BD wger-fitness
Manage gym routines and fitness tracking in wger via API. Use for viewing, editing, creating workouts, logs, nutrition plans, and progress analysis. Integrates with OpenClaw crons/subagents for automated tracking. Triggers on fitness/gym/wger queries: (1) Log workouts, (2) View routines/plans, (3) Update goals, (4) Generate reports, (5) API-based pulls/pushes.
As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
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.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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".
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
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
medium Exfiltration
net-credential-useSKILL.md:31Credential used in a network call (verify the destination is the intended service)- Create log: exec curl -X POST -H "Authorization: Token $WGER_TOKEN" -H "Content-Type: application/json" -d '{"date": "2026-04-15", "workout": [ID], "exercises": [{"reps": 10, "weight": 135, "exercis -
low Exfiltration
net-credential-useSKILL.md:13Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)- Test: exec curl -H "Authorization: Token $WGER_TOKEN" https://wger.de/api/v2/workout/ (lists routines).
vendor-host -
low Exfiltration
net-credential-useSKILL.md:21Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)- List routines: exec curl -H "Authorization: Token $WGER_TOKEN" "https://wger.de/api/v2/workout/?format=json&limit=5"
vendor-host -
low Exfiltration
net-credential-useSKILL.md:22Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)- Get log: exec curl -H "Authorization: Token $WGER_TOKEN" "https://wger.de/api/v2/workoutlog/?workout=[ID]&format=json"
vendor-host -
low Exfiltration
net-credential-useSKILL.md:32Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)- Update routine: exec curl -X PATCH -H "Authorization: Token $WGER_TOKEN" -H "Content-Type: application/json" -d '{"name": "Updated Cyber Grind"}' https://wger.de/api/v2/workout/[ID]/vendor-host
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Manage gym routines and fitness tracking in wger via API. Use for … ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 47/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 4 mutating operations with no state check
- 40Consistency. Frontmatter name (wger-fitness) differs from the folder (wger-openclaw)
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 16 steps
- 100Execution cost. Instruction body is 751 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 362: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 16 items
- +4Reference files are cited in the instructions (2 of 2)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.