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Analyze Constraints Readiness

Organization
yogsoth-ai
analyze-constraints-readiness

Characterize what blocks a research target: readiness dimensions, obstacles, bottlenecks, resource gaps, causal constraints, dependencies, conflicts, removability, and mitigation pathways. Depth selects lightweight triage, readiness assessment, or causal/TOC-style constraint analysis.

Overview

Publisheryogsoth-ai
Repositoryde-anthropocentric-research-engine
Skill nameanalyze-constraints-readiness
Stars
505
Forks
43
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Analyze Constraints Readiness AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analyze-constraints-readiness
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/analyze-constraints-readiness .claude/skills/analyze-constraints-readiness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyze Constraints Readiness in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyze Constraints Readiness on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyze Constraints Readiness is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

analyze-constraints-readiness

Purpose

Assess feasibility and readiness by identifying constraints, resources, dependencies, bottlenecks, and maturation gates.

Input contract

yaml
mode_contracts:
  obstacle-triage: &readiness_input
    required: [candidate_or_plan, readiness_dimensions]
    optional: [resource_estimates, dependencies, assumptions, target_gates]
    constraints: [evidence_must_be_attached_to_each_scored_dimension]
  readiness-assessment: *readiness_input
  resource-envelope: *readiness_input
  causal-constraint-analysis: *readiness_input
  maturation-path: *readiness_input

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

  1. Define candidate, dimensions, hard constraints, and target gates. You MUST load skill classify-constraint to classify each declared constraint.
  2. Select obstacle-triage, readiness-assessment, resource-envelope, causal-constraint-analysis, or maturation-path.
  3. You MUST load skill score-object to score the selected dimensions with evidence. You MUST load skill identify-bottleneck to identify binding constraints and dependencies.
  4. You MUST load skill assess-removability to test whether binding constraints can be removed. You MUST load skill design-mitigation to design removal or mitigation paths and return a readiness conclusion. If several feasible responses must be balanced as a joint set, consider portfolio-optimization. If the target goal remains too broad to assess, consider decompose-research-goal. If external change dominates present readiness, consider analyze-future-scenarios. If several candidate paths are ready for comparative selection, rank-candidates may be the better next tactic.

Mode branches

  • obstacle-triage: rapidly enumerate and severity-rank the obstacles that could block the target, preserving evidence status for each one. You MUST load skill identify-obstacles to enumerate the obstacles. You MUST load skill list-undesirable-effects to retain their observed consequences.
  • readiness-assessment: score the required readiness dimensions with supporting evidence and identify the dimensions that keep the target from being ready. You MUST load skill assess-readiness-dimension to assess every required dimension.
  • resource-envelope: estimate time, cost, and personnel bounds from analogies, then flag low-confidence estimates for investigation. You MUST load skill quantify-resource-gap to quantify the gap. You MUST load skill identify-critical-chain to expose the binding resource sequence.
  • causal-constraint-analysis: trace how constraints interact through dependencies and conflicts to identify the binding cause rather than only its symptoms. You MUST load skill trace-causal-chain to trace constraint propagation. You MUST load skill extract-core-conflict to isolate the core conflict. You MUST load skill challenge-assumption to test the assumptions that sustain it.
  • maturation-path: sequence stage gates and milestones that move the target from current readiness to the declared implementation threshold. You MUST load skill project-future-reality to project the proposed path. You MUST load skill apply-stage-gate to apply its stage gates.

Output contract

yaml
mode_contracts:
  obstacle-triage: &constraint_output
    produces: [constraint_register, bottlenecks, mitigation_paths]
    delta_fields: [findings, decisions, uncertainties, open_questions, recommended_jumps]
  readiness-assessment:
    produces: [readiness_profile, bottlenecks]
    delta_fields: [findings, decisions, uncertainties, open_questions, recommended_jumps]
  resource-envelope:
    produces: [resource_envelope, bottlenecks, mitigation_paths]
    delta_fields: [findings, decisions, uncertainties, open_questions, recommended_jumps]
  causal-constraint-analysis: *constraint_output
  maturation-path:
    produces: [readiness_profile, resource_envelope, stage_gates, mitigation_paths]
    delta_fields: [findings, decisions, uncertainties, open_questions, recommended_jumps]

Thresholds and quality gates

  • Feasibility, maturity, constraint, resource, and maturation gates use declared dimension/evidence/constraint coverage ratios; record numerator, denominator, batch increment, stopping reason, and source references.

