co-tank-monitor

IoT monitoring simulation to predict CO2 tank depletion and prevent weekend gas outages in cell culture facilities. Monitors cylinder pressure, calculates consumption rates, provides early warnings, and supports automated scheduling via cron.

Quality

75%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Other/co2-tank-monitor/SKILL.md

Quality

Content

75%Weight 40%Scale 1-5

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 a clear validated workflow, and it is well structured for a single-bundle skill; the main weakness is redundancy between the Key/Core Capabilities sections and the repeated fallback messaging.

Suggestions

Remove the top 'Key Capabilities' bullet list or fold it into 'Core Capabilities' to eliminate duplication and save tokens.

State the missing-parameter fallback once (e.g. in Error Handling) and reference it from the Workflow rather than repeating the full quote in both places.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with actionable tables and code, but the 'Key Capabilities' bullet list duplicates the 'Core Capabilities' section and the missing-parameter fallback message is repeated in both Workflow and Error Handling, so it could be tightened.	3 / 5
Actionability	Provides fully executable, copy-paste-ready CLI commands, cron snippets, and Python imports that map to real functions in scripts/main.py (e.g. calculate_remaining_days, will_deplete_on_weekend), with parameter tables covering the common cases.	5 / 5
Workflow Clarity	A clear 6-step numbered workflow includes validation (plausible-range check) and a fallback path, plus a Quick Check verification step; minor checkpoint gaps remain but no validate-fix-retry loop is required for this read-only monitoring task.	4 / 5
Progressive Disclosure	Well-organized into clear sections with the single bundle file (scripts/main.py) referenced correctly via inline code and external links gathered in References; minor organization gaps but content is mostly appropriately placed.	4 / 5
	Total	16 / 20 Passed

Description

75%Weight 40%Scale 1-5

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 distinct, naming concrete capabilities and a precise niche, but it omits an explicit 'Use when…' trigger clause so the use-conditions are only weakly implied.

Suggestions

Add an explicit 'Use when…' clause naming the trigger situations, e.g. 'Use when monitoring CO2 cylinder pressure, predicting tank depletion, or assessing weekend gas-outage risk for cell culture incubators.'

Include a couple of natural synonyms or shorthand variants (e.g. 'CO2 cylinder', 'incubator gas', 'tank swap scheduling') to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'predict CO2 tank depletion', 'prevent weekend gas outages', 'Monitors cylinder pressure', 'calculates consumption rates', 'provides early warnings', 'supports automated scheduling via cron' — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	The 'what' is clear and detailed, but there is no explicit 'Use when…' trigger clause; the 'when' is only weakly implied through 'prevent weekend gas outages in cell culture facilities', which per the rubric caps completeness at 3.	3 / 5
Trigger Term Quality	Includes natural domain terms ('CO2 tank', 'gas outages', 'cylinder pressure', 'cell culture', 'cron') a user would plausibly say, but lacks synonyms or file/parameter-style variants that would round out coverage.	4 / 5
Distinctiveness Conflict Risk	Targets a clearly defined niche — CO2 cylinder depletion prediction for cell culture facilities — with distinct triggers and minimal overlap risk against generic monitoring skills.	5 / 5
	Total	17 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Other/co2-tank-monitor/SKILL.md
Commit: f5ef65b

Reviewed: ~14 hours ago

Table of Contents

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