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trellis-session-insight

Reach into past AI conversation history through the `trellis mem` CLI. Use whenever the user asks 'how did we solve X last time', 'have we discussed this before', 'what was the decision on X', 'remind me what we did in this task', '上次怎么解的', '之前讨论过吗', '想起一段对话', or when starting a brainstorm that overlaps prior work, debugging a familiar bug, continuing a task across sessions, or doing a finish-work review. Returns raw past dialogue; decide for the moment whether to update spec, append to task notes, quote inline in the answer, or just internalize.

80

Quality

100%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

100%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-crafted: concise yet actionable, with concrete executable commands and a clear decision structure, while pushing detailed flag reference and verbatim phrasings into verified one-level-deep bundle files. No significant weaknesses.

DimensionReasoningScore

Conciseness

The body is lean, assumes Claude's competence, and explains only `trellis mem`-specific facts rather than general concepts, with tight aphoristic framing ('It is a tool, not a ceremony').

3 / 3

Actionability

Provides fully executable, copy-paste-ready bash commands (`trellis mem search`, `extract ... --phase brainstorm`, `context ... --turns 3 --around 2`) with real flags and arguments.

3 / 3

Workflow Clarity

Though explicitly a capability skill rather than a fixed workflow, its decision logic is clearly sequenced via the 'When to reach / When NOT / What to do with results' structure; as a read-only operation it needs no validation loop, fitting the simple-skills allowance for an unambiguous single action.

3 / 3

Progressive Disclosure

The body is an overview pointing to two real, one-level-deep, clearly signaled references (`references/cli-quick-reference.md`, `references/triggering-patterns.md`) that were verified to exist and hold the promised detail.

3 / 3

Total

12

/

12

Passed

Description

100%Weight 40%Scale 1-3

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 comprehensive: it specifies the tool and concrete actions, provides rich bilingual natural-language triggers, and explicitly states both what the skill does and when to use it. It is distinctive and unlikely to conflict with other skills.

DimensionReasoningScore

Specificity

Names the concrete tool (`trellis mem` CLI) and lists multiple specific concrete actions including returning raw dialogue and several disposition options (update spec, append notes, quote inline, internalize), matching the score-3 anchor for listing multiple specific actions.

3 / 3

Completeness

Clearly answers both 'what' (reaches into past conversation history via `trellis mem`) and 'when' with an explicit 'Use whenever...' trigger clause enumerating multiple scenarios.

3 / 3

Trigger Term Quality

Includes verbatim natural phrasings users would say in both English ('how did we solve X last time') and Chinese ('上次怎么解的'), plus scenario triggers like familiar-bug debugging — strong natural-term coverage.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (cross-session memory recall via `trellis mem`) with distinct bilingual triggers specific enough to be unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
EcoPasteHub/EcoPaste
Reviewed

Table of Contents

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