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do-in-steps

Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Use when later steps depend on the results of earlier ones.

Quality

77%

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./antigravity/skills/do-in-steps/SKILL.md

The canonical home for this skill is do-in-steps in NeoLabHQ/context-engineering-kit

SKILL.md
Quality
Evals
Security

Quality

Content

62%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 skill excels at workflow clarity — an explicit, well-checkpointed orchestration loop with real feedback and escalation paths — and its prompt templates are directly executable. Its weaknesses are severe verbosity (redundant rule restatements, concept explanations, and examples that duplicate templates) and a monolithic structure with no progressive disclosure into reference files, plus scattered typos and an inconsistent agent-type prefix.

Suggestions

Trim redundancy: collapse the repeated 'the ONLY statement of this rule' restatements into one authoritative statement of each rule, and cut the three worked examples down to short deltas from the templates instead of near-verbatim reprints of the judge prompts.

Move content into bundle files: relocate the full worked examples, the Cross-Provider Equivalence table, and the long prompt templates to references/ files with one-level-deep links from SKILL.md, keeping the body as an overview of the workflow.

Fix correctness details that hurt actionability: reconcile the 'sadd:meta-judge'/'sadd:judge' subagent_type values with the 'sdd:' agent prefix used elsewhere, and correct typos in the CRITICAL instruction text ('imidiatly', 'anyting', 'differnt').

DimensionReasoningScore

Conciseness

The ~1500-line body is noticeably verbose: generic zero-shot CoT and decomposition templates explain reasoning Claude already performs, rules are restated repeatedly ('the ONLY statement of this rule' appears four-plus times), and three ~150-line worked examples duplicate the judge prompt templates nearly verbatim. Not 1 because the orchestration policy content (model tiers, escalation, pre-existing-changes attribution) is genuinely novel rather than recycled concept explanation; not 3 because the padding is pervasive, not just isolated sections.

2 / 5

Actionability

Provides copy-paste-ready prompt templates for implementation, meta-judge, judge, and retry dispatches, plus concrete tables, report naming conventions, and verdict formats. Not 5 because of real gaps: the dispatch examples use 'subagent_type: "sadd:meta-judge"'/'sadd:judge' while the agent list uses the 'sdd:' prefix, and typos ('imidiatly', 'anyting', 'differnt') appear in critical instruction text.

4 / 5

Workflow Clarity

The sequence is explicit with strong validation checkpoints: per-step judge verification with parsed VERDICT/SCORE, explicit pass thresholds, a retry feedback loop carrying judge ISSUES into the next attempt, a max-retry circuit breaker, and escalation paths to the user. Matches the anchor-5 pattern of clear sequence, explicit validation, and feedback loops for error recovery.

5 / 5

Progressive Disclosure

The body has good section structure with working internal anchor links, but there are no bundle files at all — hundreds of lines (three worked examples, the model-selection policy details, the cross-provider equivalence table, prompt templates) that clearly belong in separate reference files are inlined in a monolithic ~1500-line SKILL.md. Not 4 because the split is not 'minor organization gaps'; not 2 because section headers and anchors keep it navigable.

3 / 5

Total

14

/

20

Passed

Description

83%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.

A strong description: it states concrete capabilities in third person and closes with an explicit, concrete 'Use when' trigger tied to step dependencies. It would reliably surface when users describe multi-step dependent work, with only minor gains available from adding natural synonyms and tightening distinctiveness.

DimensionReasoningScore

Specificity

Lists several concrete actions — 'ordered, dependent steps run sequentially', 'passing context from each step to the next', 'per-step LLM-as-a-judge verification' — with only minor gaps in coverage (no mention of task decomposition or model selection). Not 5 because coverage is not comprehensive; not 3 because it names more than 1-2 concrete actions.

4 / 5

Completeness

Clearly and explicitly answers both: what ('Execute one complex task as ordered, dependent steps... passing context... with per-step LLM-as-a-judge verification') and when ('Use when later steps depend on the results of earlier ones') with a concrete trigger phrase. Matches the anchor-5 good example structure exactly.

5 / 5

Trigger Term Quality

Good natural keyword coverage — 'steps', 'sequentially', 'dependent', 'context', 'verification', 'judge' — that users would plausibly say, though common variations like 'multi-step', 'step-by-step', or 'pipeline' are missing. Not 5 because a few natural synonyms are absent; not 3 because coverage goes beyond 'some relevant keywords'.

4 / 5

Distinctiveness Conflict Risk

The niche — sequential dependent-step execution with per-step judge verification — is mostly distinct, but 'execute a complex task in steps' has minor overlap risk with generic orchestration, planning, or agent-coordination skills. Not 5 because the opening phrase 'Execute one complex task' is broad; not 3 because the dependency/judge framing is a clear differentiator.

4 / 5

Total

17

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (1502 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

NeoLabHQ/context-engineering-kit

Path

antigravity/skills/do-in-steps/SKILL.md

Reviewed

Table of Contents

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