Borrowing it
Nothing to install: this file belongs to dealfluence/adeu. 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/dealfluence/adeu/main/.opencode/skills/hunt-comments/SKILL.mdgit clone --depth 1 https://github.com/dealfluence/adeuWrote 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/dealfluence/adeu/hunt-comments)<a href="https://agentmods.dev/skills/dealfluence/adeu/hunt-comments"><img src="https://agentmods.dev/badge/skills/dealfluence/adeu/hunt-comments/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/dealfluence/adeu/hunt-comments"><img src="https://agentmods.dev/badge/skills/dealfluence/adeu/hunt-comments.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 | $0.00026 | $0.00316 |
| Opus 5.5 | $0.00010 | $0.00126 |
| Sonnet 5.5 | $0.00005 | $0.00063 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
hunt-comments 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 2mo 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.
What it actually says
Hunt Comments Skill
This skill provides a systematic approach to finding and reducing comment density and character bloat across source code files.
Workflow
-
Run the Comment Density Spotter: Execute
python scripts/count_comment_chars.pyto rank files by comment character count:uv run python scripts/count_comment_chars.py -n 25 -
Target High-Density Files: Focus on top-ranked files with high comment ratios (>20% comment characters).
-
Identify & Remove Non-Essential Commentary:
- Delete narrative historical explanations ("Why we added this in 2024...", "Fix for issue #123 where...").
- Strip redundant docstrings that simply restate function signatures, types, or obvious operations.
- Collapse multi-paragraph block comments into brief 1-line "why" notes or remove them entirely if code is self-explanatory.
- Retain only essential architectural invariants (e.g., OPC part boundaries, Word signed int32 overflow rules, security guards).
-
Verify Behavior: Always run test suites to verify zero functional or formatting regressions:
- Python:
uv run pytest&uv run mypy src - Node:
npm run build && npm test - LangChain:
uv run pytestinlangchain
- Python:
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.
- 2mo ago First seen · 33 lines · 26 tokens per session scan A 2d6f74bfaa4c
hunt-comments is a skill published in the GitHub repository dealfluence/adeu (169 stars, last pushed 3d ago), licensed MIT. It adds 26 tokens to every session and 316 once invoked, about $0.0001 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
live-audit
Audit steam-games-mcp — build/test/lint gate, live MCP tool edge-case sweep (input validation, SteamID64/vanity/appid edge cases, key-gating), and source-level code review. Use when asked to test/audit the published or just-fixed steam-games-mcp package, hunt for bugs/edge cases, or repeat "the same kind of testing as…
review
Assess the blast radius of a change and review a diff — what it touches, what could break, which tests to run, and the prior decisions behind it. Use before a refactor, rename, or signature change, and when reviewing a pull request or your own uncommitted diff.
tool-description-check
Self-check a new or edited MCP tool description/field .describe() text before committing — verify every behavioral claim against live testing or source, check for contradictions with sibling tools, and score against Glama's Tool Definition Quality Score (TDQS) rubric. Use whenever a tool description or schema field…
release-pr-review
Review pass on an open release PR (release/ → main) — the step between git-wrapup and release-and-publish when a project releases in gated release PR mode. Reads the PR's commit range through the code-simplifier lens plus a correctness review, verifies whatever an automated reviewer left on the PR, lands fixes as…
code-simplifier
Cleanup pass that edits the working tree — over a session's uncommitted changes, or a named path or whole codebase. Reads git diff (or the named target) and simplifies, consolidates, and aligns code with the existing codebase — modernize syntax, cut unnecessary complexity and slop, consolidate duplicated logic, catch…
tool-defs-analysis
Read-only audit of MCP definition language across an existing surface — tools, resources, prompts, server instructions. Walks every definition file and checks 16 categories the LLM reads to decide whether and how to call: voice & tense, internal leaks, audience leaks, defaults, recovery hints, field descriptions…