workflow-execute

Autonomous workflow execution pipeline with CSV wave engine. Session discovery → plan validation → IMPL-*.json → CSV conversion → wave execution via spawn_agents_on_csv → results sync. Task JSONs remain the rich data source; CSV is brief + execution state.

Quality

36%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Critical

Do not install without reviewing

Optimize this skill with Tessl

npx tessl skill review --optimize ./.codex/skills/workflow-execute/SKILL.md

Quality

Discovery

17%

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 description reads like internal technical documentation rather than a skill selection guide. It focuses on implementation details (JSON files, CSV conversion, specific function names) rather than user-facing capabilities and lacks any explicit trigger guidance ('Use when...'). The highly technical jargon would make it nearly impossible for Claude to match this skill to natural user requests.

Suggestions

Add an explicit 'Use when...' clause describing user-facing scenarios, e.g., 'Use when the user wants to orchestrate multiple parallel agent tasks, run batch workflows, or execute a multi-step implementation plan across agents.'

Replace internal jargon with natural trigger terms users would actually say, such as 'batch execution', 'parallel tasks', 'multi-agent workflow', 'run implementation plan', or 'orchestrate agents'.

Rewrite the opening to describe concrete user-facing capabilities rather than internal pipeline architecture, e.g., 'Orchestrates multi-step implementation plans by distributing tasks across parallel agents in waves, tracking progress via CSV state files.'

Dimension	Reasoning	Score
Specificity	The description names specific artifacts and steps (IMPL-*.json, CSV conversion, spawn_agents_on_csv, wave execution), but the actions are described as a pipeline rather than listing concrete user-facing capabilities. It's more of an internal architecture description than a capability list.	2 / 3
Completeness	While the 'what' is partially addressed (autonomous workflow execution pipeline), there is no 'Use when...' clause or any explicit guidance on when Claude should select this skill. The description reads like internal documentation rather than a selection guide.	1 / 3
Trigger Term Quality	The terms used are highly technical and internal ('IMPL-*.json', 'spawn_agents_on_csv', 'wave engine', 'results sync'). Users would not naturally say any of these phrases when requesting this skill. Natural trigger terms like 'run tasks', 'batch execution', 'parallel agents', or 'automate workflow' are absent.	1 / 3
Distinctiveness Conflict Risk	The highly specific technical terms (spawn_agents_on_csv, IMPL-*.json, CSV wave engine) make it unlikely to conflict with other skills, but the opening phrase 'Autonomous workflow execution pipeline' is generic enough to potentially overlap with other automation or orchestration skills.	2 / 3
	Total	6 / 12 Passed

Implementation

55%

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

This skill excels at actionability and workflow clarity — every phase is fully implemented with executable code, clear sequencing, and proper validation checkpoints. However, it is severely hampered by its monolithic structure and extreme verbosity; at 500+ lines with inline implementations of CSV parsers, BFS algorithms, and full instruction templates, it consumes far too many tokens. The content desperately needs to be split into a concise overview SKILL.md with references to separate implementation files.

Suggestions

Extract the full phase implementations (JavaScript code blocks) into separate referenced files (e.g., phases/phase3-csv-conversion.js, phases/phase4-wave-execute.js) and keep only the pipeline overview, usage, and core rules in SKILL.md.

Move CSV helpers (parseCsv, parseCsvLine, updateMasterCsvRow) and the execute instruction template into separate reference files, as these are implementation details not needed in the main skill overview.

Remove the detailed 21-column CSV schema table and replace with a brief note about the CSV structure plus a link to a SCHEMAS.md reference file.

Trim explanatory comments in code that describe what Claude can already infer (e.g., '// Skip to Phase 3', '// BFS', '// Cleanup temp wave CSVs') to reduce token usage.

Dimension	Reasoning	Score
Conciseness	This skill is extremely verbose at 500+ lines, with massive inline code blocks that could be extracted to separate files. The full implementation of every phase, CSV helpers, instruction templates, and detailed column-by-column schema tables consume enormous token budget. Much of this (CSV parsing, BFS algorithm, regex escaping) is knowledge Claude already has.	1 / 3
Actionability	The skill provides fully executable JavaScript code for every phase, complete with flag parsing, CSV generation, spawn_agents_on_csv calls with output schemas, and concrete bash commands. The code is copy-paste ready and covers all edge cases including resume mode, error handling, and auto-commit.	3 / 3
Workflow Clarity	The 6-phase pipeline is clearly sequenced with an excellent ASCII diagram overview. Validation checkpoints are explicit (Phase 2 validates planning docs, Phase 3 has user confirmation, Phase 4 checks dependency failures before execution, Phase 5 reconciles state). Feedback loops exist for failed dependencies (skip cascade) and resume mode.	3 / 3
Progressive Disclosure	This is a monolithic wall of text with all implementation details inline. The full CSV helper functions, instruction template, BFS wave computation algorithm, and every phase's complete implementation are embedded in a single file. The CSV schema table, session structure, and agent assignment rules could all be separate reference files linked from a concise overview.	1 / 3
	Total	8 / 12 Passed

Validation

72%

Warnings & errors only

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

Validation — 8 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (1118 lines); consider splitting into references/ and linking	Warning
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	8 / 11 Passed

Repository: catlog22/Claude-Code-Workflow
Commit: 0f8e801

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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