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quintet-pipeline

Orchestrate multi-stage AI workflows using Interpretable Context Methodology (ICM) staged folder pipelines with human check gates and edit surfaces. Use when executing complex compound workflows (e.g. debate -> spec -> worktree implementation -> review -> verify) or two-phase review-and-fix tasks where humans must review intermediate outputs.

Quality

83%

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SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, executable body: every workflow step has a runnable command, sequencing is clear, and validation exists via human check gates and the Walk Test. The main gaps are the absent error-recovery guidance (what to do when the walk test fails or a gate edit is made) and unexplained template choices.

Suggestions

Add a short error-recovery loop for the audit step: what to check and fix when 'quintet walk my-feature' fails, and how to re-run the walk test after fixes (validate -> fix -> re-validate).

Briefly describe when to choose each template ('debate-build' vs 'review-fix') — one clause each is enough to make the init step actionable for new users.

Clarify the check-gate resume flow: after a human edits output/consensus.md or findings.json, state explicitly which command continues the pipeline and what 'verified' means before using --auto.

DimensionReasoningScore

Conciseness

The body is lean: short sections, executable one-line commands, and no explanation of concepts Claude already knows. Minor trims are possible — e.g. 'A passing Walk Test proves that any newly spawned agent or human can understand the run's goals, inputs, boundaries, and outputs...' restates the walk test's purpose at length. Fits anchor 4 (efficient, minor over-explanation) rather than 5.

4 / 5

Actionability

Provides copy-paste-ready bash for all four operations (init, status, advance, walk), including a complete executable-resolution fallback chain for the quintet binary. Not 5: the two templates ('debate-build' vs 'review-fix') are named but not explained, and what the user should concretely do at a check gate is only sketched ('inspect or edit output/consensus.md').

4 / 5

Workflow Clarity

A clear numbered sequence (initialize -> inspect -> advance -> audit) with the human check gates and the Walk Test serving as validation checkpoints. Not 5: there is no error-recovery loop — nothing says what to do when 'walk' fails or how to resume after a human edits an intermediate file, so minor validation gaps remain.

4 / 5

Progressive Disclosure

A single-file skill with well-organized sections (invariants, workflow) and no nested or dangling references, which is appropriate at this size. Not 5: the body is slightly over the simple-skill threshold (~54 lines), and the 'Core Invariants Enforced' section is borderline reference material that would belong in a separate file if the skill grows.

4 / 5

Total

16

/

20

Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that clearly states what the skill does and when to use it, with concrete trigger examples and a well-differentiated niche. Minor room for improvement in keyword breadth and enumerating the concrete pipeline operations.

DimensionReasoningScore

Specificity

Names the domain and several concrete mechanisms — 'staged folder pipelines with human check gates and edit surfaces', 'two-phase review-and-fix tasks' — but does not list the concrete operations (init, advance, walk/status), leaving minor coverage gaps. Fits anchor 4 ('several specific actions; minor gaps') rather than 3 (more than 1-2 actions named) or 5 (not fully comprehensive).

4 / 5

Completeness

Explicitly answers both questions: the first sentence states what the skill does ('Orchestrate multi-stage AI workflows using... staged folder pipelines with human check gates and edit surfaces') and the second gives a concrete trigger clause ('Use when executing complex compound workflows (e.g. debate -> spec -> worktree implementation -> review -> verify) or two-phase review-and-fix tasks'). This matches anchor 5 exactly; it is above anchor 4 because the 'when' is explicit with concrete examples, not merely present.

5 / 5

Trigger Term Quality

Includes natural user phrases like 'multi-stage AI workflows', 'worktree implementation', 'review-and-fix', and 'review intermediate outputs', giving good keyword coverage. Not 5: 'Interpretable Context Methodology (ICM)' is jargon users would not naturally say, and common synonyms such as 'pipeline', 'step-by-step', or 'sequential stages' are only implicitly covered.

4 / 5

Distinctiveness Conflict Risk

The niche is well-defined — ICM staged folder pipelines, human check gates, edit surfaces, two-phase review-and-fix — making it mostly distinct. Not 5 because the generic opening 'Orchestrate multi-stage AI workflows' overlaps with general multi-agent/workflow orchestration skills, creating minor conflict risk.

4 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

salemaziel/omc-octo-quintet

Path

skills/quintet-pipeline/SKILL.md

Reviewed

Table of Contents

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