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/tony/skills/researchnpx skills add tony/skills --skill researchgit clone --depth 1 https://github.com/tony/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/tony/skills/research)<a href="https://agentmods.dev/skills/tony/skills/research"><img src="https://agentmods.dev/badge/skills/tony/skills/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 5.1 | $0.00024 | $0.01164 |
| Opus 5 | $0.00012 | $0.00582 |
| Sonnet 5 | $0.00005 | $0.00233 |
| Haiku 4.5 | $0.00002 | $0.00116 |
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 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Business Research
Collect the data behind a business-value claim for an AI skill or agentic workflow — productivity, time saved, quality, capacity, delivery outcomes — into the interim run package the report commands render from. Value is stated in engineer-hours, cycle time, throughput, quality, and capacity. Never money.
Read these first; they bind every step:
../../references/interim-format.md— the run package layout and where runs live.../../references/provenance.md— the four tags, the anti-inflation rules, and the no-currency contract.../../references/measurement.md— what the data must feed: the saving formulas, statistics discipline, the counterfactual ladder, and the quality guardrails.../../references/instruments.md— per-instrument probes, collection discipline (timezone and cohort conventions, the GraphQL filteredCount trap, absence snapshots), and the collection targets in query-ready form.
User arguments: $ARGUMENTS
Context
Today:
!date +%F
Kernel (WSL detection):
!grep -qi microsoft /proc/version 2>/dev/null && echo "WSL" || echo "(not WSL)"
Instruments on PATH:
!for c in git gh jq; do command -v $c >/dev/null && echo "$c: available" || echo "$c: unavailable"; done
gh:
!gh auth status 2>&1 | head -3
Procedure
1. Scope and orchestration plan
Present an orchestration plan before touching disk: what will be
measured, over what pinned date range, with which instruments, to
which output directory. Enter plan mode if the host supports it —
Claude Code EnterPlanMode; Cursor, Codex, or Gemini via /plan or
Shift+Tab; otherwise present the plan as plain text and pause. Ask
where to write the run, defaulting per the location rules in
interim-format.md (Documents root; on WSL prefer the Windows
Documents folder when detected). Wait for confirmation.
2. Instrument discovery — never assumption
Probe what is actually available using the per-instrument probes in
instruments.md; never assume an instrument exists. Candidates
(illustrative, not a fixed list): git history; gh (verify auth
and rate limits before relying on it); ticket-tracker MCPs or CLIs
such as Jira or Linear; CI telemetry; session logs; time-tracking
exports. Record every instrument as available or
unavailable in the run README. Data an unavailable instrument would
have provided is recorded as unknown — never fabricated, never
silently skipped.
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 · 126 lines · 24 tokens per session scan A ba05b9738fb0
research is a skill published in the GitHub repository tony/skills (2 stars, last pushed 7d ago), licensed MIT. It adds 24 tokens to every session and 1,164 once invoked, about $0.0001 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 2026-09-03.
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