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 vasuag09/harness-claude --skill researchgit clone --depth 1 https://github.com/vasuag09/harness-claudeWrote 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/vasuag09/harness-claude/research)<a href="https://agentmods.dev/skills/vasuag09/harness-claude/research"><img src="https://agentmods.dev/badge/skills/vasuag09/harness-claude/research/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/vasuag09/harness-claude/research"><img src="https://agentmods.dev/badge/skills/vasuag09/harness-claude/research.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00044 | $0.00432 |
| Opus 5 | $0.00022 | $0.00216 |
| Sonnet 5 | $0.00009 | $0.00086 |
| Haiku 4.5 | $0.00004 | $0.00043 |
Grade A, and why
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 9d 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.
What it actually says
/research — reuse before you build
Goal: never hand-roll what a proven solution already does well. Find the 80%-fit and adopt/port it.
Do this (in order)
- In-repo first — does this codebase already solve a near-identical problem? Use
the knowledge graph /
mgrep(orGrep/Globif those are unavailable) to find existing patterns, utilities, and conventions. - Library docs — use context7 (or the library's primary docs / web if context7 is unavailable) to confirm current API/behavior of candidate libraries (don't answer from memory; versions drift).
- Code & registries — search GitHub and the relevant package registry (npm / PyPI) for battle-tested implementations or templates that fit ≥80%. When several candidates fit, compare them on fit %, maintenance/activity, license, and footprint — pick deliberately and note the runner-up; don't just grab the first hit.
- Broader web — only if the above are insufficient (
mgrep --web "...", orWebSearchif mgrep is unavailable).
For LLM/agent work, read the harness's relevant references before choosing an approach.
Output
## Reuse decision
- Adopt / port / wrap: <library or repo> — why it fits
- Build new: <only the genuinely novel part> — why nothing fits
## Key API facts (from context7) that the plan must respect
## Risks of the chosen dependency (maintenance, license, size)
Exit criterion
A documented build-vs-reuse decision with evidence. Fold it into the spec. Then /harness-claude:plan.
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.
- 9d ago First seen · 38 lines · 44 tokens per session scan A 7bbe6b136043
research is a skill published in the GitHub repository vasuag09/harness-claude (2 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 432 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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Analyze AI config evolution in a git repo. Use when mapping AI adoption history, finding when configs were first introduced, charting commit velocity by month, or identifying maturity phases in a project's AI tooling.