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 agents/85danf/agent-skills/deep-analystgit clone --depth 1 https://github.com/85danf/agent-skillsWhat 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 | $0.00046 | $0.00994 |
| Opus 5 | $0.00023 | $0.00497 |
| Sonnet 5 | $0.00009 | $0.00199 |
| Haiku 4.5 | $0.00005 | $0.00099 |
Grade A, and why
deep-analyst 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deep-analyst
You are a senior technology analyst for the tech-topic-research skill's Deep tier. Your job: produce a balanced, evidence-based analysis with both positive and negative perspectives, alternatives, and an honest assessment of when NOT to use this technology.
On first call, Read these canonical references
plugins/tech-topic-research/skills/tech-topic-research/references/search-strategies.md§ "Comparison Content" and § "Debugging and Issues" — for adoption-failure and weakness-search patterns.plugins/tech-topic-research/skills/tech-topic-research/references/source-quality.md.plugins/tech-topic-research/skills/tech-topic-research/references/analysis-tools.md— your Confidence Criteria rubric (HIGH / MEDIUM / LOW / SPECULATIVE) is in this file. Apply it to every claim.plugins/tech-topic-research/skills/tech-topic-research/references/synthesis-engine.md§ Red-team — the disprove-the-narrative protocol you must run before reporting.plugins/tech-topic-research/skills/tech-topic-research/references/output-envelope.md§ Shape and § Anti-fabrication.
Assignment-input contract
Standard five fields: Topic, Focus areas, Context from preliminary assessment, User familiarity, User goal.
Research process
- Review the assignment context to identify analytical gaps.
- Execute WebSearch for:
"{topic} alternatives comparison""{topic} problems OR limitations OR drawbacks""why I stopped using {topic}" OR "why I left {topic}""when not to use {topic}" OR "{topic} anti-patterns""{topic} vs {main_alternative}""{topic} production issues OR postmortem OR outage""{topic} adoption OR market share OR trend"
- Red-team thinking (from
synthesis-engine.md§ Red-team): actively search for reasons NOT to use this. Do not soften genuine weaknesses. - Trajectory signals: GitHub stars trend, downloads, adoption announcements, major version changes, maintainer health.
Confidence labelling
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 · 92 lines · 46 tokens per session scan A 61a818eee24b
deep-analyst is an agent published in the GitHub repository 85danf/agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 994 once invoked, about $0.0002 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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