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tech-evaluation

Investigate a candidate library, dependency, or file format against a fixed question set and end with exactly one cited recommendation (adopt / keep_current / reject / needs_more_research) -- never "it depends." Validates the structured output against a JSON schema before writing a human-readable finding. Use when choosing between candidate npm packages, dependencies, or file formats for a specific integration point, or when a plan or ADR decision hinges on an unverified factual claim about a library (does it escape by default, does it bundle cleanly, is it maintained). Do NOT use for one-line version lookups, purely subjective preference calls with no verifiable claim, or a technology choice already settled by an existing ADR -- supersede the ADR instead. Triggers: "evaluate this library", "investigate this dependency", "compare X vs Y", "properly evaluate this technology", "which format should we use", "is this package maintained".

73

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%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 strongly structured, highly actionable workflow with excellent progressive disclosure and a genuine validation feedback loop. The only real weakness is redundancy: the citation/no-guessing rule and the subagent-memory anti-pattern each repeat material already stated in the intro and workflow, which could be tightened to save tokens without losing anything.

Suggestions

Collapse the Mindset section: 'a verdict without a cited source is a guess, not a finding' and the never-'it depends' rule are already stated in the opening paragraph, so keep only the needs_more_research guidance that is new.

Drop the second anti-pattern ('NEVER let a subagent answer from memory') or reduce it to a one-line pointer — it repeats step 2's AVOID bullet verbatim, including the same Cargo.toml/mise.toml examples.

Trim the intro's restatement of the description's when/when-not scope (already covered by When to Use / When NOT to Use sections) to cut ~4 lines of token cost.

DimensionReasoningScore

Conciseness

The body is dense and free of concept-explanation padding, but key points are stated two or three times: 'a verdict without a cited source is a guess, not a finding' appears in both the intro and Mindset; the never-'it depends' rule appears four times (intro, Mindset, step 2, and implicitly in the anti-pattern rationale); and the second anti-pattern ('NEVER let a subagent answer from memory... Cargo.toml/mise.toml') restates step 2's AVOID bullet nearly verbatim. Fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than 4, since the duplication is whole sections, not minor instances.

3 / 5

Actionability

Guidance is concrete and executable: an exact validator invocation with exit-code semantics ('scripts/validate-tech-evaluation.sh <path-to-yaml>', 'exit 0 → schema-valid'), a complete itemized brief for the research subagent, explicit enum values, real file paths (scaffold, schema, validator), and a BAD/GOOD rationale example. Not 4: there are no gaps — a reader can execute the workflow without inventing steps.

5 / 5

Workflow Clarity

Six clearly sequenced steps with an explicit validation checkpoint (step 3: run validator, check exit code) and a feedback loop ('send the YAML back to the subagent rather than manually patching it into shape'), plus a final confirmation step ('confirm the new file is present in the regenerated index before reporting the evaluation as complete'). Matches the anchor with explicit validation, error-recovery loops, and completion checks; not 4 because checkpoints are present at every consequential step.

5 / 5

Progressive Disclosure

The body is a well-sectioned overview with one-level-deep references, all of which exist in the bundle (references/question-dimensions.md, references/finding-record-layout.md, assets/templates/tech-evaluation-scaffold.yaml, assets/schemas/tech-evaluation.schema.json, scripts/validate-tech-evaluation.sh), each surfaced in a 'When to Use' table. Detail (worked dimension examples, record layout) is correctly pushed to the reference files. Not 4: navigation is easy and nothing that belongs in a reference is inlined.

5 / 5

Total

18

/

20

Passed

Description

100%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.

An exemplary description: concrete actions, an explicit output contract, clear when/when-not boundaries, and a dedicated trigger-phrase list covering natural synonyms. It is on the long side, but every clause is load-bearing and none is padded fluff.

DimensionReasoningScore

Specificity

Multiple concrete actions are named: 'Investigate a candidate library, dependency, or file format against a fixed question set', 'end with exactly one cited recommendation (adopt / keep_current / reject / needs_more_research)', 'Validates the structured output against a JSON schema before writing a human-readable finding' — the action set is comprehensive with nothing vague. Not 4: there are no minor gaps; the actions, the output contract, and the validation step are all enumerated.

5 / 5

Completeness

Explicitly answers what ('Investigate... against a fixed question set and end with exactly one cited recommendation') and when ('Use when choosing between candidate npm packages, dependencies, or file formats... or when a plan or ADR decision hinges on an unverified factual claim'), plus an explicit Do-NOT-use clause and trigger phrases. Not 4: both what and when are concrete and specific, not merely present.

5 / 5

Trigger Term Quality

Natural user phrasings are explicitly listed: 'evaluate this library', 'investigate this dependency', 'compare X vs Y', 'properly evaluate this technology', 'which format should we use', 'is this package maintained' — covering synonyms across library/dependency/package/format/technology. Not 4: the trigger list includes the common variations users would actually say, not just domain keywords.

5 / 5

Distinctiveness Conflict Risk

A clear niche (evidence-cited technology evaluation for integration/ADR decisions) with distinct triggers and an explicit negative boundary ('Do NOT use for one-line version lookups, purely subjective preference calls... or a technology choice already settled by an existing ADR'). Not 4: the do-not-use list actively disambiguates against the nearest overlapping skills, minimizing misfires.

5 / 5

Total

20

/

20

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 2 deeper-than-1-level

Warning

referenced_paths_exist

Referenced path issues: 5 deeper-than-1-level

Warning

Total

14

/

16

Passed

Repository
pantheon-org/tekhne
Reviewed

Table of Contents

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