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-v02-competitive-landscape-mappinggit 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-v02-competitive-landscape-mapping)<a href="https://agentmods.dev/skills/mattgierhart/prd-driven-context-engineering/prd-v02-competitive-landscape-mapping"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v02-competitive-landscape-mapping/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-v02-competitive-landscape-mapping"><img src="https://agentmods.dev/badge/skills/mattgierhart/prd-driven-context-engineering/prd-v02-competitive-landscape-mapping.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 | $0.00090 | $0.02298 |
| Opus 5 | $0.00045 | $0.01149 |
| Sonnet 5 | $0.00018 | $0.00460 |
| Haiku 4.5 | $0.00009 | $0.00230 |
Grade A, and why
prd-v02-competitive-landscape-mapping 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Landscape Mapping
Understand market reality before defining your position.
Workflow Position
v0.1 Spark (Problem + Value) → Competitive Landscape Mapping → Product Type Classification
(what hurts) (who else solves it) (how we compete)
Consumes
This skill requires prior work from v0.1:
- **CFD-* entries (problem statements, from Problem Framing) — Evidence for what users need/want
- **CFD-* entries (value hypotheses, from User Value Articulation) — Desired outcomes users are seeking
- PRD.md Why section — Problem framing and market signals
This skill assumes v0.1 Spark is complete (both problem and value).
Produces
This skill creates/updates:
- CFD-* entries (competitive intelligence) — Analysis of direct competitors, adjacent solutions, workarounds
- BR-* entries (positioning rules) — Constraints derived from landscape analysis
- Landscape map artifact — Current behavior documentation, feature matrix, 1% better hypothesis
All CFD competitive intelligence entries should include:
confidence: 2-3/5(based on evidence tier from public sources + user validation)- Evidence source (G2 reviews, pricing pages, user interviews, etc.)
- Forward target: "Would move to 4/5 if we validate gap with 5+ target users"
Example competitive intelligence entry:
CFD-042: Competitive Intelligence — Competitor Landscape Analysis
Type: Competitive Intelligence
Date: 2026-02-01
Confidence: 3/5 (source: public-research + 3-customer-interviews)
Competitors Analyzed: 4 direct + 2 adjacent
Primary Gap: All competitors require enterprise licensing; SMB segment underserved
Feature Matrix: [Link to matrix]
1% Hypothesis: "SMB sales teams can get 80% of [Competitor A] features for 40% of price"
Evidence:
- CFD-001: 3 SMB teams paying $500/mo but using only 5 of 20 features
- CFD-015: Value hypothesis shows $12,500/year need for core 5 features only
Next Target: "Would move to 4/5 if we validate with 5+ SMB prospects willing to pay $200/mo"
What ships with it
4 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 · 282 lines · 90 tokens per session scan A d9b6be942943
prd-v02-competitive-landscape-mapping is a skill published in the GitHub repository mattgierhart/PRD-driven-context-engineering (182 stars, last pushed 9d ago), licensed MIT. It adds 90 tokens to every session and 2,298 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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