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 gongyijie85/dsh-ecc --skill plankton-code-qualitygit clone --depth 1 https://github.com/gongyijie85/dsh-eccWrote 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/gongyijie85/dsh-ecc/plankton-code-quality)<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/plankton-code-quality"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/plankton-code-quality/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/gongyijie85/dsh-ecc/plankton-code-quality"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/plankton-code-quality.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 | $0.00054 | $0.02084 |
| Opus 5 | $0.00027 | $0.01042 |
| Sonnet 5 | $0.00011 | $0.00417 |
| Haiku 4.5 | $0.00005 | $0.00208 |
Grade A, and why
plankton-code-quality 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 6d 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.
This is a copy
97% identical to plankton-code-quality — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plankton Code Quality Skill
Integration reference for Plankton (credit: @alxfazio), a write-time code quality enforcement system for Claude Code. Plankton runs formatters and linters on every file edit via PostToolUse hooks, then spawns Claude subprocesses to fix violations the agent didn't catch.
When to Use
- You want automatic formatting and linting on every file edit (not just at commit time)
- You need defense against agents modifying linter configs to pass instead of fixing code
- You want tiered model routing for fixes (Haiku for simple style, Sonnet for logic, Opus for types)
- You work with multiple languages (Python, TypeScript, Shell, YAML, JSON, TOML, Markdown, Dockerfile)
How It Works
Three-Phase Architecture
Every time Claude Code edits or writes a file, Plankton's multi_linter.sh PostToolUse hook runs:
Phase 1: Auto-Format (Silent)
├─ Runs formatters (ruff format, biome, shfmt, taplo, markdownlint)
├─ Fixes 40-50% of issues silently
└─ No output to main agent
Phase 2: Collect Violations (JSON)
├─ Runs linters and collects unfixable violations
├─ Returns structured JSON: {line, column, code, message, linter}
└─ Still no output to main agent
Phase 3: Delegate + Verify
├─ Spawns claude -p subprocess with violations JSON
├─ Routes to model tier based on violation complexity:
│ ├─ Haiku: formatting, imports, style (E/W/F codes) — 120s timeout
│ ├─ Sonnet: complexity, refactoring (C901, PLR codes) — 300s timeout
│ └─ Opus: type system, deep reasoning (unresolved-attribute) — 600s timeout
├─ Re-runs Phase 1+2 to verify fixes
└─ Exit 0 if clean, Exit 2 if violations remain (reported to main agent)
What the Main Agent Sees
| Scenario | Agent sees | Hook exit |
|---|---|---|
| No violations | Nothing | 0 |
| All fixed by subprocess | Nothing | 0 |
| Violations remain after subprocess | [hook] N violation(s) remain |
2 |
| Advisory (duplicates, old tooling) | [hook:advisory] ... |
0 |
The main agent only sees issues the subprocess couldn't fix. Most quality problems are resolved transparently.
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.
- 6d ago First seen · 238 lines · 54 tokens per session scan A 22c6818a532d
plankton-code-quality is a skill published in the GitHub repository gongyijie85/dsh-ecc (6 stars, last pushed 2d ago), licensed MIT. It adds 54 tokens to every session and 2,084 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to plankton-code-quality, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
manage-taskboard
Manage work in the native DeepSeek Harness Taskboard with exact task ids and optimistic versions. Use when an Agent must inspect project work, claim an eligible todo, record progress or blockers, verify an implementation, submit it for human review, or release its own claim; also use when a human asks how to accept…
delivery-proof
Delivery proof and the doublecheck report. Use when the work is done and the delivery must be proven — consolidate the spec, test timeline, review verdicts, and verification checks into a doublecheckreport, and only then claim completion.
red-green-tdd
Red/green test discipline for implementation work. Use once a doublecheck spec is on record and implementation is about to start — write a test that fails for the missing behavior, run it to see it fail (red), make the change, run again to see it pass (green).
code2skill-review-flow
A read-only reviewer for the main user flows in a package generated by Code2Skill. It checks whether representative paths are basically usable, without proving that every source-code detail is included.
codebase-design
A design vocabulary and workflow for creating code modules with small interfaces that hide substantial behavior. A seam is a place where behavior can be changed or tested without editing the surrounding code.
dev-qa
A development and quality process for building software, testing it, and checking it for security problems. It separates implementation, quality assurance, and security review while coordinating their handoffs.