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/melodic-software/claude-code-plugins/writenpx skills add melodic-software/claude-code-plugins --skill writegit clone --depth 1 https://github.com/melodic-software/claude-code-pluginsWhat 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 | $0.00170 | $0.02701 |
| Opus 5 | $0.00085 | $0.01350 |
| Sonnet 5 | $0.00034 | $0.00540 |
| Haiku 4.5 | $0.00017 | $0.00270 |
Grade A, and why
write 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-computed context
Current branch: !git branch --show-current 2>/dev/null || echo "unknown"
Working tree (empty = clean): !{ git status --porcelain 2>/dev/null || echo "(git status unavailable)"; } | head -5
Variables
Arguments: $ARGUMENTS
Purpose
/bugs produces a five-field structured report so the next session (or a human) can act without re-asking on vague repro, missing severity, no fix location, or hand-wavy expected/actual. Read-only. It captures, it does not fix, and it does not file (unless you explicitly ask).
This is the bug-intake stage. It sits upstream of filing the report into a work-item tracker, and it is independent of any downstream fix workflow, when the report itself is the deliverable (a Slack message, a PR comment, a verbal handoff), that is all this skill needs to do.
Five fields: title, steps to reproduce, expected vs actual, severity (with justification), and suggested fix location. A (unknown — needs reporter confirmation) placeholder is used for any field that cannot be backed from the source, rather than inventing one.
A sharp report captured up front saves the next session from re-asking. Unrepresented reproduction steps cost far more to recover later than to capture now.
Trigger conditions, when to invoke
Invoke when ANY hold:
- The user describes a defect ("there is a bug in
X", "Xis broken whenY") - The user asks for help filing/writing-up a bug ("how do I report this", "write this up")
- The user states a behavioural mismatch ("expected
X, gotY", "Xreturns wrong value whenY") - The user asks for a structured report from informal context
Skip conditions, when to NOT invoke
- Investigation needed, the bug needs reproduce-first diagnosis, not just capture. If your project provides a debugging or investigation skill, hand off to it; otherwise scope the investigation separately from this read-only capture.
- Fix already in progress. This skill only captures; it does not complete a fix.
- Feature request (a missing capability, not a defect). This is product intent, not a bug. If your project provides a PRD or requirements-intake skill, route there; otherwise capture it as a feature request, not a five-field bug report.
- Generic chore (a TODO, docs gap, or non-defect task). File it directly in your tracker; it does not need the five-field bug shape.
What ships with it
3 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 · 144 lines · 170 tokens per session scan A a0d06f0bf003
write is a skill published in the GitHub repository melodic-software/claude-code-plugins (12 stars, last pushed 2d ago), licensed MIT. It adds 170 tokens to every session and 2,701 once invoked, about $0.0009 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 2026-08-30.
Other skills, from other repositories
deep-research
Conducts iterative deep research on any topic using web search, progressive exploration, and structured synthesis. Use when asked for comprehensive research, deep investigation, thorough analysis, or multi-source exploration of any topic. Triggers: research, investigate, deep dive, comprehensive analysis, explore…
error-ux
Principles and patterns for writing error messages that help users recover. Use when auditing, writing, or improving error messages in code. Triggers: error messages, user experience, error handling, exception messages, validation errors.
adversarial-patterns
Library of realistic adversarial attack vectors and anti-patterns to avoid. Contains examples of valid attacks and subtle gaming patterns to reject.
documentation-testing
Provides heuristics for identifying incomplete or broken documentation. Use when validating README setup instructions, testing onboarding flows, or auditing documentation quality. Triggers: docs, readme, onboarding, setup validation, documentation audit.
adversarial-analysis
Analyze code to identify explicit contracts, implicit usage patterns, and realistic boundary conditions. Contains concrete formulas for calculating input realism limits. Use before generating adversarial tests.
propagate-then-search
For constraint problems: eliminate impossibilities before guessing, reduce search space through inference, fail fast on contradictions.