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 Avyayalaya/pm-skills-arsenal --skill discovery-researchgit clone --depth 1 https://github.com/Avyayalaya/pm-skills-arsenalWrote 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/avyayalaya/pm-skills-arsenal/discovery-research)<a href="https://agentmods.dev/skills/avyayalaya/pm-skills-arsenal/discovery-research"><img src="https://agentmods.dev/badge/skills/avyayalaya/pm-skills-arsenal/discovery-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/avyayalaya/pm-skills-arsenal/discovery-research"><img src="https://agentmods.dev/badge/skills/avyayalaya/pm-skills-arsenal/discovery-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 | $0.00033 | $0.21181 |
| Opus 5 | $0.00016 | $0.10590 |
| Sonnet 5 | $0.00007 | $0.04236 |
| Haiku 4.5 | $0.00003 | $0.02118 |
Grade A, and why
discovery-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 12d 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.
This is a copy
94% identical to discovery-research — 88 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Produce a Research Synthesis Brief -- a structured, evidence-graded synthesis of user research from multiple sources that separates validated findings from hypotheses, grades every claim by evidence quality, and connects insights to product decisions. The core question this skill answers: "What does the evidence actually say?" This is not a research plan or a methodology guide -- it is a reasoning engine that takes raw research inputs and produces findings a PM can act on, with explicit confidence levels and evidence gaps that drive the next research cycle.
When to Use / When NOT to Use
Use this skill when:
- Synthesizing findings from multiple user interviews into a coherent picture
- Evaluating whether research evidence is strong enough to support a product decision
- Running a discovery sprint and need to structure findings as they accumulate
- Analyzing qualitative data (interview transcripts, support tickets, forum posts) for patterns
- Building research-backed feature hypotheses before committing engineering resources
- Resolving conflicting signals across different research sources (surveys say X, interviews say Y, behavioral data says Z)
- Assessing what you still do NOT know and where evidence is dangerously thin
Do NOT use this skill when:
- You need competitive market analysis (-> Competitive & Market Analysis skill -- that is market-side structural analysis, this is demand-side primary research)
- You need to design metrics or experiments (-> Metric Design & Experimentation skill -- that is measurement, this is evidence gathering and synthesis)
- You need to write a product specification (-> Spec Writing skill -- use this skill's output as INPUT to spec writing)
- You need a research plan template without existing data to synthesize (this skill processes research, it does not design research methodology from scratch)
- You need statistical analysis of quantitative experiment results (-> Metric Design & Experimentation skill)
What ships with it
2 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.
- 12d ago First seen · 1,203 lines · 33 tokens per session scan A 742207567ded
discovery-research is a skill published in the GitHub repository Avyayalaya/pm-skills-arsenal (6 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 21,181 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to discovery-research, differing in 88 lines, and is treated as a copy.
Other skills, from other repositories
jtbd-extractor
Turn raw research into Jobs-to-be-Done statements showing what users are really trying to accomplish. Use when: extract jobs, jtbd analysis, jobs to be done, what job is the user hiring, underlying user needs.
discovery-research
Use when synthesizing user research, analyzing interview data, conducting discovery sprints, evaluating evidence quality across sources, or building research-backed feature hypotheses.
assumption-mapper
Surfaces the assumptions a product or strategy depends on, classifies them by criticality and evidence quality, and produces a prioritized test plan. Use when: assumption mapping, identify assumptions, what could go wrong, leap-of-faith assumptions, riskiest assumption test, RAT, assumption inventory, validate before…
competitive-analyzer
Structured competitive teardown for product discovery - surface the 4-6 dimensions buyers actually weigh, score every competitor on each, and identify exploitable gaps. Use when: competitive analysis, competitor teardown, market positioning, where do we win, where do we lose, competitive gap analysis, competitor audit.
feedback-prioritizer
Triages a backlog of raw customer feedback into a ranked list of opportunities scored on reach, severity, strategic fit, and confidence. Outputs a prioritized list with explicit "do not act" callouts for vocal-minority signals. Use when: triage feedback, prioritize feature requests, customer feedback backlog, what…
north-star-metric-finder
Identifies a candidate North Star Metric (NSM) for a product - the single metric that captures the value the product delivers to its customers and predicts long-term business growth. Tests candidates against five criteria and surfaces input metrics that move it. Use when: north star metric, NSM, single metric that…