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 mattgierhart/PRD-driven-context-engineering --skill prd-v01-user-value-articulationgit clone --depth 1 https://github.com/mattgierhart/PRD-driven-context-engineeringWrote 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/mattgierhart/prd-driven-context-engineering/prd-v01-user-value-articulation)<a href="https://agentmods.dev/skills/mattgierhart/prd-driven-context-engineering/prd-v01-user-value-articulation"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v01-user-value-articulation/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/mattgierhart/prd-driven-context-engineering/prd-v01-user-value-articulation"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v01-user-value-articulation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.01557 |
| Opus 5 | $0.00051 | $0.00779 |
| Sonnet 5 | $0.00021 | $0.00311 |
| Haiku 4.5 | $0.00010 | $0.00156 |
Grade A, and why
prd-v01-user-value-articulation 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 10d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Value Articulation Skill
Transform validated pain points into evidence-anchored value statements.
Workflow Position
Problem Framing → User Value Articulation → v0.2 Market Definition
(pain) (value) (who cares most)
Consumes
This skill requires prior work from v0.1:
- **CFD-* entries (pain points, from Problem Framing) — Evidence for what problems users face
- PRD.md Why section (problem statement table) — Context for pain-to-value transformation
This skill assumes v0.1 Problem Framing is complete.
Produces
This skill creates/updates:
- CFD-* entries (tagged as value hypotheses) — Transformation of pain points into value statements, with confidence scoring
- MVP scope signal — Identifies which value dimensions will drive MVP feature scope (handed to v0.3)
All CFD value hypothesis entries should include:
confidence: 2-3/5(based on evidence tier from users or market)- Evidence tier (1-5 per value hierarchy)
- Forward target: "Would move to 4/5 if we validate with beta cohort"
Example value hypothesis entry:
CFD-015: Value Hypothesis — Eliminate manual reconciliation workflow
Source Pain: CFD-001 (sales teams waste 5+ hours/week)
Evidence Tier: 2-3 (workaround + quantified cost)
Confidence: 3/5 (source: 3-customer-interviews-jan-2026)
Value Statement: "Reclaim 5 hours/week for strategic pipeline management"
Transformation: [5 hours wasted] → [5 hours available for growth]
Framing Type: Negative Removal (acute quantified loss)
Quantification: 5 hours/week = ~250 hours/year = $12,500 (at $50/hr)
Next Target: "Would move to 4/5 if we observe beta cohort using this feature"
Workflow Overview
- Receive pain points → Read CFD-IDs from Problem Framing
- Identify value unit → Time / Money / Risk / Capability
- Transform pain → value → Apply transformation pattern
- Anchor to evidence → What proof users want this outcome?
- Create CFD entry → Tag as value hypothesis with tier
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.
- 10d ago First seen · 172 lines · 103 tokens per session scan A f85f6c94dd37
prd-v01-user-value-articulation is a skill published in the GitHub repository mattgierhart/PRD-driven-context-engineering (182 stars, last pushed 9d ago), licensed MIT. It adds 103 tokens to every session and 1,557 once invoked, about $0.0005 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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