agentleFS
ai-agent-assemblyai-agent-assembly/agent-assembly/.claude/skills/product-validation/SKILL.md

product-validation

Skill4 starsChanged 3 months agoApache-2.0View on GitHub

Give every Horonom product a shared, honest answer to the question "is this actually done?" — distinct from, and never satisfied by, a green implementation PR. This is the company-common capability referenced by governance/engineering/agent-skill-architecture.md (HORO-969) §1: it owns product-level smoke/E2E/user-journey QA, quality evidence/reporting, and the defect lifecycle. It does not own unit/component/integration testing, which stays with the relevant *-development skill. Implementation PR/CI green != Product QA PASS. A merged PR with passing unit tests proves the code does what its…

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Before declaring a feature/Story/release Done, on top of an already-green engineering-loop Full Gate — never in place of it.

    line 60
  2. Distinctive:

    When a product has any supported end-to-end path (install, run, a documented user journey) — this is broader than "has a UI".

    line 62
  3. Very specific:

    When producing the durable quality evidence a release or Story sign-off needs — see references/quality-report-template.md.

    line 65
  4. Very specific:

    Run the applicable tracks from references/validation-tracks.md (smoke/install is close to always applicable.

    line 97

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<!-- horonom:generated --><!-- Source: horonomy/.github agents/skills/product-validation/SKILL.md. Do not hand-edit — rerun `python3 agents/common/project_skills.py`. -->

SKILL.md — product-validation

Purpose

Give every Horonom product a shared, honest answer to the question "is this actually done?" — distinct from, and never satisfied by, a green implementation PR. This is the company-common capability referenced by governance/engineering/agent-skill-architecture.md (HORO-969) §1: it owns product-level smoke/E2E/user-journey QA, quality evidence/reporting, and the defect lifecycle. It does not own unit/component/integration testing, which stays with the relevant *-development skill.

The core distinction — state this plainly, every time

Implementation PR/CI green != Product QA PASS. A merged PR with passing unit tests proves the code does what its author believed it should do, checked from inside the implementation. It does not prove the product still works end-to-end for a real user, from outside the implementation, on a real supported path. Those are different claims, verified by different means; collapsing them into one is exactly how a product ships broken while every dev-loop gate stayed green — see references/defect-lifecycle.md for a real, dogfooded case.

Type

Auto-used, no manifest.yaml (see below).

Composes with engineering-loop

Routing only — this skill does not duplicate the Explore/Narrow/Validate/ Escalate/Full Gate loop or the L0–L3 diagnostic contract. engineering-loop's Full Gate stage is the development-side floor (tests/lint/build pass); this skill's quality report is the product-side floor, run after Full Gate is already green, never as a substitute for it. A defect this skill's tracks surface that turns out to need a code fix re-enters engineering-loop normally (Narrow → Validate → Full Gate) before the affected scenario is rerun.

Ownership boundary — read before assuming scope

  • Owned by the relevant development skill (via engineering-loop): unit/component/integration tests that exercise internals directly, anything scoped to a module or function.
  • Owned by this skill: the four tracks in references/validation-tracks.md — smoke/install, golden user journey, adversarial/QA product-behavior, documentation-driven journey — all exercising the product as a real user would, never through implementation-internal shortcuts.
  • Composes conditionally with design-qa (agents/skills/design-qa/SKILL.md — authored in parallel this wave, HORO-969 §1) for a product with a material rendered UI/UX surface; design-qa resolves NOT_APPLICABLE for a non-visual product, but this skill still applies regardless (HORO-969 §3 point 4).

When to use

  • Before declaring a feature/Story/release Done, on top of an already-green engineering-loop Full Gate — never in place of it.
  • When a product has any supported end-to-end path (install, run, a documented user journey) — this is broader than "has a UI"; a CLI or headless service with real docs and a real command flow qualifies.
  • When producing the durable quality evidence a release or Story sign-off needs — see references/quality-report-template.md.

When NOT to use

  • As a replacement for unit/component/integration tests — those stay the development skill's job, and this skill's scenarios are not a substitute for exercising code paths in isolation.
  • To re-validate a change with zero supported end-to-end surface (a library with no runnable product path) — state NOT_APPLICABLE honestly rather than inventing a journey to run.
  • To lower a test's bar so a gate reads green — a scenario that fails stays FAIL/BLOCKED until fixed or explicitly deferred with rationale; see references/defect-lifecycle.md.

No manifest.yaml

This skill applies to any repo with a supported end-to-end path — install a CLI, run a service, follow a documented journey — which no single stack-evidence file (Cargo.toml, pyproject.toml) can gate, the same reasoning shell-development/credential-operations use for "any repo, any stack, may need this at any time." Per HORO-969 §3 point 4, this applicability is explicitly broader than and independent of the framework/UI-target evidence that gates design-qa. There is no manifest shape that correctly encodes "any product with a supported end-to-end path"; the absence of a manifest is the correct encoding, matching the existing no-manifest skills (HORO-969 §9).

Routing

  1. Confirm engineering-loop's Full Gate is already green for the change under evaluation — this skill starts after that, not instead of it.
  2. Run the applicable tracks from references/validation-tracks.md (smoke/install is close to always applicable; golden journey and docs-driven journey apply to any product with a supported path; adversarial/QA behavior scales with risk).
  3. Any reproducible material defect found becomes a real Jira Bug per references/defect-lifecycle.md — never a report-only mention.
  4. Produce the quality report per references/quality-report-template.md, even when zero Bugs were found — a report with no defects is a legitimate PASS, not an optional artifact.

Worked example

  • examples/cli-service-smoke-to-report.md — a CLI/service smoke journey through a seeded defect, its Jira Bug, fix, rerun, and the resulting quality-report excerpt.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.