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deep-research

Creates reusable, source-traced research reports and coordinates provider/mode lanes with original exports. Use for 深度研究, 调研报告, literature reviews, market or company research, and ChatGPT/Kimi/UniFuncs research routes. Technology choice uses tech-selection; competitor code uses competitors-analysis.

61

Quality

77%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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

Quality

Content

67%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 dense, highly procedural skill body with genuine validation gates, real script contracts, and a well-organized one-level-deep reference bundle. Its weaknesses are redundancy — duplicated enterprise subsections and repeated dimension/task mappings — and an over-long main body that inlines material already living in reference files.

Suggestions

Collapse the duplicated enterprise steps: keep a single E1–E7 sequence and delete the overlapping "E3: Select Analysis Frameworks…", "E4: Quality Control", and "E5: Draft Using Enterprise Template" subsections that are re-stated under "E3-E7: Enterprise Analysis, Drafting, and Review".

Remove the Task A–F restatement in the P1 "Enterprise Research Integration" section (it repeats the D1–D6 dimensions already shown in the Enterprise Workflow Overview) and replace it with a one-line pointer.

Move the source accessibility classification tables and the information-black-box handling procedure out of SKILL.md into references/source_accessibility_policy.md, keeping only a short summary and the load trigger, so the main body reads as an overview.

DimensionReasoningScore

Conciseness

The body contains essentially no explanation of concepts Claude already knows — nearly all of the 594 lines are non-obvious procedural rules — but it is noticeably redundant: the E3/E4/E5 enterprise steps appear twice ("E3: Select Analysis Frameworks…"/"E4: Quality Control"/"E5: Draft…" and again under "E3-E7: Enterprise Analysis, Drafting, and Review"), and the D1–D6 dimension list is repeated as Task A–F in the P1 section. This fits "mostly efficient but could be tightened" rather than score 2, since the padding is duplicated structure, not explanatory filler.

3 / 5

Actionability

Concrete, executable guidance throughout: named script invocations ("provider_runs.py plan", "provider_runs.py validate", "research_assets.py check"), concrete file names (study.json, source-ledger.jsonl, claims.jsonl), exact registry/status output templates, and a fully specified 11-field task-assignment format. It falls short of score 5 because no example command syntax or arguments for the two scripts are shown inline, and a few directives remain abstract ("Assign one owner per control surface").

4 / 5

Workflow Clarity

The P0–P7 pipeline is clearly sequenced with explicit validation checkpoints (validate/check commands required "before calling a report complete", P7 cross-check list, L1/L2/L3 quality gates) and per-phase status report lines. It does not reach score 5 because the enterprise pipeline is interleaved confusingly into the general P2 section and the duplicated E3–E5 subsections make the true execution order ambiguous for Enterprise Mode.

4 / 5

Progressive Disclosure

All 18 referenced files exist (verified), references are one level deep, and they are organized into three tables with explicit "When to Load" columns plus phase-anchored inline links (e.g., "load parallel-provider-ops.md before fan-out"). It misses score 5 because the main body itself is very long (594 lines) and inlines material — the source accessibility classification tables and the information-black-box procedure — that duplicates content in references/source_accessibility_policy.md rather than deferring to it.

4 / 5

Total

15

/

20

Passed

Description

78%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 well-crafted description with an explicit trigger clause, bilingual natural keywords, and deliberate boundary disambiguation against sibling skills. Its main weakness is that only two concrete capabilities surface from what is a very large skill body, and the broad research framing invites some overlap with generic search tasks.

DimensionReasoningScore

Specificity

The description names the domain and two concrete actions — "Creates reusable, source-traced research reports" and "coordinates provider/mode lanes with original exports" — but omits other core capabilities from the body (citation registry, counter-review, quality gates), so coverage is not comprehensive. It is above score 2 (which expects only minimal/generic actions) but below score 4, which requires several specific actions with only minor gaps.

3 / 5

Completeness

It explicitly answers both questions: the "what" is stated in third person ("Creates reusable, source-traced research reports and coordinates provider/mode lanes") and the "when" is an explicit "Use for…" clause with concrete trigger phrases. It clearly matches the score-5 anchor rather than score 4, whose "when" is only implicitly or loosely specified.

5 / 5

Trigger Term Quality

Strong natural keyword coverage including bilingual triggers ("深度研究", "调研报告", "literature reviews", "market or company research") and provider-specific routes ("ChatGPT/Kimi/UniFuncs research routes") that users would actually say. It falls short of score 5 because common English variations like "deep research", "due diligence", or "industry analysis" are missing.

4 / 5

Distinctiveness Conflict Risk

The description ends with explicit disambiguation redirects ("Technology choice uses tech-selection; competitor code uses competitors-analysis"), which resolves overlap with the closest sibling skills. It stays at 4 rather than 5 because the fairly broad phrase "market or company research" could still trigger this skill for simple lookup tasks it is not designed for.

4 / 5

Total

16

/

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

skill_md_line_count

SKILL.md is long (595 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

daymade/claude-code-skills

Path

deep-research/SKILL.md

Reviewed

Table of Contents

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