Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add patricio0312rev/skillset --skill coverage-strategistgit clone --depth 1 https://github.com/patricio0312rev/skillsetWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/patricio0312rev/skillset/coverage-strategist)<a href="https://agentmods.dev/skills/patricio0312rev/skillset/coverage-strategist"><img src="https://agentmods.dev/badge/skills/patricio0312rev/skillset/coverage-strategist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/patricio0312rev/skillset/coverage-strategist"><img src="https://agentmods.dev/badge/skills/patricio0312rev/skillset/coverage-strategist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.02412 |
| Opus 5 | $0.00023 | $0.01206 |
| Sonnet 5 | $0.00009 | $0.00482 |
| Haiku 4.5 | $0.00005 | $0.00241 |
Grade A, and why
coverage-strategist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to coverage-strategist — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coverage Strategist
Define pragmatic, ROI-focused test coverage strategies.
Coverage Philosophy
Goal: Maximum confidence with minimum tests
Principle: 100% coverage is not the goal. Test what matters.
Critical Path Identification
// Critical paths that MUST be tested
const criticalPaths = {
authentication: {
priority: "P0",
coverage: "100%",
paths: [
"User login flow",
"User registration",
"Password reset",
"Token refresh",
"Session management",
],
reasoning: "Security critical, impacts all users",
},
checkout: {
priority: "P0",
coverage: "100%",
paths: [
"Add to cart",
"Update cart",
"Apply coupon",
"Process payment",
"Order confirmation",
],
reasoning: "Revenue critical, business essential",
},
dataIntegrity: {
priority: "P0",
coverage: "100%",
paths: [
"User data CRUD",
"Order creation",
"Inventory updates",
"Database transactions",
],
reasoning: "Data corruption would be catastrophic",
},
};
// Important but not critical
const importantPaths = {
userProfile: {
priority: "P1",
coverage: "80%",
paths: ["Profile updates", "Avatar upload", "Preferences"],
reasoning: "Important UX, but not business critical",
},
search: {
priority: "P1",
coverage: "70%",
paths: ["Product search", "Filters", "Sorting"],
reasoning: "Enhances experience, not essential",
},
};
Layer-Specific Targets
# Coverage Targets by Layer
## Business Logic / Core Functions: 90-100%
**Why**: High ROI - complex logic, many edge cases
**What to test**:
- Calculations
- Validations
- State machines
- Algorithms
- Data transformations
## API Endpoints: 80-90%
**Why**: Critical integration points
**What to test**:
- Happy paths
- Error cases
- Validation
- Authentication
- Authorization
## Database Layer: 70-80%
**Why**: Data integrity matters
**What to test**:
- CRUD operations
- Transactions
- Constraints
- Migrations
## UI Components: 50-70%
**Why**: Lower ROI - visual changes, less critical
**What to test**:
- User interactions
- State changes
- Error states
- Critical flows only
## Utils/Helpers: 80-90%
**Why**: Reused everywhere, high impact
**What to test**:
- All public functions
- Edge cases
- Error handling
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 437 lines · 47 tokens per session scan A 946a4927bca8
coverage-strategist is a skill published in the GitHub repository patricio0312rev/skillset (6 stars, last pushed 8mo ago), licensed MIT. It adds 47 tokens to every session and 2,412 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to coverage-strategist, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.