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app-evolver

Improves an existing application using its code, product contract, authorized work history and competitor evidence. Use for 取长补短、取其精华去其糟粕、参考竞品优化应用 or evolving an app from successful and failed work. Carries authorized changes through implementation and user-result verification, including REA and reverse Skill composition; use skill-evolver for Skills.

57

Quality

72%

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SecuritybySnyk

Passed

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tessl review fix ./app-evolver/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

60%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 presents a well-sequenced five-stage orchestration workflow with explicit verification layers, failure-correction feedback loops, and a concrete decision table, with detail properly split into four real reference files. Its weaknesses are density and abstraction: much of the guidance is policy prose that could be tightened and lacks worked examples or concrete probes.

Suggestions

Reference feature-investigation.md directly from the body (e.g., in §2's reverse-analysis section) so every bundle file is discoverable one level deep from SKILL.md.

Tighten the §1 binding list and other abstract caveat prose — many sentences state the same boundary in multiple ways and could be merged without losing directives.

Add one short worked example of the §3 decision table filled in (one mechanism, its evidence, disposition, and smallest probe) to make the transfer-decision guidance concrete.

DimensionReasoningScore

Conciseness

The body is free of padding and never explains concepts Claude already knows — every sentence is a compressed directive — but the prose is uniformly dense and abstract (e.g., 'A failed parameter family is not a reason to try indefinitely') and several passages, particularly the §1 binding caveats, could be tightened, matching anchor 3's 'mostly efficient but could be tightened' rather than anchor 4.

3 / 5

Actionability

Concrete guidance is present and specific for an instruction-only skill: the §3 decision table enumerates required answers (Evidence, Disposition, Next evidence), §1 names exact binding inputs (repository/ref, pinned immutable ref), and §5 names distinct verification layers with a read-back-at-consumption-point checkpoint. Minor gaps — no worked example or concrete probe per stage — hold it at anchor 4 rather than 5.

4 / 5

Workflow Clarity

Five numbered stages form a clear sequence with explicit validation checkpoints and feedback loops: 'When evidence fails, inspect raw signals, seek a falsifier and correct the root cause or the mistaken criterion. Retry only after a relevant input or hypothesis changes', plus calibration of new guards on healthy and failing inputs. This sits between anchors 4 and 5; verification steps are named abstractly rather than as concrete executable checks, so 4 is the better fit.

4 / 5

Progressive Disclosure

The body is an overview that appropriately splits detail into references/ with well-signaled, one-level-deep links ('Read [evidence and fit](references/evidence-and-fit.md) before selecting changes'), and all three referenced paths resolve to real files. Minor gaps: feature-investigation.md is never referenced from the body (only reachable via skill-composition.md), and the reference files cross-link each other, matching anchor 4 rather than 5.

4 / 5

Total

15

/

20

Passed

Description

70%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 explicit bilingual trigger phrases and disambiguation from the sibling skill-evolver skill. Its main weakness is abstraction: capabilities are named as process-level statements rather than concrete operations, and the trigger coverage is somewhat narrow.

Suggestions

Add 1-2 concrete capability statements (e.g., 'extract transferable mechanisms from a competitor's implementation, decide adopt/adapt/reject per mechanism, implement and verify each accepted change') to lift specificity from process abstraction to concrete actions.

Broaden trigger coverage with natural English variants such as 'optimize the app', 'learn from competitors', or 'apply lessons from past work' alongside the current Chinese idioms.

Expand the unexplained 'REA' acronym or drop it from the description since it is not a term a user would naturally say when needing this skill.

DimensionReasoningScore

Specificity

The description names the domain ('Improves an existing application using its code, product contract, authorized work history and competitor evidence') and several actions ('Carries authorized changes through implementation and user-result verification'), but the actions are process-level abstractions rather than concrete capabilities, matching anchor 3 more than anchor 4's 'several specific actions' like 'fills forms, converts pages to images'.

3 / 5

Completeness

Both 'what' (improves an app using code, contract, history and competitor evidence) and 'when' (explicit 'Use for' clause with concrete trigger phrases) are present, matching anchor 4. It falls short of anchor 5 because the trigger set is narrow and idiomatic and the 'what' is dense with unexplained jargon like 'REA'.

4 / 5

Trigger Term Quality

'Use for 取长补短、取其精华去其糟粕、参考竞品优化应用 or evolving an app from successful and failed work' provides natural bilingual trigger phrases users would actually say, but common English variants like 'optimize app' or 'learn from competitors' are missing, so it falls just short of anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The niche (evolving an existing app from prior work and competitor evidence) is distinct, and 'use skill-evolver for Skills' explicitly disambiguates the closest sibling skill, matching anchor 4's 'mostly distinct; minor overlap risk' with generic code-improvement skills.

4 / 5

Total

15

/

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

daymade/claude-code-skills

Path

app-evolver/SKILL.md

Reviewed

Table of Contents

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