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flow-spec

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

Quality

69%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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

Quality

Content

62%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 skill excels at workflow discipline — an explicit 8-step gated sequence with thorough validation and error recovery — and its bash guidance is largely executable. Its weaknesses are severe repetition of enforcement boilerplate and a monolithic single-file layout with no reference files despite abundant content that warrants them.

Suggestions

Consolidate the prohibition/enforcement language: state each rule once (Step 4 or the "Prohibited Actions" section) and drop the repeated "MANDATORY"/"DO NOT PROCEED" boilerplate from every heading and step.

Move the NLSpec template and the Step 6.5 adversarial-challenge orchestration bash into a references/ file (e.g. references/nlspec-template.md, references/challenge-dispatch.md), keeping SKILL.md as a lean overview with clearly signaled one-level-deep pointers.

Trim decorative blocks — the provider availability banner, attribution footer, and repeated provider-tracking loops — to the minimum needed for the workflow.

DimensionReasoningScore

Conciseness

The ~480-line body is noticeably verbose: "MANDATORY" is repeated in nearly every heading, the same prohibitions appear in Step 4, the "Prohibited Actions" section, and "Error Handling", and enforcement boilerplate ("DO NOT PROCEED", all-caps warnings, banners) pads every step. It does not teach concepts Claude already knows, so it is above anchor 1, but the redundancy is extensive rather than occasional, placing it at anchor 2.

2 / 5

Actionability

Concrete, mostly copy-paste-ready bash with real script paths (orchestrate.sh, feature-workflow.sh, state-manager.sh), precise jq validation logic, and a complete NLSpec markdown template. It falls short of anchor 5 because several commands require manual placeholder substitution ("<project name>", "<actors>") and depend on external plugin scripts whose behavior is assumed.

4 / 5

Workflow Clarity

The 8-step sequence is explicit and gated: Step 5 verifies the probe synthesis with a run-ID-matched receipt check, Step 7 validates spec completeness with a scoring procedure, and the "Error Handling" section defines per-step recovery (e.g. "If synthesis missing, show orchestrate.sh logs, DO NOT proceed"). Frontmatter validation_gates and the boundary clarification flow add explicit checkpoints and feedback loops, matching anchor 5.

5 / 5

Progressive Disclosure

No references/, scripts/, or assets/ directories exist — everything is inline in one large file. Step headers give usable in-file navigation, but content that clearly belongs in separate files (the full NLSpec template, the ~35-line Step 6.5 challenge orchestration bash, the provider banner) is inlined, matching anchor 3: some structure, but content that should be separate is inline.

3 / 5

Total

14

/

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 is functional and correctly structured with an explicit "use when" clause, but it is thin on both capability detail and natural trigger vocabulary. It relies on the project-specific term "NLSpec" rather than the phrases a user would naturally say.

Suggestions

Expand the "what" with 1-2 concrete outputs, e.g. "generates structured specifications with actors, behaviors, constraints, and acceptance criteria".

Add natural trigger phrases users would actually say, such as "write a spec", "create a specification", or "requirements document", alongside the existing NLSpec terminology.

Clarify what "multi-AI research and consensus" means operationally (e.g., "runs multi-provider research and adversarial review before synthesis") so the trigger condition is concrete.

DimensionReasoningScore

Specificity

"NLSpec authoring" and "structured specification from multi-AI research and consensus" name the domain and 1-2 concrete actions, but coverage is not comprehensive (no mention of the actors, behaviors, constraints, or acceptance criteria the spec contains). It fits anchor 3 rather than 4 because only the core action is listed.

3 / 5

Completeness

Both "what" ("NLSpec authoring — structured specification from multi-AI research and consensus") and an explicit "use when you need a structured specification..." trigger are present. It falls short of anchor 5 because the "what" side is minimal and the trigger, while explicit, leans on the niche term NLSpec rather than concrete trigger phrases.

4 / 5

Trigger Term Quality

Relevant keywords like "specification", "research", and "consensus" are present, but common natural phrasings users would say ("write a spec", "requirements", "spec generation") are missing and "NLSpec" is project jargon. This matches anchor 3: some relevant keywords but missing common variations.

3 / 5

Distinctiveness Conflict Risk

"NLSpec" combined with "multi-AI research and consensus" carves out a fairly distinct niche with minimal conflict risk against unrelated skills. It is not a 5 because it could still overlap with generic specification-writing or spec-kit skills when a user simply asks to "write a spec".

4 / 5

Total

14

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

nyldn/claude-octopus

Path

.claude/skills/flow-spec/SKILL.md

Reviewed

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