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shadowbroker

Query the ShadowBroker OSINT intelligence platform for real-time geospatial intelligence, place AI intel pins on the map, manage autonomous monitoring, inject data into native layers, fetch satellite imagery, aggregate news, generate intelligence reports, and participate in the Wormhole mesh network.

57

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./openclaw-skills/shadowbroker/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%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 highly actionable with executable code and clear decision routing, but it is large and monolithic: redundant 'Important Rules' restate earlier guidance, destructive/batch operations lack validation checkpoints, and substantial reference tables belong in separate files. Splitting reference material out and adding verification steps for risky operations would lift conciseness, workflow clarity, and progressive disclosure.

Suggestions

Move the enriched-data-field tables, the full signature-key table, and the GT/SAR method tables into separate reference files (e.g., REFERENCE.md, SIGNATURES.md) linked from a concise overview, to improve progressive disclosure and conciseness.

Add explicit validation/confirmation checkpoints for destructive and batch operations (e.g., list pins before `clear_pins()`, confirm count before `clear_analysis_zones()`, verify injection before `clear_injected()`).

Remove the redundant 'Important Rules' section or fold it into the existing 'Agent Fast Path' / 'Decision Framework' to cut repetition and reduce token cost.

DimensionReasoningScore

Conciseness

The body is mostly efficient — dense tables and executable code rather than concept explanations Claude already knows — but at ~770 lines it is voluminous, and the trailing 'Important Rules' section restates the 'Agent Fast Path' and 'Decision Framework' (open SSE first, fetch targeted, geocode before pinning, use signatures). It is not 3 because of this redundancy and bulk, and not 1 because it avoids explanatory fluff and assumes Claude's competence.

2 / 3

Actionability

It provides abundant copy-paste-ready, executable Python with real method names and arguments (e.g., `await sb.place_pin(lat=34.05, lng=-118.24, label=..., category=..., ttl_hours=48)`), plus tables mapping each method to its return value and use case. It is not 2 because the guidance is concrete and complete rather than pseudocode or abstract direction.

3 / 3

Workflow Clarity

A clear numbered 'Decision Framework' (steps 1–8) and per-feature 'rules of engagement' sequence the work well, but destructive and batch operations like `clear_pins()`, `clear_analysis_zones()` ('use sparingly'), and `clear_injected()` lack validation/confirmation checkpoints, which per the guidelines caps workflow clarity at 2. It is not 3 because feedback loops for risky ops are missing, and not 1 because the sequence is explicitly laid out.

2 / 3

Progressive Disclosure

The body is well organized into clear sections, but it is a single ~770-line monolithic SKILL.md with no bundle files and no references — the enriched-field tables, full 20-entry signature table, and detailed GT/SAR method tables are reference material that should live in separate files. It is not 1 because the sectioning prevents a 'wall of text' feel, and not 3 because no content is split out into one-level-deep references.

2 / 3

Total

9

/

12

Passed

Description

67%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 specific and clearly niched to a named platform, but it reads as a capability list rather than trigger guidance: it omits any explicit 'Use when...' clause and relies on some technical jargon over natural user phrasings. Adding explicit trigger conditions and common-speech keywords would raise completeness and trigger_term_quality.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers (e.g., 'Use when tracking flights/ships, geolocating events, fetching satellite imagery of a place, or asking what is happening in a region').

Replace or augment jargon ('Wormhole mesh network', 'inject data into native layers') with user-facing terms ('mesh network messaging', 'add your own data sources to map layers').

Include common natural variations users would actually say, such as 'what's near me', 'track this aircraft/ship', 'military movements', or 'OSINT near a location'.

DimensionReasoningScore

Specificity

The description lists many concrete actions — 'place AI intel pins on the map', 'inject data into native layers', 'fetch satellite imagery', 'aggregate news', 'generate intelligence reports' — matching the score-3 anchor of multiple specific concrete actions. It is not the level below (2) because it goes well beyond naming a domain plus a couple of actions into a comprehensive action list.

3 / 3

Completeness

It strongly answers 'what does this do' with a full capability list, but contains no 'Use when...' clause or equivalent explicit trigger guidance, so per the judging guidelines completeness is capped at 2. It is not 1 because the 'what' is clearly and thoroughly stated, and not 3 because the 'when' is only implied, not explicit.

2 / 3

Trigger Term Quality

It includes some natural terms users would say ('OSINT intelligence', 'satellite imagery', 'news'), but leans on jargon ('Wormhole mesh network', 'inject data into native layers', 'autonomous monitoring') and omits common variations like 'track flights/ships', 'what's near me', or 'military movements'. It is not 3 because coverage of natural user phrasings is incomplete, and not 1 because several genuinely common terms are present.

2 / 3

Distinctiveness Conflict Risk

Tied to a named platform ('ShadowBroker OSINT intelligence platform') with a distinct geospatial-intelligence/mesh-network niche, making it unlikely to trigger for the wrong skill. It is not 2 because the named platform and specific capability set remove the overlap risk that the level-2 anchor describes.

3 / 3

Total

10

/

12

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (782 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository
BigBodyCobain/Shadowbroker
Reviewed

Table of Contents

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