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 ryan-scheinberg/harness --skill complete-slicegit clone --depth 1 https://github.com/ryan-scheinberg/harnessWrote 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/ryan-scheinberg/harness/complete-slice)<a href="https://agentmods.dev/skills/ryan-scheinberg/harness/complete-slice"><img src="https://agentmods.dev/badge/skills/ryan-scheinberg/harness/complete-slice/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/ryan-scheinberg/harness/complete-slice"><img src="https://agentmods.dev/badge/skills/ryan-scheinberg/harness/complete-slice.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 | $0.00037 | $0.00614 |
| Opus 5.5 | $0.00015 | $0.00246 |
| Sonnet 5.5 | $0.00007 | $0.00123 |
| Haiku 4.5 | $0.00004 | $0.00061 |
Grade A, and why
complete-slice 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 1mo 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Take one slice from SLICES.md and ship it. Often for dev work, but might be a different task that you should still complete to the standards of the company
Process
-
Read the slice and the brief. Find the target slice. Internalize what it delivers. Read
PROJECT_BRIEF.mdfor architectural context -
Orient in the codebase. Module boundaries, test patterns, naming, DI style, config approach, what prior slices built. Your code should feel native — same seams, same style. Greenfield: scaffolding + test runner + lint + CI belong inside slice 1, not as separate phases
-
Plan the TDD sequence. Map acceptance criteria to red-green-refactor cycles. Start with the criterion that proves the core approach. Decide what's a real collaborator and what's a system boundary to mock. Keep the plan light. Use judgement for when to use TDD or take a different approach
-
Build one test at a time. Red → green → refactor. One failing test, minimum code to pass, clean up while green, next. Public interfaces only; mock system boundaries only; tests survive internal refactors. Writing all tests first produces tests that describe imagined behavior, not actual. See tdd-reference.md for the full methodology
-
Bake SRE in as you go. Structured logs at decision points and error paths; metrics on the behaviors this slice introduces; health checks on new endpoints; errors that fail explicitly at boundaries with context; config externalized. Operational readiness isn't a final step — if it's not present when tests go green, it won't be added
-
Verify. All acceptance criteria met and covered by passing tests. Observable outcomes demonstrably work — run it, hit the endpoint, read the logs. Full suite green, not just new tests. No unrelated changes mixed in
-
Consider future agents by running
updating-ai-knowledge, often forAGENTS.md -
Close the slice. Check the boxes in
SLICES.md. Follow-up work, discovered edge cases, or risks land in the relevant future slice, or in a## Notessection at the bottom ofSLICES.md -
Report what you shipped and verified back to whoever ran you
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 35 lines · 37 tokens per session scan A e34feefe9955
complete-slice is a skill published in the GitHub repository ryan-scheinberg/harness (2 stars, last pushed 3mo ago), licensed MIT. It adds 37 tokens to every session and 614 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
user-story-writer
Escribir historias de usuario bien estructuradas con criterios de aceptación y casos extremos. Úsalo cuando te pidan escribir historias de usuario, crear tickets a partir de un resumen de características, convertir un PRD en historias o redactar criterios de aceptación. Produce historias listas para estimar en formato…
tdd-workflow
A test-first development workflow, commonly called TDD, where a failing test is written before the code that should make it pass. The cycle then adds the smallest working change and cleans it up.
python-testing
Expert in Python testing with pytest and test-driven development.
user-story
Create user stories with Mike Cohn format and Gherkin acceptance criteria. Use when turning user needs into development-ready work with clear outcomes and testable conditions.
nl-to-constraints
Transforms natural language requirements (user stories, verbal descriptions, business rules) into formal specifications and constraints. Use when converting informal requirements into structured, testable specifications with explicit constraints. Outputs in multiple formats including BDD-style Given-When-Then, JSON…
lsp-refactoring
Intelligent code refactoring using IDE-level tools (rename, find-references, go-to-definition), AST-aware pattern matching, and TDD verification. Use for safe, large-scale refactoring with precision.