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nbj-ob1-agent-memory-openclaw

Use Nate Jones OB1 Agent Memory from OpenClaw with provenance, scope, review, and use-policy discipline.

59

Quality

67%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./integrations/openclaw-agent-memory/plugin/skills/openclaw-agent-memory/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

The body is highly actionable with concrete tools, parameters, and policies, and its workflow is clearly sequenced with explicit gating and checklists. Its main weaknesses are minor redundancy in the evidence/instruction-promotion guidance and the absence of any progressive file structure for the specialized modes.

Suggestions

Merge the two adjacent sentences about evidence-to-instruction promotion into a single rule to remove the redundancy and tighten token use.

Consider splitting the Code Review Memory and TaskFlow Work Log modes into separate reference files (e.g. references/code-review.md) referenced from the main body, so the general workflow stays lean and the specialized detail is progressively disclosed.

Add a one-line worked example of a recall or write-back call with concrete parameter values to make the already-actionable guidance copy-paste ready.

DimensionReasoningScore

Conciseness

The body is mostly lean and operational with no padding about concepts Claude already knows, but it could be tightened — notably the two adjacent sentences about memory promotion ('Agent-written memory starts as evidence by default...') and ('Decision memories can become future instructions only when... user_confirmed or imported...') restate the same evidence-to-instruction rule, matching 'mostly efficient but includes some unnecessary explanation or could be tightened'.

2 / 3

Actionability

For an instruction-only skill the guidance is concrete and specific: exact tool names (openbrain_recall, openbrain_writeback, etc.), explicit parameter lists (task_type, query, entities, scope, limits, sensitivity), enumerated write-back categories, and named use_policy fields; per the scoring notes, absence of code is not penalized when guidance is this actionable.

3 / 3

Workflow Clarity

The Core Rule states the sequence (recall before work, write back after), sections follow that order, and there are explicit validation/gating steps (use_policy levels, project_only/include_unconfirmed defaults), a feedback loop (conflict-resolution rule), a fallback (tools unavailable -> continue and note), and checklist-style mode sections, matching 'clear sequence with explicit validation steps; feedback loops; checklists'.

3 / 3

Progressive Disclosure

The skill is a single ~110-line file with well-organized sections and no nested/deep references (good), but it exceeds the 'under 50 lines' allowance for a no-reference skill and keeps two specialized modes (Code Review Memory, TaskFlow Work Log) inline that could be externalized, matching 'some structure but content that should be separate is inline'.

2 / 3

Total

10

/

12

Passed

Description

57%

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 distinctive and names its niche clearly, but it relies on abstract discipline nouns instead of concrete actions and omits any explicit 'Use when' trigger, leaving the 'when to use it' half of completeness only implied.

Suggestions

Add an explicit 'Use when...' clause naming concrete user-facing scenarios (e.g. 'Use when an OpenClaw task has OB1 memory tools and needs continuity across runs').

Replace or supplement the abstract nouns ('provenance, scope, review, use-policy discipline') with concrete actions such as 'recall prior decisions, write back compact lessons, report which memories were used'.

Include natural term variations a user might actually say (e.g. 'OB1 memory', 'agent continuity', 'recall and write-back') alongside the product names.

DimensionReasoningScore

Specificity

The description names the domain ('Nate Jones OB1 Agent Memory from OpenClaw') and several named capabilities ('provenance, scope, review, and use-policy discipline'), but these are abstract discipline nouns rather than the concrete verbs (e.g. 'extract', 'fill', 'merge') that anchor 3 rewards; it sits at 'names domain and some actions, but not comprehensive'.

2 / 3

Completeness

It states what the skill does ('Use ... Agent Memory ... with provenance, scope, review, and use-policy discipline') but lacks an explicit 'Use when...' trigger clause; per the judging guidelines a missing explicit trigger caps completeness at 2.

2 / 3

Trigger Term Quality

It includes the relevant ecosystem keywords a user in that context would say ('OB1 Agent Memory', 'OpenClaw'), but the remainder is technical jargon ('provenance, scope, review, use-policy discipline') with no common natural-language variations, matching 'some relevant keywords but missing common variations'.

2 / 3

Distinctiveness Conflict Risk

The product-specific names ('Nate Jones OB1 Agent Memory', 'OpenClaw') carve out a clear niche with distinct triggers that are unlikely to fire for unrelated skills, matching 'clear niche with distinct triggers; unlikely to conflict'.

3 / 3

Total

9

/

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
NateBJones-Projects/OB1
Reviewed

Table of Contents

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