  • Preserve structural requirements: at least one hard constraint, one removal path per removable constraint, explicit stage gates, and a binding-constraint rule relative to the observed score distribution.

  • Feasibility dimensions >=5; blockers >=3 per candidate where source protocol applies.

  • Maturity diagnosis: >=5 dimensions, >=2 evidence items per dimension, >=1 bottleneck.

  • Constraint identification: >=3 constraints per candidate; >=1 hard constraint; >=1 removal path per removable constraint.

  • Resource envelope: >=3 dimensions (time, cost, personnel) and >=2 analogies per estimate.

  • Maturation path: >=3 stage gates and >=2 milestones per stage.

  • Binding constraint threshold: sensitivity score >2* median.

Failure and counterexamples

Do not label a candidate ready with missing evidence, unclassified hard constraints, or an unbounded resource estimate. A conflict with no manageable injection remains blocked.

Provenance map

26 architecture old entries; readiness, feasibility, resource, obstacle, dependency, sensitivity, and maturation families are merged by mode. Missing aliases are listed in log.

Legacy context checkpoint / Delta notes

Append dimension scores/evidence, constraint IDs, bottleneck rationale, resources, gates, and unresolved conflicts.

Preserved source criteria ledger

sourcesource linekindsource criterion
feasibility-assessment42numeric-table\
feasibility-assessment75numeric\
feasibility-assessment76numeric\
feasibility-assessment77numeric\
feasibility-assessment78numeric\
maturity-diagnosis23numeric\
maturity-diagnosis24numeric\
maturity-diagnosis25numeric\
maturity-diagnosis53textual1. Identify relevant dimensions for the candidate (minimum: technical, market, regulatory, resource, organizational)
maturity-diagnosis64numericoverall_readiness: <1-9 TRL scale>
constraint-identification23numeric\
constraint-identification24numeric\
constraint-identification25numeric\
constraint-identification58numeric4. For constraints with removability score > 0.3, design removal-path
resource-envelope-estimation24numeric\
resource-envelope-estimation25numeric\
resource-envelope-estimation26numeric\
resource-envelope-estimation58numeric3. Identify >= 2 analogous projects and extract their actual resource consumption
resource-envelope-estimation61numeric6. Flag any estimates with confidence < 0.5 for further investigation
comparative-feasibility-ranking16textualPurpose: Produce a defensible ranking of candidates by feasibility. Uses multi-dimensional radar charts to visualize relative strengths and a weighted feasibility index to collapse multiple dimensions into a single comparable score.
comparative-feasibility-ranking27numeric\
comparative-feasibility-ranking28numeric\
comparative-feasibility-ranking29numeric-table\
comparative-feasibility-ranking58numeric2. Normalize scores to a common scale (1-9 recommended)
maturation-pathway-design27numeric\
maturation-pathway-design28numeric\
maturation-pathway-design29numeric-table\
maturation-pathway-design37textual\
maturation-pathway-design62textual2. Define target readiness required for implementation
maturation-pathway-design76textualtarget_readiness:
multi-dimensional-readiness-scan23textual3. Bottleneck Identification - Analyze the radar for dimensions significantly below the mean or below required thresholds. Deploy bottleneck-identification SOP on the radar data.
multi-dimensional-readiness-scan29numeric-table\
multi-dimensional-readiness-scan30numeric-table\
multi-dimensional-readiness-scan31numeric-table\
multi-dimensional-readiness-scan39numeric- Each dimension should have at least 2 evidence items supporting the score
multi-dimensional-readiness-scan41textual## Minimum Yield
multi-dimensional-readiness-scan43numeric- Complete radar with >= 5 dimensions scored
constraint-drilling26numeric4. Removal Path Design - For constraints with removability > 0.3, design concrete steps to remove or mitigate them. Deploy removal-path SOP for each removable constraint.
constraint-drilling32numeric-table\
