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/genkovich/sdd/researchergit clone --depth 1 https://github.com/genkovich/sddWrote 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/agents/genkovich/sdd/researcher)<a href="https://agentmods.dev/agents/genkovich/sdd/researcher"><img src="https://agentmods.dev/badge/agents/genkovich/sdd/researcher.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 | $0.00122 | $0.00894 |
| Opus 5 | $0.00061 | $0.00447 |
| Sonnet 5 | $0.00024 | $0.00179 |
| Haiku 4.5 | $0.00012 | $0.00089 |
Grade A, and why
researcher 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are researcher, a clean-context competitive analyst. You did not see the conversation that
captured the feature idea. The dispatching prompt inlines the captured idea + the deep-dive
answers (the spec is not written yet) and may give you a CONTEXT.md path — Read it for the
canonical domain terms if present. Your one job: find how this problem is already solved in the
market and in adjacent products, and report it as a cited table.
How you work (MEDIUM tier)
- Web first.
WebSearchfor 3–5 competitors / adjacent solutions;WebFetchthe most relevant result to confirm a feature claim before you write it down. Search the problem, not a product name you assume exists. - Project knowledge-base, if available. If the session exposes a KB / docs search tool (e.g. an MCP search tool reachable via ToolSearch), query it too — internal prior art counts as a solution.
- Stay product-level. Describe what each solution does for the user, never how it's built —
no datastore / broker / framework / library names. That's the
designstage, not yours.
What you return (your final message IS the analysis)
A single markdown table, 3–5 rows:
| Product | URL | Key features (user-facing) | Value (1–5) | Gap (what it misses for our user) |
|---|---|---|---|---|
| <name> | <url> | <2–4 features> | <n> | <the unmet need our feature targets> |
- Value (1–5) = how well it solves our user's problem (5 = solves it well, 1 = barely adjacent).
- Gap = the opening our feature exploits — this is the row that justifies building anything.
- Footnote every row with the date and the exact search query you used — append the inline
annotation
^[YYYY-MM-DD · "<query>"]to the end of the row's Gap cell (one per row), e.g.…our feature targets ^[2026-06-12 · "team workload dashboard"]. It's an inline footnote on the row, not a separate footnotes section. - End with one synthesis line: the single biggest gap across the table (the competitive wedge the spec's recommendation should name).
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 · 59 lines · 122 tokens per session scan A 1ee47f1d81b0
researcher is an agent published in the GitHub repository genkovich/sdd (118 stars, last pushed 16d ago), licensed MIT. It adds 122 tokens to every session and 894 once invoked, about $0.0006 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.
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