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prime-intellect

Safely inspect Prime Intellect identity and plan compute or training workflows using the installed prime CLI and current official documentation.

58

Quality

73%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./backend/cli/skills/ml-training/prime-intellect-lab/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%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.

A lean, well-structured, safety-conscious skill: the workflow has explicit approval gates, post-mutation reconciliation, and artifact-level verification, and the credential boundary section captures facts Claude could not infer. Its main weakness is actionability beyond the identity check — the operational steps defer almost everything to '--help' without even one concrete example subcommand per product area.

DimensionReasoningScore

Conciseness

The body is dense and efficient with no explanation of concepts Claude already knows; sections like 'OpenScience credential boundary' deliver novel, non-derivable facts per token. It is not a 5 because a few advisory sentences are repeated or could be trimmed (e.g. 'Do not claim that a product is private beta...' re-appears in spirit in 'Never present a successful identity check as proof...').

4 / 5

Actionability

Concrete executable commands exist for the identity check ('prime --help', 'prime whoami'), but the rest of the workflow stays at the guidance level — 'Use the relevant command's --help', 'Read the current command reference', 'reconcile state with a read-only list/status command' — without a single concrete subcommand example for pods, environments, or training. This matches the 'some concrete guidance but incomplete' anchor; it does not reach 4 because the executable coverage has significant gaps.

3 / 5

Workflow Clarity

The 8-step workflow is clearly sequenced with explicit validation checkpoints and error-recovery guidance: approval gates before any paid mutation, 'save the returned resource ID and reconcile state with a read-only list/status command', 'Never blindly retry after an ambiguous timeout', and 'Never report training success from job state alone; inspect the requested artifacts and evaluation result'. This exceeds the level-4 anchor (which tolerates minor validation gaps) and matches the level-5 anchor's explicit validation plus error-recovery loop.

5 / 5

Progressive Disclosure

There are no bundle files (no references/, scripts/, or assets/), and the body is under 50 lines of well-organized sections with a clearly signaled 'Sources of truth' section pointing one level deep to external documentation URLs. Per the rubric's guideline for skills under 50 lines with no need for external references, this matches the top anchor.

5 / 5

Total

17

/

20

Passed

Description

57%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 is accurate and distinctive thanks to the named platform, but it reads as a capability summary rather than a trigger. It lacks a 'Use when...' clause and the natural vocabulary (GPU, pods, environments, pricing) users would actually employ when needing this skill.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user mentions Prime Intellect, needs to check their prime CLI identity/entitlements, or wants to rent GPUs, launch pods, publish environments, or run hosted training.'

Enumerate 2-3 more concrete actions in place of the generic 'plan compute or training workflows' — e.g. 'check GPU availability and pricing, provision pods, publish evaluation environments, launch training runs'.

Include common synonyms and surface names users say (GPU compute, pods, clusters, Prime Intellect marketplace/dashboard) to improve trigger-term coverage.

DimensionReasoningScore

Specificity

The description names the domain ('Prime Intellect identity') and two concrete action areas ('inspect... identity' and 'plan compute or training workflows'), but does not enumerate the specific operations (e.g., checking pod status, listing GPU availability, launching environments), matching the '1-2 concrete actions, not comprehensive' anchor. It is not a 4 because it stops at two generic action verbs rather than 'several specific actions'.

3 / 5

Completeness

The 'what' is clear (inspect identity, plan compute/training workflows), but there is no 'Use when...' clause or equivalent explicit trigger guidance; per the judging guidelines a missing 'when' clause caps completeness at 3. It is above 2 because the 'what' is concrete, and below 4 because the 'when' is entirely absent rather than merely weak.

3 / 5

Trigger Term Quality

Relevant keywords are present ('Prime Intellect', 'prime CLI', 'compute', 'training', 'identity') but common natural variations users would say are missing — 'GPU', 'pods', 'rent GPUs', 'cluster', 'environments', 'primeintellect.ai'. This matches the 'some relevant keywords but missing common variations or synonyms' anchor rather than the 'good keyword coverage' level.

3 / 5

Distinctiveness Conflict Risk

'Prime Intellect' is a specific named platform with a specific CLI, giving the description a clear niche with distinct triggers and minimal conflict risk against other compute/training skills. Anti-drift check: the level-4 anchor ('minor overlap risk with closely related skills') is too weak since the platform name itself disambiguates.

5 / 5

Total

14

/

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

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/ml-training/prime-intellect-lab/SKILL.md

Reviewed

Table of Contents

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