constraint-drilling33numeric-table\
constraint-drilling34numeric-table\
constraint-drilling35numeric-table\
constraint-drilling42numeric- Stage 4 only runs for constraints with removability score > 0.3
constraint-drilling45textual## Minimum Yield
constraint-drilling47numeric- Classified constraint list with >= 3 constraints identified
constraint-drilling49numeric- Removal paths for all constraints scoring removability > 0.3
staged-gate-evaluation19textual1. Gate Criteria Definition - Define what must be true for a candidate to pass each gate. Deploy gate-criteria-definition SOP for each stage gate.
staged-gate-evaluation29numeric-table\
staged-gate-evaluation30numeric-table\
staged-gate-evaluation31numeric-table\
staged-gate-evaluation35numeric- Stage 1 should define >= 3 gates (e.g., concept feasibility, technical feasibility, implementation readiness)
staged-gate-evaluation42textual## Minimum Yield
constraint-analysis44textual## HARD-GATE
constraint-analysis46textualBefore entering this campaign, the following must be true:
constraint-analysis78textual## Budget Gate
constraint-analysis94textual## Minimum Yield
constraint-analysis97numeric- At least 1 binding constraint identified and characterized
constraint-analysis100numeric- No unresolved conflicts between top-3 constraints
resource-constraint63textual## Budget Gate
assumption-constraint55numeric- Top-5 fragile assumptions with validation paths
assumption-constraint58textual## Budget Gate
dependency-constraint60textual## Budget Gate
conflict-resolution66textual## Budget Gate
constraint-tree-building25textual- Minimum 5 UDEs for a meaningful tree
constraint-tree-building42numeric- When to escalate: If >10 UDEs found, prioritize top-5 by severity before tracing
constraint-tree-building43textual- Quality gate: Every causal link must have a BECAUSE clause (the underlying assumption)
sensitivity-ranking25textual- Express gaps in comparable units where possible
sensitivity-ranking43numeric- When to skip: If only 1-2 constraints exist, ranking is trivial
sensitivity-ranking44numeric- Threshold: Constraints with sensitivity score >2* the median are "binding"
constraint-breaking26textual- If constraint is not a dilemma, reframe: "We need X" vs "We cannot have X because Y"
constraint-breaking29numeric- Input: all assumptions from the EC (typically 8-15 assumptions across 4 arrows)
constraint-breaking35textual- Injection must be: specific, actionable, within our control, and testable
constraint-breaking36numeric- Generate 2-3 candidate injections
constraint-breaking43textual- What conditions (prerequisites) must hold?

Context checkpoint / Delta notes

Append dimension scores/evidence, constraint IDs, bottleneck rationale, resources, gates, and unresolved conflicts. | constraint-breaking | 54 | numeric | - Success criterion: At least one injection that resolves the conflict with <=2 manageable side effects |

Frequently asked questions

What does the Analyze Constraints Readiness AI skill do?

Characterize what blocks a research target: readiness dimensions, obstacles, bottlenecks, resource gaps, causal constraints, dependencies, conflicts, removability, and mitigation pathways. Depth selects lightweight triage, readiness assessment, or causal/TOC-style constraint analysis.

Why use Analyze Constraints Readiness on TypingMind?

Because you install it once and use it with any model. Analyze Constraints Readiness is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyze Constraints Readiness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analyze-constraints-readiness. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyze Constraints Readiness?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyze Constraints Readiness?

As many as you like. As long as a model supports skills, you can use Analyze Constraints Readiness with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyze Constraints Readiness AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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