CtrlK
BlogDocsLog inGet started
Tessl Logo

arn-code-batch-planning

This skill should be used when the user says "batch planning", "batch plan", "arness batch planning", "arn-code-batch-planning", "plan multiple features", "plan all features", "plan unblocked features", "plan the backlog", "plan from backlog", "batch spec and plan", "plan next features", "sequential planning", "multi-feature plan", "plan the next batch", "plan these features", "batch plan GitHub issues", "batch plan from Jira", "plan issues in batch", or wants to plan multiple features from the greenfield Feature Tracker, GitHub issues, or Jira issues in a single session. Pre-analyzes all selected features in parallel, then guides sequential spec review with pipelined plan generation. This skill is typically invoked directly or after arn-brainstorming completes and chains to arn-code-batch-implement upon completion. For single-feature planning, arn-planning is the correct entry point.

66

Quality

81%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

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 exceptionally thorough and actionable with excellent workflow clarity, explicit error handling, and concrete instructions at every step. However, it is severely over-long — the inline detail for every sub-flow, edge case, and preference lookup makes it a massive token consumer that would benefit greatly from extracting major subsections (scope assessment, complex phase upgrade gate, swift/standard plan generation) into reference files. The content is high quality but poorly budgeted for context window efficiency.

Suggestions

Extract the Complex Phase Upgrade Gate (Step 3.5c, ~60 lines) into a reference file like `references/complex-phase-upgrade-gate.md` and replace with a 2-line reference pointer

Move the scope assessment scoring procedure (Step 2.5b, detailed criterion weighting and override rules) into a reference file, keeping only the high-level flow inline

Extract the swift/standard plan generation logic (Step 2.6) into a reference file — the template loading, directory creation, and review flow details don't need to be in the main sequencer

Consolidate the repeated AskUserQuestion patterns — many follow identical structures (3 options: proceed/switch/exit) that could be referenced as a standard pattern rather than spelled out each time

DimensionReasoningScore

Conciseness

This skill is extremely verbose at ~400+ lines with extensive inline logic for every edge case, sub-option, and error path. Much of this could be delegated to reference files or condensed significantly. The multi-level selection flows, complex phase upgrade gate with session memory model, and tier override logic are all spelled out in exhaustive detail that bloats the token budget enormously.

1 / 3

Actionability

The skill provides highly specific, concrete instructions at every step: exact commands (gh issue list), exact file paths, exact AskUserQuestion option text, exact table formats, and precise branching logic. Every decision point has explicit options and outcomes, making it fully executable.

3 / 3

Workflow Clarity

The multi-step workflow is clearly sequenced (Steps 0-6) with explicit validation checkpoints, error handling with retry/skip/abort options at each failure point, status reversion on failure, progress reporting, and feedback loops throughout. The pipeline diagram at the top provides excellent orientation.

3 / 3

Progressive Disclosure

The skill does reference external files appropriately (backlog-selection.md, plan-shipping.md, scope-router-criteria.md, templates), but an enormous amount of procedural logic is inline that could be in reference files — particularly the Complex Phase Upgrade Gate (Step 3.5c), the scope assessment scoring procedure (Step 2.5), and the swift/standard plan generation (Step 2.6). The main file tries to be both overview and complete procedure.

2 / 3

Total

9

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong skill description that excels across all dimensions. It provides an exhaustive list of trigger phrases, clearly describes what the skill does and when to use it, and explicitly differentiates itself from related skills (arn-planning for single features). The description is well-structured and actionable for skill selection.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions: 'Pre-analyzes all selected features in parallel', 'guides sequential spec review with pipelined plan generation', and specifies integration with Feature Tracker, GitHub issues, and Jira issues. It also clarifies chaining behavior with other skills.

3 / 3

Completeness

Clearly answers both 'what' (pre-analyzes features in parallel, guides sequential spec review with pipelined plan generation) and 'when' (explicit trigger phrases listed, plus contextual triggers like wanting to plan multiple features from various sources). Also distinguishes from single-feature planning via arn-planning.

3 / 3

Trigger Term Quality

Excellent coverage of natural trigger terms including many variations users would say: 'batch planning', 'plan multiple features', 'plan the backlog', 'batch plan GitHub issues', 'batch plan from Jira', 'plan issues in batch', 'sequential planning', 'multi-feature plan', etc. Very comprehensive keyword list.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive with a clear niche (batch/multi-feature planning) and explicitly differentiates itself from arn-planning (single-feature) and describes its position in the workflow chain (after arn-brainstorming, before arn-code-batch-implement). Very unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Validation

81%

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

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (620 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

9

/

11

Passed

Repository
AppsVortex/arness
Reviewed

Table of Contents

Is this your skill?

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.