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 skills/d-oit/rust-2026-template/codacynpx skills add d-oit/rust-2026-template --skill codacygit clone --depth 1 https://github.com/d-oit/rust-2026-templateWhat 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 | $0.00089 | $0.00716 |
| Opus 5 | $0.00044 | $0.00358 |
| Sonnet 5 | $0.00018 | $0.00143 |
| Haiku 4.5 | $0.00009 | $0.00072 |
Grade A, and why
codacy 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codacy Static Analysis
When to Use
- User asks for this skill's functionality
Orchestrate static analysis using Codacy Analysis CLI (local) and Codacy Cloud CLI (remote).
Installation & Auth
# Analysis CLI (for local runs)
npm i -g @codacy/analysis-cli
# Cloud CLI (for PR data and suppressions)
npm i -g @codacy/codacy-cloud-cli
export CODACY_API_TOKEN=<your-api-token>
PR Triage Workflow
-
Get PR analysis:
codacy pull-request gh <org> <repo> <prNumber> --output json > /tmp/codacy-pr.json -
Categorize issues:
- False positives → Suppress via Cloud CLI.
- Real issues → Fix in code.
-
Suppress false positives:
codacy pull-request gh <org> <repo> <prNumber> --ignore-issue <numeric-resultDataId> --ignore-reason FalsePositiveNote: Use numericresultDataId, NOT hash IDs. -
Fix issues: Batch fix patterns and verify with local lint/tests.
Local Analysis
# Initialize configuration (generates .codacy.yml)
codacy-analysis init --default
# Run local analysis
codacy-analysis analyze --pr --output-format json
Known Limitations
| Tool Category | Status | Note |
|---|---|---|
| JS/TS/Shell | ✅ Works | ESLint9, Stylelint, ShellCheck |
| Rust | ⚠️ Limited | Local analysis uses jscpd and Lizard; Cloud uses Opengrep |
| Python/Ruby | ❌ Fails | Missing runtimes/venv issues |
| Java/PMD | ❌ Fails | Missing Java runtime |
Always cross-reference with Cloud CLI for full PR data.
Rationalizations
| Rationalization | Reality |
|---|---|
| "Local analysis shows 0 issues, so we are good." | Analysis CLI has limited local tool support; Cloud CLI is the source of truth. |
| "I'll use the issue hash for suppression." | Codacy CLI requires the numeric resultDataId for suppressions. |
Red Flags
- Relying solely on local
codacy-analysisfor Rust/Python/Java projects. - Attempting to suppress issues without a valid
--ignore-reason. - Ignoring the
resultDataIdfield in JSON output in favor of hashes.
What ships with it
4 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.
- 2d ago First seen · 85 lines · 89 tokens per session scan A af2abfb6881d
codacy is a skill published in the GitHub repository d-oit/rust-2026-template (10 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 716 once invoked, about $0.0004 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
review-backend
Performs a strict code review of Rust backend code (files, diffs, or snippets). Trigger whenever the user shares backend code (Rust, SQL) and asks for a review, feedback, or says "review this" — including partial snippets.
yada-yada-yada
Enter Seinfeld Mode. Explain programming, debugging, architecture, and code reviews through observational comedy inspired by Seinfeld. Trigger when the user says "what's the deal", festivus, serenity, yada, soup, shrinkage, sponge-worthy, master of my domain, double dip, newman, vandelay, nostrand, constanza or…
fix-issue
Fixes a bug, implements a missing feature, or implements one slice of a larger feature, starting from a GitHub issue link, number, or direct text description. Trigger when the user gives a GitHub issue URL/reference/description and asks to fix, implement, resolve, or work on it.
review-frontend
Performs a strict code review of the Svelte 5 frontend code (files, diffs, or snippets). Trigger whenever the user shares frontend code (Svelte, TypeScript, CSS) and asks for a review, feedback, or says "review this" — including partial snippets.
make-it-so
Enter Star Trek command mode. Use Star Trek terminology naturally while remaining a highly competent engineering officer. Trigger when the user mentions Star Trek, Picard, make it so, engage, stardate, number one, federation, borg, warp, enterprise.
triage-review-finding
Verifies and triages a code review finding (from an AI review, audit doc, or human reviewer) against the actual codebase. Trigger when the user pastes a review comment/finding and asks to check, analyze, or evaluate whether it makes sense.