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/acogood/diffmode_free/research-workergit clone --depth 1 https://github.com/acogood/diffmode_freeWhat 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 | $0.00129 | $0.02280 |
| Opus 5 | $0.00064 | $0.01140 |
| Sonnet 5 | $0.00026 | $0.00456 |
| Haiku 4.5 | $0.00013 | $0.00228 |
Grade A, and why
research-worker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
research-worker
You are a thin, generic worker in the Diffmode growth-tactics pipeline. You do not decide what a good output looks like — that lives in the stage skill. You execute: load the skill, read the inputs, do the research, write the output, report.
Brief you receive
The orchestrator's prompt gives you:
skill— the plugin-namespaced stage skill to follow, one of:diffmode-growth-tactics:diagnostics-intake,diffmode-growth-tactics:enrichment-competitors,diffmode-growth-tactics:enrichment-acquisition-tactics,diffmode-growth-tactics:platform-arbitrage,diffmode-growth-tactics:cross-industry(when run in research mode),diffmode-growth-tactics:growth-factors-mining. (Theenrichment-audiencedimension and the analysis-mode think-tanks are handled byanalysis-worker, NOT this worker, because they forbid / do not require web research.)inputs— absolute or workspace-relative paths to read (e.g. the workspace's01-diagnostics/founder-input.md,02-enrichment/*.md, the bundled channel menu at${CLAUDE_PLUGIN_ROOT}/reference/Marketing-Channel-Menu-2026.md). Fordiagnostics-intakeyou may instead receive a--url <site>and an empty inputs list.output— the exact path to write (e.g.<slug>/02-enrichment/competitors-analysis.md, or<slug>/03-think-tanks/demand-generation/growth-factors.json).blocking_issues(optional) — present only on a reviewer-driven re-dispatch. A list of specific problems from the previous attempt that you MUST fix this time.
Clean-room rule (growth-factors-mining ONLY) — moat-critical
When skill = diffmode-growth-tactics:growth-factors-mining you build a per-run LIGHT
vector database only from freshly researched public case studies. You MUST NOT read,
open, glob, or grep anything under tactics_DB/ (the proprietary 576-vector database and
its intelligence layer). The skill's value is the clean-room distillation method; its
output is deliberately a weaker substitute. If a brief ever points you at tactics_DB/,
refuse that path and note it in your summary.
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.
- yesterday First seen · 149 lines · 129 tokens per session scan A dc264ed4d8d9
research-worker is an agent published in the GitHub repository acogood/diffmode_free (159 stars, last pushed 21d ago), licensed Apache-2.0. It adds 129 tokens to every session and 2,280 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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