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/codacy/codacy-skills/configure-codacy-cloudnpx skills add codacy/codacy-skills --skill configure-codacy-cloudgit clone --depth 1 https://github.com/codacy/codacy-skillsWhat 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 | $0.00159 | $0.06858 |
| Opus 5 | $0.00079 | $0.03429 |
| Sonnet 5 | $0.00032 | $0.01372 |
| Haiku 4.5 | $0.00016 | $0.00686 |
Grade C, and why
configure-codacy-cloud scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf .codacy/tmp .codacy/remote.config.json .codacy/auto.config.json How it starts
The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configure Codacy (Cloud)
Glossary: See glossary.md for shared definitions of Codacy concepts (issues, findings, severity, coverage, tools, patterns, etc.).
This skill tunes the Codacy configuration of a repository directly on Codacy Cloud, using the cloud as the source of truth. It does not run local analysis. It reads the current cloud issue landscape, applies a higher-signal set of tools and patterns, reanalyzes on Codacy, and iteratively cuts noise over two passes — producing a clean, high-signal configuration with a full audit trail of what changed and why.
For the local-first variant that discovers a stack from scratch and runs codacy-analysis analyze locally, use the configure-codacy skill instead. This skill is for repositories already on Codacy with a finished analysis where you want to tune the cloud config in place.
Prerequisites and requirements
- Codacy Cloud CLI (
codacy) — drives all cloud reads, updates, and reanalysis. Seecodacy-cloud-clifor setup. - Codacy Analysis CLI (
codacy-analysis) — used only for config-file operations (init --remote,init --auto,config --merge). Seecodacy-analysis-clifor setup. - Both CLIs share credentials at
~/.codacy/credentials, so a single login covers both.
This skill has three hard requirements. Verify all three before doing anything else and stop with clear guidance if any fails:
-
The repository is already on Codacy. Confirm with:
codacy repo --output jsonIf this fails (not on Codacy, no auth), stop. Tell the user to add the repo to Codacy first (e.g.
codacy repo --add) — this skill does not set up a new repository. -
The repository has at least one finished analysis. Inspect the
codacy repo --output jsonoutput for a completed/last-analysis indicator, and confirm the issue overview returns data:codacy issues -O -o json 2>/dev/null | jq '.'If the repo was never analyzed, or analysis is still running, stop. Tell the user to wait for the first analysis to finish — the whole flow depends on cloud issue data as the baseline.
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 · 357 lines · 159 tokens per session scan C 46f558ffa4bb
configure-codacy-cloud is a skill published in the GitHub repository codacy/codacy-skills (11 stars, last pushed 1mo ago), licensed MIT. It adds 159 tokens to every session and 6,858 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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