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/85danf/agent-skills/tech-topic-researchnpx skills add 85danf/agent-skills --skill tech-topic-researchgit clone --depth 1 https://github.com/85danf/agent-skillsWrote 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/85danf/agent-skills/tech-topic-research)<a href="https://agentmods.dev/skills/85danf/agent-skills/tech-topic-research"><img src="https://agentmods.dev/badge/skills/85danf/agent-skills/tech-topic-research.svg" alt="Measured on agentmods" 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 | $0.00085 | $0.06081 |
| Opus 5 | $0.00043 | $0.03040 |
| Sonnet 5 | $0.00017 | $0.01216 |
| Haiku 4.5 | $0.00009 | $0.00608 |
Grade A, and why
tech-topic-research 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 5d 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 — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Teach or evaluate a topic through progressive disclosure. Deliver a useful gist within seconds, then deepen the answer with parallel web research and a written study guide when the user wants more.
When to use this skill
Use this skill when the user explicitly invokes it with a topic, or when the user asks to:
- Learn about, understand, or evaluate a technology, tool, framework, library, service, concept, or methodology.
- Compare two or more such items ("X vs Y", "X or Y for {use case}", "compared to Z").
- Decide whether to adopt a tool, prepare for an interview on a topic, build a mental model, or get a curated reading list.
This skill is invoked only by explicit user request — never autonomously. The frontmatter flag disable-model-invocation: true blocks auto-matching; hosts that don't honor it MUST still refuse without an explicit user request. Explicit invocation is consent to consider running the skill; the Token cost and approval gate (Phase 1 step 5) still applies.
When NOT to use this skill
Skip this skill — it is the wrong tool — when:
- The user wants a single-fact lookup ("what version of X ships JSONB?", "who is the CEO of Y?"). One WebSearch call answers it.
- The user is debugging a specific error and wants a fix. Use a debugging workflow, not a research one.
- The user wants a quick sanity check or a yes/no answer.
- The user already has a clear opinion and just wants confirmation.
- The user explicitly asks for "a quick answer" or "in one sentence".
If you are not sure whether the request is research or a lookup, default to the lookup — research is opt-in, see the next section.
Token cost and approval gate
⚠️ This skill is heavy on token usage. A Standard run spawns four parallel haiku subagents, each issuing 4–6 web searches and several WebFetch calls. A Deep run adds an Opus subagent and a Sonnet reviewer. Total cost is typically 5–30× a normal turn, and the subagent fan-out cannot be cancelled mid-flight.
What ships with it
21 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.
- agents/community-searcher.md 2.6 KB
- agents/comparison-searcher.md 3.2 KB
- agents/deep-analyst.md 4.1 KB
- agents/docs-searcher.md 4.8 KB
- agents/integration-searcher.md 2.5 KB
- agents/synthesis-reviewer.md 5.4 KB
- agents/tutorial-searcher.md 2.3 KB
- assets/comparison-guide-template.md 1.6 KB
- assets/research-plan-template.md 1.8 KB
- assets/source-summary-template.md 1.2 KB
- assets/study-guide-template.md 1.9 KB
- references/analysis-tools.md 5.9 KB
- references/capability-risk.md 1.4 KB
- references/comparison-mode.md 5.8 KB
- references/freshness-gate.md 1.1 KB
- references/output-envelope.md 4.2 KB
- references/search-strategies.md 6.1 KB
- references/source-quality.md 2.9 KB
- references/synthesis-engine.md 3.5 KB
- references/teaching-tone.md 4.9 KB
- references/troubleshooting.md 6.6 KB
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.
- 5d ago First seen · 440 lines · 85 tokens per session scan A 240d430bf94e
tech-topic-research is a skill published in the GitHub repository 85danf/agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 85 tokens to every session and 6,081 once invoked, about $0.0004 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 2026-08-31.
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