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 agentmods add commands/zevtos/agentpipe/testgit clone --depth 1 https://github.com/zevtos/agentpipeWhat 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 | $0.00025 | $0.00751 |
| Opus 5 | $0.00013 | $0.00376 |
| Sonnet 5 | $0.00005 | $0.00150 |
| Haiku 4.5 | $0.00003 | $0.00075 |
Grade A, and why
test 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are orchestrating comprehensive test coverage for the project. This goes beyond "write some unit tests" — it's a full test strategy with risk-based prioritization.
Context
@CLAUDE.md
Test Target
$ARGUMENTS
Pipeline
Step 1: Coverage Analysis
Before writing any tests:
- Identify the testing framework in use (check package.json, pyproject.toml, Cargo.toml, etc.)
- Run existing tests to establish baseline: what passes, what fails, what's slow
- If coverage tooling exists, run it to identify untested code paths
- Map critical code paths that MUST be tested (auth, payments, data mutations, business rules)
- Identify which test types already exist (unit, integration, e2e)
Present:
- Current coverage: what's tested, what's not
- Risk map: critical untested code paths ranked by risk
- Test strategy: which types of tests to add and why
Step 2: Test Implementation (Tester Agent)
Run the tester agent:
"Analyze the codebase and implement comprehensive tests.
Target: $ARGUMENTS (if empty, focus on highest-risk uncovered code)
Current test coverage: [paste from Step 1]
Write tests in this priority order:
- Critical business logic — domain rules, calculations, state transitions
- API endpoints — request validation, response format, error handling, auth
- Data layer — queries return correct results, constraints enforced, migrations work
- Error paths — what happens when things fail (network, DB, invalid input)
- Edge cases — boundary values, empty inputs, concurrent access
For each test file:
- Follow existing test patterns and conventions
- Use real dependencies where possible (mock only external HTTP services)
- Name tests descriptively: test_[scenario]_[expected_result]
- Include setup/teardown for clean test isolation
Consider advanced strategies where they add value:
- Property-based testing for serialization/deserialization round-trips
- Property-based testing for algorithmic invariants
- Contract tests if there are service-to-service APIs
- Fuzzing for parsers or security-critical input processing"
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.
- 2d ago First seen · 87 lines · 25 tokens per session scan A 7f91417ee8fb
test is a command published in the GitHub repository zevtos/agentpipe (11 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 751 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.