Borrowing it
Nothing to install: this file belongs to radiantlogicinc/fastworkflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/radiantlogicinc/fastworkflow/main/.claude/skills/fastworkflow-research-methodology/SKILL.mdgit clone --depth 1 https://github.com/radiantlogicinc/fastworkflowWrote 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/radiantlogicinc/fastworkflow/fastworkflow-research-methodology)<a href="https://agentmods.dev/skills/radiantlogicinc/fastworkflow/fastworkflow-research-methodology"><img src="https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/fastworkflow-research-methodology/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/radiantlogicinc/fastworkflow/fastworkflow-research-methodology"><img src="https://agentmods.dev/badge/skills/radiantlogicinc/fastworkflow/fastworkflow-research-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00175 | $0.06183 |
| Opus 5.5 | $0.00070 | $0.02473 |
| Sonnet 5.5 | $0.00035 | $0.01237 |
| Haiku 4.5 | $0.00017 | $0.00618 |
Grade A, and why
fastworkflow-research-methodology 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 2mo 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 — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TEAM-PRIVATE: embeds content from uncommitted internal docs. Do not commit or publish this skill without the developer'sexplicit approval.
fastWorkflow Research Methodology
The discipline that turns a hunch into an accepted result in this repo — or into a documented retirement. This is the constitution; the tau2-reliability-campaign skill is the worked application of it.
The house summary, from the plan's own documentation standard: "if it isn't written here with a number and an interval, it didn't happen" (docs/tau2_retail_reliability_implementation_plan.md, Appendix G).
When to use / when NOT to use
| Situation | Skill |
|---|---|
| You have an idea and want to know how to make it an accepted result here | this skill |
| You are running the E0–E25 retail reliability experiments in order | tau2-reliability-campaign |
| You need the actual variance / pass^k / McNemar / CI math with worked examples | fastworkflow-proof-and-analysis-toolkit |
| You want to know which open problems are worth attacking | fastworkflow-research-frontier |
| You need tau-bench mechanics (harness, simulator, pass@1 vs pass^k, parity rules) | fastworkflow-taubench-reference |
| You need what counts as test evidence / how to add pytest tests | fastworkflow-validation-and-qa |
| Your change is code, not research — how it gets classified/gated/reviewed | fastworkflow-change-control |
| You want the history of every dead end and abandoned feature | fastworkflow-failure-archaeology |
| You are writing a design doc or deciding what may be claimed publicly | fastworkflow-docs-and-positioning |
Glossary (define once, use everywhere)
| Term | Meaning |
|---|---|
| pass@1 / pass^k | pass@1 = fraction of tasks passing one run. pass^k = fraction passing all k independent runs. pass^k measures reliability; pass@1 measures capability. |
| τ²-bench retail | Public benchmark: agent plays a retail customer-service rep against a simulated user (another LLM). Scoring is binary and conjunctive on final DB state — one wrong write = 0 for the task. |
| The 15 hard tasks | Hand-picked τ²-retail task IDs that failed under memory pressure (plan Appendix F). An adversarial sample AND our months-long tuning set — which is why anti-p-hacking rules exist. |
| bd (beads) | The issue tracker (bd CLI, source of truth .beads/issues.jsonl). Mandatory for ALL tracking (AGENTS.md). |
| E-card | Experiment card: the plan's unit of proposed work (E0–E25 in docs/tau2_retail_reliability_implementation_plan.md §7). Hypothesis with predicted numbers, design, metrics, gate criteria, dependencies. |
| Pre-registration | Writing hypothesis + config + metrics + analysis plan, and committing it, before the run. |
| Adversarial review | A reviewer (human or agent) explicitly tasked to refute a design against the actual working tree at a pinned commit — the fix-vof pattern. |
| McNemar / Clopper-Pearson / Wilson | Paired before/after significance test; exact/approximate confidence intervals on proportions. Recipes in fastworkflow-proof-and-analysis-toolkit. |
| Ablation | Measuring one change at a time so effects are attributable. |
| RSI | Recursive self-improvement — here always bounded and for reliability (E21), never free-running. |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 242 lines · 175 tokens per session scan A a4f3c26de557
fastworkflow-research-methodology is a skill published in the GitHub repository radiantlogicinc/fastworkflow (52 stars, last pushed 13d ago), licensed Apache-2.0. It adds 175 tokens to every session and 6,183 once invoked, about $0.0007 per session on Opus 5.5. 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.
fastworkflow-research-methodology compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fastworkflow-research-methodologyradiantlogicinc/fastworkflow | Skill | 52 | 13d ago | Apache-2.0 | A | 175 | Claude Code |
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