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 EmanuelVogt/skills --skill ca-full-cyclegit clone --depth 1 https://github.com/EmanuelVogt/skillsWrote 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/emanuelvogt/skills/ca-full-cycle)<a href="https://agentmods.dev/skills/emanuelvogt/skills/ca-full-cycle"><img src="https://agentmods.dev/badge/skills/emanuelvogt/skills/ca-full-cycle/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/emanuelvogt/skills/ca-full-cycle"><img src="https://agentmods.dev/badge/skills/emanuelvogt/skills/ca-full-cycle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00113 | $0.03344 |
| Opus 5 | $0.00056 | $0.01672 |
| Sonnet 5 | $0.00023 | $0.00669 |
| Haiku 4.5 | $0.00011 | $0.00334 |
Grade A, and why
ca-full-cycle 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.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CA Full-Cycle — Research · Plan · Implement · Review
One human gate before code, one human loop after it. Everything between runs alone.
┌──────────┐ ┌────────┐ ┌───────────┐ ┌──────────┐ ┌────────┐
│ RESEARCH │ → │ PLAN │ → │ IMPLEMENT │ → │ REVIEW │ → │ QA │
└──────────┘ └────────┘ └───────────┘ └──────────┘ └────────┘
human + AI AI AI AI human
(the gate) top tier worker waves independent fix loop
The human's job is to make the problem understood (Research) and to judge the result (QA). The pipeline's job is everything else — and it never stops in the middle to ask whether it may continue.
Critical Rules (read before acting)
Loading this skill's files. References live under references/ in this skill's own directory.
Resolve them relative to the skill directory — never the workspace root. A card or a section you
open is read to its end. Sub-agents open the long references only by section, never whole; the
orchestrator reads its current phase reference whole.
- One gate before code. The brief confirmation closes Research — the single pre-execution
approval. From there to the end of Review the pipeline is autonomous: never ask permission to
plan, to dispatch a wave, to run the Reviewer, or to fix a gap the machinery caught. The human
returns at QA. The only mid-pipeline escalations are the bounded-failure valves (3 strikes), a
worker's
spec-ambiguitystop, and a discovery that invalidates the brief — those go to the user; nothing else does. - Tests derive from the brief's ACs and assert brief-defined outcomes — they never mirror the implementation. The gate (the test runner) decides a task is done, not self-assessment. Never weaken, skip, or delete a test to make it pass. A red that turns green on re-run with no code change is a flaky finding to report — never a pass to absorb.
- One atomic, pathspec-limited commit per task. Never batch tasks into one commit.
- The orchestrator never implements a clustered plan. From 4 tasks up, Implement is delegated to workers — one per cluster, all clusters of a wave in parallel; pulling a cluster into the planning window is a violation, not a shortcut. A plan of ≤3 tasks runs inline in the planning window (light path) — and rules 5–6 still hold.
- Verification is structural, never prompted. A fresh wave verifier closes every wave (gate + delivery check + one discrimination mutant); a fresh, independent Reviewer closes the feature (author ≠ reviewer, evidence-or-zero). Neither is optional, neither is offered as a question. Declared limit: the wave verifier anchors on the plan, the Reviewer on the brief — a wrong plan is caught by the brief-anchored close; a wrong brief is caught only by human QA. That is what QA is for.
- Artifacts in English, chat in the user's language. Everything under
.ca-plans/plus slugs, payloads, summaries and verdicts is English — agents are the only readers, and English re-reads are cheaper. The QA script is chat, so it follows the user; product text (UI labels, error messages) is quoted as-is.
What ships with it
7 files 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.
- 9d ago First seen · 182 lines · 113 tokens per session scan A 11d149b85f65
ca-full-cycle is a skill published in the GitHub repository EmanuelVogt/skills (6 stars, last pushed 4d ago), licensed MIT. It adds 113 tokens to every session and 3,344 once invoked, about $0.0006 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 2026-08-31.
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.