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loki-mode

Version 2.35.0 | PRD to Production | Zero Human Intervention > Research-enhanced: OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, Agent SDK, HN Production (2025)

44

Quality

46%

Does it follow best practices?

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/loki-mode/SKILL.md

The canonical home for this skill is loki-mode in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

58%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 is a well-structured, actionable overview that uses progressive disclosure to push detail into reference files and includes clear workflows with validation checkpoints. Its main weakness is verbosity — it re-explains concepts Claude already knows and duplicates guidance — and a handful of references advertised in the table do not exist as files.

Suggestions

Trim sections that re-explain general knowledge (e.g. descriptions of what unit tests are, restated vendor best-practice quotes, and the redundant 'Essential Patterns' list that overlaps the body sections) to improve token efficiency.

Reconcile the References table with the actual files: either add the missing references/claude-best-practices.md, mcp-integration.md, spec-driven-dev.md, and architecture.md, or remove them from the table so navigation is not broken.

Move the large model-selection tables and the inlined Code-Review/Guardrail/Tripwire pattern definitions into a reference file, keeping only the routing decision logic inline.

DimensionReasoningScore

Conciseness

At ~600 lines the body is noticeably verbose, re-explains widely-known concepts (what a unit test is, generic parallel-agent patterns, explanatory quotes restating vendor best practices) and repeats guidance across sections, adding padding beyond what Claude needs.

2 / 5

Actionability

It provides concrete, mostly-executable guidance — real Task tool snippets, model-selection tables, a bash launch command, and structured dispatch templates — with only minor gaps (some code is illustrative pseudocode like the parallelization loop).

4 / 5

Workflow Clarity

The RARV cycle, SDLC phase flow, and decision tree give a clear sequence with explicit VERIFY checkpoints and feedback loops (verify-fail -> rollback -> retry), satisfying the validation requirement for destructive/batch operations with only minor gaps.

4 / 5

Progressive Disclosure

Structure is good: an overview body with a clear References table pointing one level deep to 14+ real reference files; however several referenced files (claude-best-practices.md, mcp-integration.md, spec-driven-dev.md, architecture.md) are listed in the table but are not actually present in references/, slightly undermining navigation.

4 / 5

Total

14

/

20

Passed

Description

35%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 reads as a version-marketed tagline stuffed with vendor names rather than a functional description of what the skill does and when to invoke it. It lacks concrete actions and any 'Use when...' trigger guidance. Rewriting it to state the skill's actual behavior and natural invocation triggers would substantially improve all dimensions.

Suggestions

Replace the marketing-style tagline with concrete actions the skill performs, e.g. 'Orchestrates a multi-agent SDLC pipeline from PRD to deployment: generates tasks, dispatches subagents, runs quality gates, and verifies each feature.'

Add an explicit 'Use when...' clause with natural trigger phrases users would say, e.g. 'Use when the user wants to run an autonomous, zero-intervention build of a product from a PRD, or asks to spin up the Loki Mode multi-agent system.'

Drop version numbers and the vendor-name list from the description (move them into the body or a changelog) so the description stays focused on behavior and triggers.

DimensionReasoningScore

Specificity

Names a domain ('PRD to Production', 'Zero Human Intervention') and concrete entities (OpenAI SDK, DeepMind, Anthropic, AWS Bedrock) but lists no concrete actions the skill performs — it enumerates research sources rather than actions like 'generate code' or 'deploy services'.

3 / 5

Completeness

It has only a weak/vague 'what' (a tagline) and no 'when' clause at all — there is no 'Use when...' guidance, which caps completeness at 3 and the absence of any when pushes it to 2.

2 / 5

Trigger Term Quality

Keywords are version numbers, vendor names, and jargon ('PRD to Production', 'Zero Human Intervention', 'Agent SDK') rather than natural phrases a user would say when needing this skill; there are no user-shaped trigger words.

2 / 5

Distinctiveness Conflict Risk

The brand name 'Loki Mode' gives it some distinctiveness, but the description's generic startup/autonomy framing could overlap with many other agent-orchestration skills, so it is only somewhat distinct.

3 / 5

Total

10

/

20

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.

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (728 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 5 missing

Warning

Total

13

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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