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Analyzing competing products

Skill casioreview20-glitch/forge-os/skills/core/research/analyzing-competing-products

The open-source control plane for AI agents — skill routing, context governance, trustworthy execution, evidence, security, and multi-agent orchestration.

Install
npx -y skills add casioreview20-glitch/forge-os --skill analyzing-competing-products

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  • 10 days oldThe repository was created 10 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
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What its author says it does

Copied from the file, not written here

Use when analyzing competing products is required during research work, especially when the result must be traceable, independently reviewable, and safe to hand to another agent.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

7.5 KB, as published. Nobody here has run it

Analyzing Competing Products

Overview

This skill owns one bounded responsibility: analyzing competing products. Its focus is analyzing competing products within research lifecycle boundaries. It converts declared inputs into typed artifacts and reproducible evidence without silently changing product scope.

Trigger

Activate only when the project is in one of these stages: discovery, research, all contract preconditions pass, and the router identifies a missing output this skill can produce. Do not activate merely because the skill name resembles the user request.

Required Inputs

  • confirmed-intent
  • Optional: research-questions
  • Current gate result, open findings, artifact hashes, and invalidation state
  • Required tools: web-search
  • Optional tools: none
  • Confirmed human decisions relevant to this scope

Method-Specific Protocol

  1. Define the exact decision, actors, objects, states, invariants, side effects, and non-goals owned by analyzing competing products.
  2. Build a decision table for normal, boundary, invalid, permission, failure, retry, recovery, concurrency, migration, and abuse conditions relevant to analyzing competing products.
  3. Apply analyzing competing products only to direct input artifacts; record assumptions, rejected alternatives, and any human decision still required.
  4. Trace the resulting contract to user value, security, reliability, cost, operability, and downstream consumers.
  5. Create reproducible checks that would fail if analyzing competing products were incomplete or implemented incorrectly.

Procedure

  1. Translate the confirmed brief into answerable research questions.
  2. Define source quality and freshness criteria before searching.
  3. Define the exact decision, actors, objects, states, invariants, side effects, and non-goals owned by analyzing competing products.
  4. Build a decision table for normal, boundary, invalid, permission, failure, retry, recovery, concurrency, migration, and abuse conditions relevant to analyzing competing products.
  5. Apply analyzing competing products only to direct input artifacts; record assumptions, rejected alternatives, and any human decision still required.
  6. Trace the resulting contract to user value, security, reliability, cost, operability, and downstream consumers.
  7. Create reproducible checks that would fail if analyzing competing products were incomplete or implemented incorrectly.
  8. Collect evidence from independent primary or authoritative sources.
  9. Separate observations, interpretations, uncertainties, and contradictions.
  10. Map findings to product decisions and open questions.
  11. Publish a source-linked artifact and research gaps.

Verification Questions

  • Does the artifact make the owned decision for analyzing competing products explicit and bounded?
  • Are failure, recovery, permissions, concurrency, and irreversible side effects addressed where applicable?
  • Can every load-bearing claim be traced to a confirmed fact, direct artifact, executable check, or declared assumption?
  • Would a downstream agent know exactly what changed, what remains open, and what must be invalidated?

Evidence Packet

Produce or reference all applicable evidence:

  • analyzing-competing-products-decision-table
  • analyzing-competing-products-verification-report
  • analyzing-competing-products-handoff-envelope

Evidence must identify the current artifact hash, command or method used, result, reviewer identity, timestamp, and limitations.

Output Contract

Produce:

  • research-evidence
  • problem-discovery

The primary artifact must include schema version, provenance, consumed artifact IDs, decisions, evidence references, residual risks, validation state, and invalidation targets. Narrative explanation may accompany the artifact but cannot replace it.

Quality Gate

Reviewer: independent-reviewer

  • The output directly and completely performs analyzing competing products within its declared boundary.
  • Does the artifact make the owned decision for analyzing competing products explicit and bounded?
  • Are failure, recovery, permissions, concurrency, and irreversible side effects addressed where applicable?
  • Can every load-bearing claim be traced to a confirmed fact, direct artifact, executable check, or declared assumption?
  • Would a downstream agent know exactly what changed, what remains open, and what must be invalidated?
  • Every material claim is traceable to an input, decision, executable check, or evidence item.
  • Required fields are complete and machine-readable.
  • The producing agent is not the approving reviewer.
  • Open uncertainty and residual risk are explicit; critical findings are never hidden by an aggregate score.

Pass only when: All mandatory rules pass, evidence targets the current artifact hash, and no unresolved critical finding applies.

Forbidden Shortcuts

  • Do not infer a material requirement that the user has not confirmed.
  • Do not replace a typed artifact with a long explanation.
  • Do not approve work produced by the same agent identity.
  • Do not hide a critical failure behind a high aggregate score.
  • Do not load unrelated project history, files, references, or skill bodies.
  • Do not mark evidence complete when it targets a different artifact hash or version.

Failure Modes

  • guessing a material requirement
  • producing prose without the contracted artifact
  • self-approving the output
  • expanding scope without a decision record
  • treating analyzing competing products as a naming exercise
  • covering only the happy path
  • creating extension points without a current contract or consumer

Escalation and Invalidation

Stop and request a human decision when scope, risk acceptance, irreversible action, cost ceiling, privacy boundary, or product direction is materially ambiguous. When this artifact changes, invalidate only descendants named by the artifact graph; preserve unaffected verified branches.

Handoff

  • Next transition: the graph router selects a real consumer of research-evidence, problem-discovery.
  • Required evidence: contract-validation, independent-review, analyzing-competing-products-decision-table, analyzing-competing-products-verification-report, analyzing-competing-products-handoff-envelope.
  • Required envelope fields: artifactId, schemaVersion, sha256, producingSkill, producingAgent, consumedArtifacts, decisionIds, evidenceIds, residualRisks, validationState, invalidationTargets, stopCondition.
  • Stop condition: Output contract is satisfied, a blocker is recorded, or a material human decision is required.

Token and Context Policy

Load at most 8 direct artifacts and reference depth 1. Use stable IDs, hashes, signatures, and deltas instead of repeating full history. Use established domain terminology, state each requirement once, and spend context on decisions, code, tests, or evidence rather than narration.

Reference Playbook

Load skills/references/core/research.md only when this skill needs pack-wide decision tables, evidence patterns, or cross-skill handoff rules.

See contract.json for the machine-readable contract.

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