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deep-research

Multi-agent research orchestration: split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file. Use for systematic web/document research, competitive or industry analysis, batch link/dataset processing, and long-form evidence synthesis. Triggers: "深度调研", "deep research", "wide research", "多 Agent 调研", "系统调研".

64

Quality

80%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/deep-research/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

70%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 delivers an exceptionally well-sequenced orchestration workflow with concrete, mostly copy-paste-ready bash and strong validation/feedback loops for batch subprocess execution. Its weaknesses are redundancy (the tool-priority and MCP-preference rules are repeated several times, and basic CLI usage is taught inline) and a complete lack of progressive disclosure — everything lives in one long SKILL.md with no reference files.

Suggestions

Deduplicate the tool-priority rule (skills → firecrawl → exa → WebFetch/WebSearch): state it once in the constraints section and reference it from steps 0, 3, and 注意事项 instead of restating it four times.

Move the "Claude Code 非交互模式参考" section and the dispatch/parallel script templates into a references/ file (e.g., references/claude-noninteractive.md), keeping SKILL.md to the workflow itself with one-level-deep, clearly signaled pointers.

Fix the parallel-execution examples so they are fully copy-paste ready: define `$name`/`tasks.txt`/`task_ids.txt` and reconcile the `.json` child outputs with the `.md` aggregation step.

DimensionReasoningScore

Conciseness

Mostly skill-specific operational detail rather than concepts Claude already knows, but noticeably padded: the tool-priority rule (skills → firecrawl → exa → WebFetch/WebSearch) is repeated at least four times (关键约束, step 0, step 3 prompt rules, 注意事项), and the "Claude Code 非交互模式参考" section teaches basic `claude -p` usage Claude can get from `claude --help`. It fits "mostly efficient but includes some unnecessary explanation or could be tightened" rather than the clearly verbose anchor 2, because nearly all content is task-specific.

3 / 5

Actionability

Provides mostly executable guidance: concrete bash templates (`timeout 600 claude -p "$(cat "$prompt_file")" --allowedTools … --output-format json`, `stdbuf -oL -eL … | tee`, GNU parallel and job-control loops), exact directory conventions (`.research/<name>/child_outputs/<id>.md`), and timeout policy (300/900s). Minor gaps keep it below 5: the parallel example references undefined `$name`/`tasks.txt`/`task_ids.txt` and `.json` outputs are never reconciled with the `.md` aggregation step.

4 / 5

Workflow Clarity

Steps 0–9 are strictly sequenced with explicit validation checkpoints and feedback loops appropriate to batch orchestration: mandatory scoping with real sampled evidence before planning, user confirmation gates, per-prompt review before dispatch ("逐一快速审阅生成的 prompt 文件…确认变量替换正确、指令完整后再派发"), failure isolation with retry and `failed_ids` tracking, timeout escalation rules, a pre-delivery "双重体检质检", and a full 交付前自检清单 covering directory, compliance, quality, and failure reporting.

5 / 5

Progressive Disclosure

The skill is a single monolithic ~270-line file with no bundle files (no references/, scripts/, or assets/ exist), and content that clearly belongs in separate files is inlined — notably the general `claude -p` CLI reference section and the dispatch/parallel script templates. Section headers and a size-tier table give it real structure, so it sits at "some structure but could be better organized" rather than the minimal-structure anchor 2.

3 / 5

Total

15

/

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 orchestration actions, an explicit "Use for" scope, and a bilingual trigger list, fully answering both what and when. The only weaknesses are a slightly incomplete synonym set and mild overlap risk on the broad "deep research" trigger.

Suggestions

Add a few natural English synonyms to the trigger list (e.g., "research report", "literature review", "industry/market research") to reach comprehensive keyword coverage.

Sharpen the distinctiveness of the "deep research" trigger by tying it to the orchestration angle (e.g., "when a research goal must be split across parallel subagents") to reduce overlap with generic web-search skills.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — "split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file" — with only minor gaps in coverage (e.g., caching, quality checks, scheduling depth are unstated). It is not a 5 because the action list, while specific, stops short of comprehensively covering the pipeline described in the body.

4 / 5

Completeness

Explicitly answers both questions: what it does ("Multi-agent research orchestration: split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file") and when to use it ("Use for systematic web/document research…" plus a dedicated "Triggers:" list with concrete phrases).

5 / 5

Trigger Term Quality

Includes natural trigger terms in two languages — "Triggers: '深度调研', 'deep research', 'wide research', '多 Agent 调研', '系统调研'" — plus "Use for systematic web/document research, competitive or industry analysis, batch link/dataset processing". A few natural synonyms are missing (e.g., "research report", "literature review", "market/industry research" as literal phrases), so it sits between the good (4) and comprehensive (5) anchors.

4 / 5

Distinctiveness Conflict Risk

The niche is distinct — headless multi-agent orchestration via `claude -p` subprocesses — with its own bilingual trigger terms. Minor overlap risk remains with generic web-research or report-writing skills triggered by the broad phrase "deep research", keeping it just below the clear-niche anchor 5.

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

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository
feiskyer/claude-code-settings
Reviewed

Table of Contents

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