MCPInstall

would-agents-actually

Use when a claim depends on an agent taking a real action.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/srinitude/skills --skill would-agents-actually
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads would-agents-actually automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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.
  4. Verify every load-bearing source at its primary record. Complete the source and trace cards in 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 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. 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, 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 when benchmark, trial, grader, or transport evidence may inform the analysis, then recheck every primary source before use.
  • Load verdict-template.md when creating a verdict. Copy it outside assets/; do not edit the installed template.
  • Load help.md for the help command, verdict-insufficient-evidence.md for a researched verdict, and 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 before changing behavior, trigger boundaries, or evaluation cases.
  • Load 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.

FAQ

What does the would-agents-actually skill do?

Use when a claim depends on an agent taking a real action.

How do I install would-agents-actually?

Run npx skills add https://github.com/srinitude/skills --skill would-agents-actually in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/would-agents-actually/ (personal) or .claude/skills/would-agents-actually/ (project).

Which AI agents can use would-agents-actually?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is would-agents-actually safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains would-agents-actually?

It is published by srinitude in srinitude/skills. It has 536 recorded installs.

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