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harness

This skill should be used for multi-session autonomous agent work requiring progress checkpointing, failure recovery, and task dependency management. Triggers on '/harness' command, or when a task involves many subtasks needing progress persistence, sleep/resume cycles across context windows, recovery from mid-task failures with partial state, or distributed work across multiple agent sessions. Synthesized from Anthropic and OpenAI engineering practices for long-running agents.

68

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

A highly actionable, well-sequenced protocol specification with strong workflow clarity and executable code throughout. Its weaknesses are length (inline examples and framing that could be trimmed) and the absence of progressive disclosure — everything is packed into one monolithic file with no reference links.

Suggestions

Move the full harness-tasks.json schema, the concurrency/locking script, and the error-handling category table into separate reference files (e.g. references/state-schema.md, references/concurrency.md, references/error-handling.md), keeping SKILL.md a concise overview that links one level deep.

Tighten the inline examples — reduce the three full task objects in the JSON sample and the 10-line progress-log sample to one representative example each to cut tokens without losing clarity.

Condense the 'Design Principles' section, which reads as philosophical framing rather than executable protocol, to reclaim tokens for the operational content.

DimensionReasoningScore

Conciseness

The ~366-line body avoids basic-concept padding and is mostly operational protocol, but it is lengthy: a full three-task JSON sample, a 10-line progress-log sample, and a philosophical 'Design Principles' section could all be tightened. It sits at 'mostly efficient but could be tightened' rather than 'lean, every token earns its place'.

2 / 3

Actionability

Provides fully executable guidance: a copy-paste-ready atomic mkdir locking script with stale-lock reclamation, concrete git commands ('git reset --hard <started_at_commit>', 'git clean -fd', 'git cat-file -t <hash>'), 'timeout <seconds> <command>' wrappers, a complete JSON schema, and decision matrices with concrete actions.

3 / 3

Workflow Clarity

Multi-step processes are clearly sequenced (Session Start, Task Selection Algorithm, Task Execution Cycle) with an explicit validation checkpoint (run validation.command before declaring completion) and feedback loops (rollback-and-retry, the Context Window Recovery decision matrix, the error-category recovery table). Destructive ops carry validation, so no cap applies.

3 / 3

Progressive Disclosure

There are no bundle/reference files; all detail lives inline in a single 366-line SKILL.md. Sections are well-organized with clear headers, but reference material that could be split out — the full state schema, the locking implementation, the error-handling table — is inline rather than one level deep, matching the 'content that should be separate is inline' anchor.

2 / 3

Total

10

/

12

Passed

Description

85%

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 that answers both what and when with explicit trigger guidance and a distinctive niche. Its main weakness is trigger phrasing that skews toward engineering jargon over the natural language a user would actually say.

Suggestions

Add natural-language trigger phrasings a user would actually say — e.g. 'long-running tasks', 'tasks too big for one session', 'work that keeps getting interrupted' — alongside the technical terms like 'sleep/resume cycles across context windows' to broaden trigger coverage.

Trim the marketing-style closing ('Synthesized from Anthropic and OpenAI engineering practices for long-running agents'), which adds fluff without aiding triggering.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — 'progress checkpointing, failure recovery, and task dependency management' plus 'progress persistence, sleep/resume cycles across context windows, recovery from mid-task failures with partial state' — rather than vague language. Voice is third-person, so no specificity penalty applies.

3 / 3

Completeness

Clearly answers both 'what' (multi-session autonomous agent work with checkpointing, recovery, dependency management) and 'when' with an explicit trigger clause ('This skill should be used for... Triggers on...or when...'). The explicit 'Use when'-equivalent guidance means it is not capped at 2.

3 / 3

Trigger Term Quality

Includes the explicit '/harness' command trigger plus scenario triggers ('many subtasks', 'distributed work across multiple agent sessions'), but leans technical ('sleep/resume cycles across context windows', 'progress persistence') rather than natural user phrasings and misses common variations. It is above the no-keywords anchor but below the broad-natural-coverage anchor.

2 / 3

Distinctiveness Conflict Risk

Occupies a clear niche — long-running, cross-session agent orchestration with context-window recovery — anchored by the distinct '/harness' command and signals ('sleep/resume cycles across context windows', 'distributed work across multiple agent sessions') that are unlikely to fire for unrelated skills.

3 / 3

Total

11

/

12

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
stellarlinkco/myclaude
Reviewed

Table of Contents

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