agentsclimarketplace

Ai release readiness desk

Skill MadewellRD/skills-lab/dist/vendor/google/ai-engineering-command-desk/ai-release-readiness-desk

assess readiness to release AI capabilities across requirements, evals, safety review, red-team status, inference ops, observability, rollback, docs, support handoff, and owner approval.From its SKILL.md

Install
npx -y skills add MadewellRD/skills-lab --skill ai-release-readiness-desk

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 2 stars2 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.

SKILL.md

6.0 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

AI Release Readiness Desk

Role

Assess whether an AI capability is ready to release. Check requirements, evals, safety review, red-team status, inference operations, observability, rollback, docs, support handoff, and owner approval.

Use when

  • An AI capability is approaching launch or staged rollout.
  • A go/no-go decision needs evidence.
  • A release has unresolved eval, safety, ops, or support questions.

Do not use when

  • The capability is still in early discovery.
  • Core requirements or target users are unknown.
  • The user needs general software release planning without AI-specific gates.

Required evidence

  • Accepted requirements, issue scope, and release target.
  • Eval results, safety review, red-team status, and known risks.
  • Inference ops, observability, rollback, support, and documentation readiness.
  • Approval owner and launch criteria.

Workflow

This order is mandated and must not be rearranged. Gate evidence is assessed before blockers are classified, and blockers are classified before a go/no-go is issued. A go/no-go produced ahead of its evidence is not a decision.

  1. Establish scope and release target.
  2. Check eval, safety, red-team, ops, observability, and rollback gates against evidence.
  3. Classify blockers, warnings, and accepted risks, each with a named owner.
  4. Produce the go/no-go recommendation.
  5. Prepare downstream release or deployment handoff.

Within step 2 the gates are independent: assessing eval status, safety review status, red-team status, inference-ops readiness, observability coverage, rollback readiness, documentation, and support handoff is parallel-safe. Steps 3 through 5 are aggregate and depend on every gate result being in.

Outputs

A readiness run returns the whole decision package together, since a verdict without its gate evidence is not reviewable:

  • go/no-go report: the verdict, the per-gate status behind it, and the evidence each status rests on.
  • launch blocker list: every blocker with what would clear it and who owns clearing it. An empty list is a real result, stated as "no blockers found" alongside the gates that were assessed.
  • risk acceptance notes: recorded only for risks a named owner has actually accepted, with the scope of that acceptance. Where nothing has been accepted, the section says so rather than being padded.
  • rollback checklist: trigger conditions, the steps, who executes them, and how completion is confirmed.
  • handoff summary: what the receiving team needs in order to operate the release.

The bar is that a release owner could hold the go/no-go conversation from this package alone. The independent gates named above are the fan-out unit; classification and verdict aggregate over the complete gate set.

Completeness applies to the set, never to the contents of a gate. An eval result, a safety sign-off, a red-team run, or a rehearsed rollback that has no source is reported as missing and blocks a "go"; it is never scored as passing because launch is close. A fabricated pass is the single output that turns this desk into a liability.

Workflow packet fields

  • capability_id or workflow_id
  • user_goal and target outcome
  • source_facts and evidence_links
  • risk_level and approval_state
  • open_questions and halt_reasons
  • downstream_handoff_targets
  • release_target
  • gate_status
  • blockers
  • warnings
  • accepted_risks
  • approval_owner
  • go_no_go

Halt conditions

Default posture is to proceed and label the assumption inline. A missing documentation link or an unconfirmed support contact is a soft gap: record it as a listed warning against a named owner and continue. Halt only when one of the six hard-halt classes applies.

  • Approval: no approval owner exists for a material risk, or launch would proceed without the authorization the risk tier requires.
  • Production or destructive: the release would reach production with no rehearsed rollback path.
  • Security or privacy: an unresolved security, privacy, or data-exposure finding is open against the release.
  • Source conflict: eval results, safety review, and release records disagree about what is actually shipping or about its status.
  • Release integrity: a gate would be recorded as passed without evidence, or launch would proceed with unresolved blockers.
  • Connector unreachable: eval results, safety review records, red-team findings, or deploy configuration exist but cannot be read.

Downstream handoffs

  • release-operations-desk
  • deployment-desk
  • observability-readiness-desk
  • incident-response-desk
  • ai-incident-response-desk

Source hierarchy

  • User-provided objective, acceptance criteria, and risk tolerance are the first scope boundary.
  • Repository, issue, eval, dataset, telemetry, and release evidence are authoritative for implementation state.
  • Provider documentation and external model documentation are used for model or API capabilities when internal evidence is absent.
  • Conversation summaries and stakeholder notes are decision context, not proof of production behavior.

Quality bar

  • Preserve traceability from recommendation to source evidence.
  • State uncertainty explicitly and label it inline; reserve halts for the hard classes above.
  • Prefer measurable gates over qualitative approval language.
  • Avoid widening autonomy, data exposure, or release scope without an explicit decision.
  • Passing means every gate carries a status and its supporting evidence, every blocker and accepted risk carries an owner, the go/no-go is stated plainly with its reasons, and the rollback path is named and rehearsed.

Capability baseline

Use references/capability-baseline.md for what may be assumed about the executing model: context budget, native self-verification, long-horizon continuation, and parallel fan-out. It also states the governance invariants that do not relax as models improve.

What ships with it: 3 files

6.3 KB alongside SKILL.md

agents/

assets/

references/

Gives 0 of the 12 instructions most ship operate skills give in ~1.2k tokens

Counted across 779 of the 1,178 authors here whose files we hold, read 2026-08-07

  • Document a rollback plan before deploymentin 41 of 779, across 22 files
  • Update the changelogin 21 of 779, across 19 files
  • Run the test suitein 20 of 779
  • Create an annotated git tagin 20 of 779
  • Clean up feature flags after full rolloutin 18 of 779, across 10 files
  • Verify deployment health after launchin 18 of 779, across 10 files
  • Test both feature flag statesin 17 of 779, across 9 files
  • Verify the working tree is cleanin 17 of 779
  • Make database migrations backward-compatiblein 16 of 779, across 8 files
  • Set up error monitoring before launchin 15 of 779, across 7 files
  • Monitor metrics at each rollout stagein 14 of 779, across 5 files
  • Create a GitHub releasein 14 of 779

Said here and by no other author read

  • trace recommendations to source evidence
  • check all release gates against required evidence
  • classify blockers, warnings, and accepted risks with named owners
  • prepare downstream release or deployment handoff
  • label assumptions inline and continue past soft gaps
  • halt immediately if any hard-halt condition applies

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.

Keep looking

Skills are one crate of 326,144. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.