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flow-spec

NLSpec authoring — use when you need a structured specification from multi-AI research and consensus

58

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./skills/flow-spec/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

70%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.

The body delivers a well-sequenced, validated multi-step workflow with strong executable guidance, but it is padded with redundant enforcement language and keeps the NLSpec template inline with no bundled sub-file references for progressive disclosure.

Suggestions

Consolidate the repeated "MANDATORY/CANNOT SKIP/DO NOT PROCEED" markers and the Prohibited Actions section into a single concise checkpoint list to reduce token bloat.

Extract the NLSpec template (lines ~172-226) into a bundled references/nlspec-template.md file and reference it one level deep, improving progressive disclosure.

Trim justifications that explain why a step matters (Claude already understands spec quality) and keep only the executable instructions.

DimensionReasoningScore

Conciseness

Mostly efficient with concrete command blocks, but padded with repetitive "MANDATORY/CANNOT SKIP" markers, a restated Prohibited Actions section, and verbose justifications (e.g., why clarifying questions matter) that could be trimmed.

3 / 5

Actionability

Provides concrete executable bash commands (check-providers.sh, state-manager.sh, orchestrate.sh probe), a full NLSpec template, and validation snippets, with only minor placeholder gaps (<actors>, <project description>).

4 / 5

Workflow Clarity

A clearly sequenced 8-step process with explicit validation gates, feedback loops (synthesis verification, adversarial review-and-revise, completeness scoring), and dedicated Error Handling guidance.

5 / 5

Progressive Disclosure

No bundled reference/script/asset directories exist; the ~55-line NLSpec template is inlined rather than split into a referenced file, and external paths point to non-bundled plugin scripts.

3 / 5

Total

15

/

20

Passed

Description

62%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.

The description clearly states what the skill does and when to use it with an explicit trigger, but the trigger language is specialized and lacks the common synonyms a user would naturally say. Specificity is adequate but not comprehensive.

Suggestions

Expand the trigger clause with natural phrases users actually say, e.g. "use when you need a structured specification, to spec out a feature, define requirements, or specify a system from multi-AI research and consensus".

Surface 1-2 more concrete actions or outputs (e.g., "generate a structured NLSpec with actors, behaviors, constraints, and acceptance targets") to lift specificity.

DimensionReasoningScore

Specificity

Names the domain ("NLSpec authoring") and a concrete deliverable ("structured specification from multi-AI research and consensus") but lists only a single composite action rather than multiple discrete actions.

3 / 5

Completeness

Explicitly answers both what ("NLSpec authoring") and when ("use when you need a structured specification"), but the when clause is general rather than enumerating concrete triggering situations.

4 / 5

Trigger Term Quality

Has a natural trigger ("use when you need a structured specification") but relies on specialized terms ("NLSpec", "multi-AI research") and misses common synonyms like "spec out", "requirements", or "specifying a system".

3 / 5

Distinctiveness Conflict Risk

The proprietary "NLSpec" term and "multi-AI research and consensus" framing carve a distinct niche with only minor overlap risk against generic spec/research skills.

4 / 5

Total

14

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
nyldn/claude-octopus
Reviewed

Table of Contents

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