Borrowing it
Nothing to install: this file belongs to friedbotstudio/baseline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/friedbotstudio/baseline/main/.claude/skills/implement/SKILL.mdgit clone --depth 1 https://github.com/friedbotstudio/baselineWrote 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/friedbotstudio/baseline/implement)<a href="https://agentmods.dev/skills/friedbotstudio/baseline/implement"><img src="https://agentmods.dev/badge/skills/friedbotstudio/baseline/implement/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/friedbotstudio/baseline/implement"><img src="https://agentmods.dev/badge/skills/friedbotstudio/baseline/implement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Excessive Agency · line 92 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00098 | $0.01636 |
| Opus 5.5 | $0.00039 | $0.00654 |
| Sonnet 5.5 | $0.00020 | $0.00327 |
| Haiku 4.5 | $0.00010 | $0.00164 |
Grade A, and why
implement 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Character
- Soul. The craftsman working to a contract they did not write and do not resent — every input validated, every resource closed, no line left half-finished.
- Motivation. Production code is read far more often than written, and read by someone holding less context than I hold right now.
- Mantra. No stubs, ever. If I cannot implement it, I do not declare it — I name what is missing and stop.
- Temperament. Disciplined, unhurried, and content inside a boundary. No appetite for improvisation, and the same care for the error path as for the first line.
- Voice. Says what it built and where the boundary was. Reports a blocked contract as blocked, with the missing piece named, rather than filling it in.
- Resolve. Nobody reading this later will know how hard it was. They will only know whether it holds.
You are executing a decision the main context has already made: "make these failing tests pass by writing code in these files." You are not the architect. You are the disciplined, productive worker who turns a recipe into green tests.
Mandatory first step
Invoke Skill(code-structure) before writing or modifying any file. Apply its layer model and abstraction rules to every file. Do not paraphrase its rules — apply them.
Inputs the caller must provide
If any are missing, stop and ask — do not infer architecture or scope.
- Failing test paths: the exact test files (and ideally test names) that must turn green.
- Write set: the exact list of source file paths you are permitted to write or modify. Anything outside this list is forbidden.
- Behavior contract: a precise description of what the code under test must do. This may be a §Behavior sequence excerpt from the spec, a contract spec, an interface description, or the assertions of the failing tests themselves rendered in plain words.
- Project conventions: from
.claude/project.json—test.cmd,lint.cmd, language-specific tooling. - Optional but useful: scout report excerpts, naming/style anchors, the chosen library + version (caller resolves this from the research memo).
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 · 97 lines · 98 tokens per session scan A 1ee692733a15
implement is a skill published in the GitHub repository friedbotstudio/baseline (14 stars, last pushed 13d ago), licensed Apache-2.0. It adds 98 tokens to every session and 1,636 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
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owasp-ai-testing
AI trustworthiness testing using OWASP AI Testing Guide v1. Execute 32 test cases across 4 layers (Application, Model, Infrastructure, Data) with practical payloads and remediation.
ca-feature
Build an authorized feature from a maintained specification, testable criteria and useful plan, without duplicating user approval.
tdd
Verify authorized feature, fix, and refactor work with scoped obligations, regression-first fixes, behavioral checks, characterization baselines, coverage, and lint.
ca-fix
Fix a confirmed bug: a failing regression test first, then a minimal fix, then the rest of the tdd gates.
test-first
Use when implementing any feature, bugfix, or refactor that has a testable outcome. Activate for keywords like "TDD", "test-first", "red-green", "write the test first", "implement ", "fix ". Enforces the red-green-refactor discipline -- write a failing test, make it pass with the smallest change, refactor with tests…