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 Agent-Engineer-Master/skill-engineer --skill map-five-forcesgit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWrote 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/agent-engineer-master/skill-engineer/map-five-forces)<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/map-five-forces"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/map-five-forces/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/agent-engineer-master/skill-engineer/map-five-forces"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/map-five-forces.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 | $0.00188 | $0.02616 |
| Opus 5.5 | $0.00075 | $0.01046 |
| Sonnet 5.5 | $0.00038 | $0.00523 |
| Haiku 4.5 | $0.00019 | $0.00262 |
Grade C, and why
map-five-forces scanned grade C with 1 finding 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 1mo 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer --> How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Map Five Forces
For a defined industry, produce a Porter Five Forces analysis extended with complementors (sixth force), AI-as-named-force, and an AI-reshape overlay on the classical five. Output: five-forces.md that names THE governing force that determines this industry's profit distribution.
The discipline: Five Forces is a hypothesis generator, not a conclusion. Every force assessment must be tested against profit pool data, carry a direction-of-travel arrow, and (in 2026) reckon with how AI reshapes it.
Iron rules:
- Every evidence claim carries a V/C/A/I tag — see
../_shared/provenance-tagging.md. - The governing force named in one causal sentence ("Buyer concentration governs this industry because three customers represent 60% of demand and have substituted on price every two years.").
- Complementors (sixth force) assessed as a named section.
- AI assessed as a named force AND inline per classical force (reshape commentary) AND summarized in the reshape matrix.
- Every force carries a direction-of-travel arrow (intensifying / stable / weakening) with velocity where non-stable.
- Focal value-chain layer specified explicitly; the same industry has different forces at different layers.
- At-a-glance heatmap mandatory near top of output.
Process
1. Intake
Confirm: industry slug, geographic scope, focal value-chain layer (OEM / integrator / distributor / fab / fabless / IP / equipment — pick exactly one). Read references/forces-rubric.md. Apply the platform-industry test in references/platform-extensions.md; if positive, add a boundary note to the output.
2. Assess the five classical forces
For each force — Rivalry, Supplier Power, Buyer Power, Threat of New Entry, Threat of Substitutes — produce:
- (a) Intensity rating (Low / Moderate / High) with ≥2 supporting claims, each V/C/A/I-tagged
- (b) Direction of travel (intensifying ↑ / stable ↔ / weakening ↓) with velocity (slow / medium / fast) when non-stable, supported by a tagged evidence claim — see
references/dynamism.md - (c) One sentence on WHY this force is at this intensity (the structural reason, not the symptom)
- (d) How AI is reshaping this force — required inline subsection: intensifying / weakening / no material effect, with 1 tagged evidence claim when non-trivial. The word "AI" must appear within the force's section. This is primary; step 5's matrix summarizes these.
- For platform industries, apply the layer-specific adjustments in
references/platform-extensions.md(e.g., rivalry assessed at both within-ecosystem and cross-ecosystem levels)
What ships with it
10 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.
- assets/heatmap-template.md 2.6 KB
- evals/evals.json 5.1 KB
- references/ai-as-force.md 7.0 KB
- references/complementors.md 2.4 KB
- references/dynamism.md 3.8 KB
- references/edge-cases.md 2.9 KB
- references/forces-rubric.md 10 KB
- references/learnings.md 2.3 KB
- references/platform-extensions.md 4.8 KB
- scripts/validate_forces.py 8.9 KB runs code
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.
- 1mo ago First seen · 106 lines · 188 tokens per session scan C 85b640424ffa
map-five-forces is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (8 stars, last pushed 3mo ago), licensed MIT. It adds 188 tokens to every session and 2,616 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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