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discovery-research-synthesis

Turning research artifacts into actionable PM insight. Customer interviews, user research notes, support ticket reviews, sales call transcripts, survey data, in-app feedback, all synthesized into the decisions they are meant to inform. The discipline of moving from raw discovery data to clear product direction without losing signal in the synthesis or fabricating insight that was not actually there. Triggers on research synthesis, customer interview synthesis, user research analysis, discovery readout, research insights, sales call analysis, support ticket analysis, qualitative data analysis. Also triggers when a team has done research but cannot turn it into decisions, when synthesis is producing pretty decks but no roadmap movement, or when an upcoming PM decision needs to be grounded in research already conducted.

68

Quality

84%

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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 well-architected instruction skill with excellent progressive disclosure, a clearly sequenced and validated workflow, and concrete actionable guidance. Its main weakness is conciseness — the body restates its keystone framing and adds motivational prose that pads token cost without adding actionable signal.

Suggestions

Trim the 'Closing: synthesis is where research earns its keep' section and other restatements of the data-dump/insight-theater framing that recur across sections; the keystone is established once and does not need re-argument.

Cut motivational sentences like 'The prep work is unglamorous; teams that skip it never reach the later stages' that restate what the numbered sequence already implies.

Reduce the 'Common failure modes' list redundancy with the inline failure-mode callouts already present in each section to lower token cost without losing diagnostic value.

DimensionReasoningScore

Conciseness

The body is mostly efficient and well-structured but noticeably padded with restated framing and motivational prose (e.g., the closing 'synthesis is where research earns its keep' section and repeated data-dump/insight-theater explanations) that re-explain points Claude can already infer, placing it just above the verbose midpoint rather than lean.

3 / 5

Actionability

Provides concrete, specific guidance throughout — named synthesis stages, concrete pattern-name examples ('Onboarding configuration friction' vs 'users had trouble'), and numeric batch sizes (8-15 interviews, 100-500 tickets) — with only minor gaps since much of it is instructional rather than executable code, which fits an instruction skill.

4 / 5

Workflow Clarity

The six-stage synthesis sequence is explicitly numbered and ordered with a non-skipping mandate, and the review-and-validation loop section gives clear feedback loops (participant review -> adjacent-team review -> challenge session -> iterate before publish), satisfying explicit checkpoints and error-recovery for the core workflow.

5 / 5

Progressive Disclosure

Clear overview in SKILL.md with well-signaled one-level-deep references: every major section ends with a 'Detail in [`references/...`]' link, all 9 referenced files exist, and a consolidated 'Reference files' index maps each file to its contents, making navigation easy.

5 / 5

Total

17

/

20

Passed

Description

91%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, third-person description that answers both what and when with rich natural trigger phrases and concrete artifact types. It is comprehensive on triggers and completeness, with only minor specificity and distinctiveness gaps.

DimensionReasoningScore

Specificity

Lists several concrete action areas — 'Customer interviews, user research notes, support ticket reviews, sales call transcripts, survey data, in-app feedback, all synthesized into the decisions they are meant to inform' — naming domain and multiple synthesis activities, with minor gaps (no single verb like 'tag' or 'cluster' is enumerated, keeping it just below 5).

4 / 5

Completeness

Explicitly answers both 'what' ('Turning research artifacts into actionable PM insight... synthesized into the decisions they are meant to inform') and 'when' ('Triggers on... Also triggers when...'), with concrete trigger phrases throughout.

5 / 5

Trigger Term Quality

Comprehensive natural trigger coverage including synonyms and phrases users actually say: 'research synthesis, customer interview synthesis, user research analysis, discovery readout, research insights, sales call analysis, support ticket analysis, qualitative data analysis', plus situational phrasings about teams unable to turn research into decisions.

5 / 5

Distinctiveness Conflict Risk

Clearly niched to discovery research synthesis with distinct triggers, and explicitly disambiguates from sibling skills in the body (user-feedback-aggregation, jtbd-framing); the description itself does not name those siblings, leaving minor overlap risk with adjacent research skills, so just below 5.

4 / 5

Total

18

/

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
rampstackco/claude-skills
Reviewed

Table of Contents

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