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auto-research

Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis

Quality

78%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

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

Quality

Content

82%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 is a clean, token-efficient dispatcher that delegates to an authoritative playbook with a precise substitution rule. It is highly concise and well-structured, with only minor gaps in executable detail and validation checkpoints.

DimensionReasoningScore

Conciseness

Five lean lines with no padding and no explanation of concepts Claude already knows; every token earns its place by directing to the authoritative playbook and the one substitution rule.

5 / 5

Actionability

Gives concrete executable guidance — specific file paths and a named 'invoke_subagent' function for the substitution — but it is a delegation pointer rather than copy-paste code, leaving a minor gap in how invoke_subagent is called.

4 / 5

Workflow Clarity

The read → execute → follow cog.md → apply substitution sequence is clear and unambiguous, though no explicit validation checkpoints are present (acceptable for a thin dispatcher).

4 / 5

Progressive Disclosure

SKILL.md acts as an overview that delegates to one-level-deep, clearly signaled references (the authoritative playbook, cog.md, per-worker agent files); the references are external system files rather than this skill's bundle, keeping it just short of the top anchor.

4 / 5

Total

17

/

20

Passed

Description

63%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 specific and concrete about capabilities but omits any 'when to use' trigger guidance, which the rubric penalizes by capping completeness. Voice is correctly third person.

Suggestions

Add an explicit 'Use when...' clause naming natural user triggers (e.g., 'Use when the user needs deep, multi-source strategic research or a comprehensive research report').

Include natural synonyms and phrasings users would actually say, such as 'deep research', 'research report', or 'strategic analysis'.

Tighten the trigger terms to reduce overlap with adjacent research/analysis skills by naming the distinctive signals (parallel threads, multi-agent synthesis).

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis' — giving comprehensive coverage of what the engine does.

5 / 5

Completeness

It clearly answers 'what' but provides no 'Use when...' clause or equivalent explicit trigger guidance for when to invoke it, which caps completeness at 3 per the rubric guideline.

3 / 5

Trigger Term Quality

'research' and 'strategic analysis' are relevant but the description lacks common natural variations, synonyms, or user-facing phrasings a person would say, missing terms like 'deep research' or 'research report'.

3 / 5

Distinctiveness Conflict Risk

'Deep strategic research engine' is a fairly distinct niche with low overlap risk, though the absence of explicit trigger phrases keeps it from being unambiguously distinguishable from related research skills.

4 / 5

Total

15

/

20

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

huytieu/COG-second-brain

Path

.agents/skills/auto-research/SKILL.md

Reviewed

Table of Contents

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