How do I install the AI Discovery Strategy skill?

Run npx skillmds@latest add immamdouhaboammar/ai-discovery-strategy in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is AI Discovery Strategy safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with AI Discovery Strategy?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI Discovery Strategy free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published AI Discovery Strategy?

imMamdouhaboammar (@immamdouhaboammar) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Discovery Strategy by immamdouhaboammar

Name: AI Discovery Strategy
Author: imMamdouhaboammar

File contents

Ai Discovery Strategy

Job

Improve retrievability and truthful representation across AI answer surfaces

Own this request only when AI discovery intervention is the clear decision boundary. If ownership is ambiguous or several functions compete, route to marketing-council. If the user explicitly asks for dependent work across functions, use ../../scripts/dynamic_router.py to build a bounded DAG.

Operating contract

Read references/skill-spec.json first for activation, invariants, workflow freedom, evidence rules, handoffs, and completion conditions.
Use references/decision-model.md when framing or challenging the decision.
Check references/failure-modes.md before finalizing a recommendation.
Render the response against references/output-contract.md.
Use packaged shared references or current external research only when they are load-bearing. Never present inference as evidence.

Evidence discipline

Classify material claims as fact, inference, assumption, or unknown. Prefer supplied primary evidence. Verify current platform, policy, product, pricing, or market claims when freshness affects the recommendation. Do not fabricate research, tool calls, metrics, customer language, or causal proof.

Routing

Focused request: stay inside this Skill.
Ambiguous or cross-functional ownership: hand to marketing-council.
Explicit dependency chain: use ../../scripts/dynamic_router.py.
After Skill ownership is known, theory/agent selection may use ../../scripts/neural_router.py; neural nodes never replace Skill routing.

Execution connections

Primary specialist: ../../agents/ai-discovery-strategist.md
Skeptical counterweight: ../../agents/marketing-skeptic.md
Domain challenge gate: ../../hooks/ai-surface-check.md
Evidence gate: ../../hooks/evidence-gate.md
Keep these as decision inputs, not automatic authority. The Skill owns the final evidence-bound synthesis.

Neural connections

Owning Skill: ai-discovery-strategy
Decision boundary: AI discovery intervention
Neural graph: ../../neural/graph.json
Neural router: ../../scripts/neural_router.py
Theory and specialist selection happens only after Skill ownership; neural nodes never replace Skill routing.
Use the local references/skill-spec.json evidence policy and invariants to reject neural recommendations that are unsupported by the request evidence.

Completion gate

Complete only when the decision is explicit, evidence and inference are separated, a credible alternative was considered, outputs are rendered, material uncertainty is stated, and measurement plus reversal evidence are defined.

Local behavioral evaluations live in evals/activation.yml, evals/behavior.yml, evals/pressure.yml, and evals/regression.yml.

---name: ai-discovery-strategydescription: Use when the user needs to improve retrievability and truthful representation across AI answer surfaces; route here only when this decision boundary is the clear owner.---# Ai Discovery Strategy## JobImprove retrievability and truthful representation across AI answer surfacesOwn this request only when **AI discovery intervention** is the clear decision boundary. If ownership is ambiguous or several functions compete, route to `marketing-council`. If the user explicitly asks for dependent work across functions, use `../../scripts/dynamic_router.py` to build a bounded DAG.## Operating contract1. Read `references/skill-spec.json` first for activation, invariants, workflow freedom, evidence rules, handoffs, and completion conditions.2. Use `references/decision-model.md` when framing or challenging the decision.3. Check `references/failure-modes.md` before finalizing a recommendation.4. Render the response against `references/output-contract.md`.5. Use packaged shared references or current external research only when they are load-bearing. Never present inference as evidence.## Evidence disciplineClassify material claims as fact, inference, assumption, or unknown. Prefer supplied primary evidence. Verify current platform, policy, product, pricing, or market claims when freshness affects the recommendation. Do not fabricate research, tool calls, metrics, customer language, or causal proof.## Routing- Focused request: stay inside this Skill.- Ambiguous or cross-functional ownership: hand to `marketing-council`.- Explicit dependency chain: use `../../scripts/dynamic_router.py`.- After Skill ownership is known, theory/agent selection may use `../../scripts/neural_router.py`; neural nodes never replace Skill routing.## Execution connections- Primary specialist: `../../agents/ai-discovery-strategist.md`- Skeptical counterweight: `../../agents/marketing-skeptic.md`- Domain challenge gate: `../../hooks/ai-surface-check.md`- Evidence gate: `../../hooks/evidence-gate.md`- Keep these as decision inputs, not automatic authority. The Skill owns the final evidence-bound synthesis.## Neural connections- Owning Skill: `ai-discovery-strategy`- Decision boundary: `AI discovery intervention`- Neural graph: `../../neural/graph.json`- Neural router: `../../scripts/neural_router.py`- Theory and specialist selection happens only after Skill ownership; neural nodes never replace Skill routing.- Use the local `references/skill-spec.json` evidence policy and invariants to reject neural recommendations that are unsupported by the request evidence.## Completion gateComplete only when the decision is explicit, evidence and inference are separated, a credible alternative was considered, outputs are rendered, material uncertainty is stated, and measurement plus reversal evidence are defined.Local behavioral evaluations live in `evals/activation.yml`, `evals/behavior.yml`, `evals/pressure.yml`, and `evals/regression.yml`.

Frequently asked questions

AI Discovery Strategy