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arn-code-batch-implement

This skill should be used when the user says "batch implement", "implement all", "batch execution", "implement all features", "parallel implement", "implement in parallel", "arness batch implement", "arn-code-batch-implement", "run batch implementation", "implement everything", "launch batch workers", or wants to spawn parallel worktree-isolated background agents to implement multiple pending features simultaneously. Each worker runs as a full independent session with all tools. This skill requires pending plans in .arness/plans/ — run arn-code-batch-planning first if none exist.

80

Quality

76%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/arn-code/skills/arn-code-batch-implement/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Discovery

89%

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 excels at trigger term coverage and completeness, providing an extensive list of activation phrases and clearly stating both what the skill does and when to use it. It also includes a helpful prerequisite note. The main weakness is that the 'what' portion could be more specific about the concrete actions performed beyond 'implement features in parallel' — e.g., what does each worker actually do (create branches, write code, run tests, open PRs)?

Suggestions

Add more specific concrete actions describing what each worker does (e.g., 'creates feature branches, writes implementation code, runs tests, and commits changes').

DimensionReasoningScore

Specificity

The description mentions spawning parallel worktree-isolated background agents to implement multiple pending features simultaneously, and that each worker runs as a full independent session with all tools. It names the domain and some actions but doesn't list multiple concrete actions beyond 'implement features in parallel'.

2 / 3

Completeness

The description clearly answers both 'what' (spawn parallel worktree-isolated background agents to implement multiple pending features simultaneously) and 'when' (explicit trigger phrases listed, plus the condition about pending plans in .arness/plans/). It also includes a prerequisite dependency note.

3 / 3

Trigger Term Quality

The description includes an extensive list of natural trigger phrases users would say: 'batch implement', 'implement all', 'batch execution', 'implement all features', 'parallel implement', 'implement in parallel', 'implement everything', 'launch batch workers', plus tool-specific identifiers. This provides excellent coverage of variations.

3 / 3

Distinctiveness Conflict Risk

The description is highly specific to a particular workflow involving batch parallel implementation via worktree-isolated agents, with very distinct trigger terms like 'batch implement', 'arn-code-batch-implement', and references to a specific plans directory. This is unlikely to conflict with other skills.

3 / 3

Total

11

/

12

Passed

Implementation

62%

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

This skill is highly actionable and has excellent workflow clarity with thorough validation checkpoints, error recovery, and user decision points. However, it suffers significantly from verbosity — the main SKILL.md contains extensive implementation detail (worktree collision resolution, slug computation, retry algorithms) that should be delegated to reference files, making it far too long for a sequencer/orchestrator skill that explicitly states it 'MUST NOT duplicate sub-skill logic.'

Suggestions

Move the worktree slug computation (Step 4a) and collision resolution logic into a dedicated reference file (e.g., worktree-provisioning.md) and reference it from the main skill, reducing inline verbosity by ~40%.

Move the retry/deferred logic (Step 6) and the aggregation logic (Step 6.5) into separate reference files, keeping only a brief summary and pointer in the main workflow.

Remove the worked example in Step 6 — Claude can infer the retry pattern from the instructions without a narrative walkthrough.

DimensionReasoningScore

Conciseness

The skill is extremely verbose at ~300+ lines with extensive edge-case handling, retry logic, collision resolution, worked examples, and detailed sub-steps that could be offloaded to reference files. Much of this detail (slug sanitization rules, empty-slug fallback, collision detection algorithms) could be in referenced documents rather than inline, especially given the skill already references external files.

1 / 3

Actionability

The skill provides highly concrete, executable guidance throughout: specific git commands, exact bash snippets, precise Agent tool parameters, explicit prompt templates, and detailed table formats for output. Every step has clear, copy-paste-ready instructions.

3 / 3

Workflow Clarity

The workflow is exceptionally well-sequenced with numbered steps, explicit validation checkpoints (pre-flight validation, worktree verification, retry logic with clear success/failure paths), feedback loops for deferred features, and clear decision points with user prompts at each gate. Error recovery is thoroughly addressed.

3 / 3

Progressive Disclosure

The skill references external files appropriately (preflight-validation.md, worker-instructions.md, ensure-config.md) but then inlines enormous amounts of detail that should be in those reference files — the worktree collision resolution algorithm, retry logic, and slug computation are all inline when they could be in dedicated references. The result is a monolithic main file despite having a reference structure available.

2 / 3

Total

9

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
AppsVortex/arness
Reviewed

Table of Contents

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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.