SKILLEMALL.ai
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BC OrchardOS

Agentic project and task management plugin for OpenClaw. Persistent SQLite-backed task board with a queue runner that auto-dispatches ready tasks as subagents, REST API, native agent tools, and a web dashboard.

ClawHub Agent Skills author: derp42 v0.2.5-rc.5 MIT-0 30 files body ≈ 521 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
77/100
safety, quality, tests
Safety 60%
88
Quality 40%
60
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 8

✓ No critical or high findings

Medium and low: 8
  • medium Exfiltration net-credential-use docs/ux-brief.md:196
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $TOKEN" http://127.0.0.1:18789/orchard/projects
  • low Secrets in code secret-high-entropy-token package-lock.json:183
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…FSj+bWLt…2sH/Kn8E…h6w==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:287
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…wHD+vkj3…wBQ/hCAQ…tUp/3Qh6…OOw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:293
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…CpK+FtMRQVdIMN6/Df5j…tIC+7KYK…qaA==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:346
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…RFW+TK4J…oUr/txX3…6Ns/A==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:370
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…JgI+2Q5U…e1E+Nyvgdz/aIyN…n58/GELp3+w==",
    detector
  • low Exfiltration exfil-secret-in-url src/kb/knowledge.ts:32
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)
    const url = `https://generativelanguage.googleapis.com/v1beta/models/${model}:embedContent?key=…
    placeholder
  • low Exfiltration net-credential-use src/ui/dashboard.generated.ts:4
    Credential used in a network call (verify the destination is the intended service) (detector / deny-list definition; code comment; security demo / example)
    export const DASHBOARD_HTML = "<!DOCTYPE html>\n<html lang=\"en\" data-theme=\"dark\">\n<head>\n  <meta charset=\"utf-8\">\n  <meta http-equiv=\"Cache-Control\" content=\"no-cache, no-store, must-reva
    detectorcommentdemo

Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (OrchardOS) differs from the folder (orchard)
  • 55Failures and branches. 1 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 10 steps
  • 100Execution cost. Instruction body is 521 tokens
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • -32 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 210: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: suspicious
The skill is broadly coherent as an OpenClaw task manager, but it includes sensitive operational and data-flow behavior that users should review before installing.
LLM: suspicious (high) · VirusTotal: · 29 May 2026