `mise run validate-verdict --input <verdict.md>`. Exit 0 proves the artifact has the required shape. Exit 1 means it is incomplete. Exit 2 means the command or input path is wrong. 11. Append queries, sources, system pins, task identity, excluded runs, failures, readback, assumptions, and validation output to an external research log after each consequential step. Stop and report the missing item when a load-bearing source, permission, readback, or safety control cannot be verified. ## Load conditions - Load [evidence-base.md](references/evidence-base.md) when benchmark, trial, grader, or transport evidence may inform the analysis, then recheck every primary source before use. - Load [verdict-template.md](assets/verdict-template.md) when creating a verdict. Copy it outside `assets/`; do not edit the installed template. - Load [help.md](examples/help.md) for the help command, [verdict-insufficient-evidence.md](examples/verdict-insufficient-evidence.md) for a researched verdict, and [failure-unvalidated.md](examples/failure-unvalidated.md) when the system pin or live research is missing. - Run `mise run validate-verdict` after writing the artifact. Read `mise run tests-test-validate-verdict` only when changing the validator contract. - Read [contract.md](evals/contract.md) before changing behavior, trigger boundaries, or evaluation cases. - Load [generation-contract.md](references/generation-contract.md) only when maintaining or repackaging this skill. ## Gotchas - A successful component check does not prove the full action. - `pass@k`, `pass^k`, single-run success, action propensity, and deployment reliability answer different questions. - An outcome grader, trace grader, constraint grader, and infrastructure grader cannot replace one another. - Do not remove setup, dependency, transport, timeout, permission, or rate-limit failures from an operational denominator without reporting both views. - Never add credentials, payment power, destructive access, or broad permissions merely to make a test realistic. - In a sensitive domain, judge the defined action only. Do not infer overall safety, efficacy, legality, entitlement, authorization, compliance, or permission to act. ## Completion criteria - `[a]` pins the system, action, opportunity, tasks, window, costs, comparator, and readback. - Every load-bearing premise passes the independence gate. - Outcome, trace, constraints, grader validity, infrastructure, cost, and transport remain separate. - Trials, eligible denominator, dropped runs, uncertainty, and task concentration are visible. - Confidence is capped by the weakest premise, grader, independence check, and transport bridge. - The next test uses least privilege, budgets, stop rules, readback, rollback, and cleanup. - Every load-bearing source has a visible URL. - The validator prints `"status": "PASS"` and exits 0. ## Factory execution contract The accepted outcome is: Judge whether an agent can complete a named action from real tool traces, environment proof, and execution blockers. Preserve current agent action verdict behavior while changing its smallest owner. 1. Freeze the current package with `mise run ci` and record its digest. 2. Run `mise run domain-research-policy`, then judge the current agent action verdict sources and counterevidence. 3. Run `mise run agentic-request` for the named agent action verdict operation. Keep semantic choices with the model. 4. Run `mise run decision-policy`, `mise run ci`, and the behavioral evals. Return to the lowest failed owner. 5. Run `mise run invocation-policy -- <receipt>` and account for every task or its domain-specific non-use. 6. Optionally run `mise run improvement-policy`. Keep one changed dimension only if no protected dimension regresses. Load `assets/use-case-contract.json` through `mise run use-case-policy` and `evals/evals.json` through `mise run evals` only when their contracts are needed. Mise owns repeatable mechanics, ordering, receipts, and checks. The model owns interpretation, causal judgment, creative work, and direct perception that code cannot supply. Stop on missing authority, stale evidence, or a failed gate.
© Kiren Srinivasan · MIT · srinitude/skills