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 Ashkaan/contextium --skill explaingit clone --depth 1 https://github.com/Ashkaan/contextiumWrote 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/ashkaan/contextium/explain)<a href="https://agentmods.dev/skills/ashkaan/contextium/explain"><img src="https://agentmods.dev/badge/skills/ashkaan/contextium/explain/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/ashkaan/contextium/explain"><img src="https://agentmods.dev/badge/skills/ashkaan/contextium/explain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 17 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00046 | $0.02622 |
| Opus 5.5 | $0.00018 | $0.01049 |
| Sonnet 5.5 | $0.00009 | $0.00524 |
| Haiku 4.5 | $0.00005 | $0.00262 |
Grade A, and why
explain scanned grade A 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 26d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **External concepts**: use WebSearch/WebFetch. These are out of the `research-agent`'s scope — it reads this repo, not the internet. When you already know the URL, fetch it with `curl` and read the page yourself: WebFe How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain — Deep Research & Root Cause Analysis
Investigate a topic until confident in root cause or core understanding. Produce an actionable executive summary plus deeper analysis.
Steps: step-0-validate-input → step-1-classify-complexity → step-2-frame → step-3-research → step-4-synthesize → step-5-next-action-gate
Critical
- Validate input first. If
$ARGUMENTSis empty or too vague, ask ONE clarifying question before launching research. Vague input wastes parallel-agent budget on the wrong target. - A class-level flaw becomes work, by default. If the explanation identifies a flaw in a shared mechanism (one affecting several files), the Step 5 question's default option is "Ship class fix now" per
AGENTS.md § Standards → A class fix is atomic. Do NOT silently end at explanation — surface the chain.
step-0-validate-input
If $ARGUMENTS is empty or too vague, ask ONE clarifying question first. Do not
launch research on an ambiguous target.
step-1-classify-complexity
- Quick — well-scoped factual, single concept. Skip to Step 4 using own knowledge + one targeted lookup if needed.
- Standard — requires tracing code/docs/external sources; single likely answer. Steps 2-4 sequentially.
- Deep — cross-cutting, multiple possible causes, systems-level "why". Steps 2-4 with parallel research agents.
State the classification and a one-sentence restatement before proceeding.
step-2-frame
INVESTIGATION FRAME:
- Question: [precise restatement]
- Type: [concept | root-cause | failure-mode | design-rationale | comparison]
- Scope: [in scope vs out of scope]
- Hypotheses: [1-3 ranked by likelihood]
- Key unknowns: [what confirms/rejects each]
If the question references files, a project, or recent work, pull context (read files, git log). If it references a production incident, gather the same context you would to explain it — logs, the recent diff, the alert itself — but do not start fixing: this skill's job is to say WHY, and the fix is a separate decision someone should make with the explanation in hand.
What ships with it
2 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.
- 26d ago Changed 947887e674f0
- 27d ago Changed 1d85df6d4b29
- 28d ago Changed · -35 lines · +14 tokens per session 8f9ea34983c2
- 1mo ago Changed 873f93755a85
- 1mo ago First seen · 205 lines · 32 tokens per session scan A 06c8fb8d1985
explain is a skill published in the GitHub repository Ashkaan/contextium (36 stars, last pushed 13d ago), licensed MIT. It adds 46 tokens to every session and 2,622 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
social-physics-filter
Unified boundary enforcement, interpersonal diagnostic, and relational audit engine. Absorbs 40 psychology + 2 social protocols and all relationship case studies.
therapeutic-ifs
Unified inner work engine: Schema deconstruction (diagnosis) + IFS therapy (treatment). Absorbs: schema-deconstruction.
Diagnostic-First Refactoring
A non-destructive, decoupled analysis protocol for refactoring code. Generates a "Bill of Materials" report before any code is touched.
diagnostic-first-refactoring
Analyze codebase structure before making changes — the "Surgeon's Scan" pattern.
investigate-root-cause
Use when encountering ANY bug, error, test failure, or unexpected behavior. Activate for keywords like "bug", "error", "failing", "broken", "doesn't work", "unexpected", "crash", "exception", "TypeError", "undefined", stack traces, or any error message. Also trigger when tests fail unexpectedly, when behavior differs…
evidence-driven-debugging
Use during active debugging when you have a hypothesis to test or need to instrument a running system. Activate for keywords like "debug", "instrument", "log", "trace", "breakpoint", "what's happening at runtime", "production behavior". Pair to investigate-root-cause for the doing-it phase. Always record what you…