Name: 1k-cold-start-ssr
Rating: 72.8 (1 reviews)
Author: OneKeyHQ

1k-cold-start-ssr

Jotai Cold Start SSR + unified startup timing schema — cold start optimization via MMKV snapshot hydration and the cross-platform `[StartupTiming]` log taxonomy for OneKey native app. Use when debugging startup performance regressions, analyzing cold start timeline, comparing iOS vs Android startup phases, or modifying the snapshot hydration pipeline. Triggers on: cold start, startup optimization, 启动时间, SSR hydration, Balance displayed regression, MMKV snapshot, contextAtomBase, flushColdStartCache, __ONEKEY_CTX_ATOM_SNAPSHOT__, StartupTiming, main_host.did_start, bg_runner.start, ios.main_entry.evaluated, android.app.on_create, android.activity.on_create.

Quality

88%

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Securityby

Passed

No findings from the security scan

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 dense, highly actionable skill body with executable code, clear diagnostic workflows, and explicit validation checkpoints, but it is verbose and monolithic with no external reference files despite material that could be progressively disclosed. Splitting baseline data and detailed tables into reference files would improve both conciseness and progressive disclosure.

Suggestions

Move the measured baseline timing tables (Android/iOS baselines, iOS-vs-Android comparison) into a reference file (e.g. references/startup-baselines.md) and keep SKILL.md as an overview with a one-level pointer; update baselines there rather than inline.

Tighten or gate the iOS 'TBD, awaiting fixed build' section — summarize the known lazy-init bug and deferred re-baseline in a sentence, or move to the same reference file, to avoid carrying stale/placeholder content in the main body.

Extract the detailed regression-pattern and SSR-cache-key reference tables into a separate reference doc so the main SKILL.md surfaces the core architecture and workflow while keeping deep lookup material one level deep.

Dimension	Reasoning	Score
Conciseness	The body assumes Claude's competence (no basic-concept padding) and is high-signal throughout, but at ~490 lines it carries exhaustive measured baseline timing tables and an iOS 'TBD, awaiting fixed build' section that could be tightened — the 'mostly efficient but could be tightened' anchor rather than 'every token earns its place'.	2 / 3
Actionability	Provides copy-paste-ready executable Phase 1/2/3 TypeScript snippets, concrete bash grep pipelines, and exact file paths / MMKV key names, matching the 'fully executable code/commands; copy-paste ready' anchor and above the pseudocode/incomplete score-2 anchor.	3 / 3
Workflow Clarity	The 'Diagnosing Cold Start Regressions' section is a clear Step 1–6 sequence with explicit validation checkpoints and expected outputs ('Expected: N keys (+XXXms)'), plus a symptom→cause→fix table, matching the 'clear sequence with explicit validation steps; feedback loops' anchor rather than the checkpoint-missing score-2 anchor.	3 / 3
Progressive Disclosure	Content is well-sectioned but entirely monolithic — no references/, scripts/, or assets/ bundle files exist and all detail (baseline timing tables, regression pattern tables) is inline, matching the 'content that should be separate is inline' anchor; it is not a disorganized wall of text (score 1) and lacks the one-level-deep external references needed for score 3.	2 / 3
	Total	10 / 12 Passed

Description

100%

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 highly specific, well-structured description that cleanly answers what the skill does and when to use it, with strong natural and technical trigger coverage. It uses third-person voice and avoids vague fluff or over-claims.

Dimension	Reasoning	Score
Specificity	Names multiple concrete actions — 'cold start optimization via MMKV snapshot hydration', 'debugging startup performance regressions', 'analyzing cold start timeline', 'comparing iOS vs Android startup phases', 'modifying the snapshot hydration pipeline' — matching the 'lists multiple specific concrete actions' anchor rather than the score-2 partial coverage.	3 / 3
Completeness	Explicitly answers 'what' (MMKV snapshot hydration + cross-platform StartupTiming taxonomy for OneKey) and 'when' via an explicit 'Use when debugging…analyzing…comparing…or modifying…' clause, satisfying the 'both what AND when with explicit triggers' anchor; clearly above score 2 where 'when' is only implied.	3 / 3
Trigger Term Quality	Covers natural user terms ('cold start', 'startup optimization', '启动时间') alongside a rich explicit 'Triggers on:' list (StartupTiming, MMKV snapshot, Balance displayed regression, main_host.did_start), giving good coverage of terms a user would actually say; not merely 'some relevant keywords' (score 2).	3 / 3
Distinctiveness Conflict Risk	Highly specific niche — 'Jotai Cold Start SSR', 'OneKey native app', and distinctive identifiers like contextAtomBase, flushColdStartCache, __ONEKEY_CTX_ATOM_SNAPSHOT__ make conflict with other skills unlikely, matching the 'clear niche with distinct triggers' anchor.	3 / 3
	Total	12 / 12 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: OneKeyHQ/app-monorepo
Path: .skillshare/skills/1k-cold-start-ssr/SKILL.md
Commit: d71e6b7

Reviewed: about 5 hours ago

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