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Analysis Capability Ladder

Community
Sahil-SS9
analysis-capability-ladder

Pick the strongest analysis level available, discover and propose new tools when a gap blocks the answer, state the level used, and finish within budget.

Overview

PublisherSahil-SS9
Repositoryagent-personas
Skill nameanalysis-capability-ladder
Stars
67
Forks
4
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Sahil-SS9 on GitHub. Read the source before you install it.

Installation

Install the Analysis Capability Ladder 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/Sahil-SS9/agent-personas/tree/main/skills/analysis-capability-ladder
  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/Sahil-SS9/agent-personas.git /tmp/agent-personas
mkdir -p .claude/skills
cp -r /tmp/agent-personas/skills/analysis-capability-ladder .claude/skills/analysis-capability-ladder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analysis Capability Ladder 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 Analysis Capability Ladder 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 Analysis Capability Ladder 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.

Analysis capability ladder

The specialist's judgement is the same at every level; what changes is how much of the artefact it can see. Detect the level, use the best one in scope, say which one, and let the level set the confidence ceiling. Tools named below are current examples, not requirements; any tool that does the same job counts.

When to use

At the start of every investigation, and again whenever a question needs evidence the current level cannot reach.

Procedure

  1. Detect what is installed and in scope; do not assume. Record name and version of anything used.
  2. Choose the highest level the question needs and the scope allows:
    • Level 0, format tools (always available): strings, symbol/section listing, raw disassembly, hex view, a contained run. Reaches small binaries and confirms facts; on large or optimised code it cannot see structure. Confidence ceiling: claims about control flow are inferred unless byte-verified.
    • Level 1, decompiler (for example Ghidra headless, rizin/radare2 with a decompiler plugin, IDA, Binary Ninja): readable pseudocode, functions, references. The default workhorse once installed. Pseudocode is decompiler output, not source; types and names are guesses until corrected.
    • Level 2, integration layer (for example REA): wraps a decompiler and adds evidence records, callers/callees, explicit limitations, and app/web/.NET coverage. Use when present; it adds bookkeeping, not new truth.
    • Level 3, runtime (for example gdb, Frida, process capture): observed behaviour. Only inside owner-authorised isolation.
  3. Read at the level's natural unit: functions at Level 1-2, not whole-binary dumps. Ask for one function or one region at a time.
  4. Promote a finding only with corroboration from a second view (decompiler claim + bytes, or static claim + a contained run).
  5. Start an investigation ledger before deep exploration: record the question, known facts, competing hypotheses, evidence links, confidence, rejected explanations and next decision-relevant action. Keep hypotheses explicit; do not collapse ambiguity into a single story.
  6. Choose the next action by expected information gain: prefer the observation that distinguishes the leading hypotheses (for example, a caller trace, data-flow edge, decoded buffer, or contained run) over more undirected output. Stop when further actions are unlikely to change the verdict.
  7. For security questions, model source → transformations → sink and authentication/authorisation gates. Resolve indirect calls, callbacks, vtables and library boundaries where they affect the claim; an import or API name alone is not a behaviour.

Exploring new tools

The installed set is a starting point, not a ceiling. When a question needs a capability nothing installed provides (a format, language runtime, packer, firmware layout, protocol or platform the current tools cannot read), go looking.

  1. Name the gap precisely: what evidence is missing, and which level would supply it.
  2. Search for candidates: current open-source and vendor tools, format-specific parsers, unpackers, decompiler plugins and scripts. Prefer maintained, widely used, documented tools; note version and last release.
  3. Evaluate each candidate before proposing it: what it reads, licence, cost, install footprint (user-local or system), network access, trust signals (maintainers, signatures/checksums, issue history) and whether it can run offline inside isolation.
  4. Propose, do not install: give the owner a short recommendation (gap, best candidate, alternative, install scope, risk) and wait for approval. Never install system-wide, add paid tools, or run untrusted tooling outside isolation without explicit consent.
  5. Once approved: install user-local where possible, verify checksums, smoke-test on a known sample, record name and version, then continue the investigation at the new level.
  6. If nothing suitable exists, a small purpose-built script is a valid tool; keep it deterministic and include it with the evidence.

Finishing discipline

  • Keep working notes short; do not hand-decode long instruction runs when a higher level can show the same thing, and do not narrate arithmetic.
  • Budget: by the time about two-thirds of the tool budget is spent, stop exploring and write the report. Unanswered questions go under unknowns.
  • The final message is always the report. Never end on a tool request, never write or imitate tool output yourself.

Decision rules

  • The report states the level used and what that level could not show.
  • A missing tool lowers confidence; it never licenses guessing.
  • If only Level 0 is available on a large or optimised binary, say the result is partial instead of over-reading.
  • When the gap blocks the verdict, recommend the tool that would close it rather than stopping at "not installed".

Pitfalls

  • Treating Level 2 bookkeeping as extra evidence: it is the same decompiler underneath.
  • Escalating to runtime when static evidence already answers the question.

Done

A report that names its level and tools, reaches a verdict within budget, and lists what a higher level would add.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analysis Capability Ladder AI skill do?

Pick the strongest analysis level available, discover and propose new tools when a gap blocks the answer, state the level used, and finish within budget.

Why use Analysis Capability Ladder on TypingMind?

Because you install it once and use it with any model. Analysis Capability Ladder 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 Analysis Capability Ladder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Sahil-SS9/agent-personas/tree/main/skills/analysis-capability-ladder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analysis Capability Ladder?

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 Analysis Capability Ladder?

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

Is the Analysis Capability Ladder AI skill free?

Yes. It is published on GitHub by Sahil-SS9 under the MIT 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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