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entroly-context-control

Surgically select, compress, and recover codebase context using Entroly's MCP tools. Slashes token usage by up to 95% with Merkle-exact recovery receipts and WITNESS verification.

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Price unconfirmed★ 476 GitHub starsRegistry updated · Oct 13, 2026context compression developer-tools

Overview

Entroly Context Intelligence Skill

Use this skill to optimize context, query the codebase with token budgets, compress bulky tool outputs, and recover omitted code byte-for-byte using the Entroly MCP server.

Core Workflows

1. Token-Budgeted Codebase Selection (`optimize_context`)

When answering a complex question or preparing a refactor across multiple files:

Call optimize_context with your natural language query and a token budget (e.g., budget=4000 or budget=8000).
Entroly executes a knapsack optimization algorithm that ranks AST fragments by relevance, entropy, and dependency relations.
Returns surgical code fragments, line numbers, and a cryptographic Merkle receipt.

2. Semantic Code Recall (`recall`)

When searching for specific symbols, implementations, or interfaces:

Call recall(query="...") with top_k=5 or top_k=10.
Returns ranked pointers with filenames, line numbers, and snippet digests.

3. Reversible Output Compression (`compress` & `recover`)

When dealing with large terminal dumps (build traces, test logs):

Call compress(data=..., budget=...) to distill down critical errors and signals while stripping noise.
Every compression produces a content-addressed SHA-256 handle.
To restore the original uncompressed text at any time, call recover(handle="...").

4. Codebase Health & Architecture Diagnostics (`health`)

To assess codebase quality before or after major changes:

Call health() to inspect clone pairs, dead symbols, god files, and architecture violations.

5. Grounding & Hallucination Verification (`witness_verify`)

To verify an agent claim or proposed code against source evidence:

Call witness_verify(claim=..., evidence=...) for local NLI-backed verification.

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File metadata

name: entroly-context-control
description: "Surgically select, compress, and recover codebase context using Entroly's MCP tools. Slashes token usage by up to 95% with Merkle-exact recovery receipts and WITNESS verification."

View original text

---
name: entroly-context-control
description: "Surgically select, compress, and recover codebase context using Entroly's MCP tools. Slashes token usage by up to 95% with Merkle-exact recovery receipts and WITNESS verification."
---

# Entroly Context Intelligence Skill

Use this skill to optimize context, query the codebase with token budgets, compress bulky tool outputs, and recover omitted code byte-for-byte using the Entroly MCP server.

## Core Workflows

### 1. Token-Budgeted Codebase Selection (`optimize_context`)
When answering a complex question or preparing a refactor across multiple files:
- Call `optimize_context` with your natural language query and a token budget (e.g., `budget=4000` or `budget=8000`).
- Entroly executes a knapsack optimization algorithm that ranks AST fragments by relevance, entropy, and dependency relations.
- Returns surgical code fragments, line numbers, and a cryptographic Merkle receipt.

### 2. Semantic Code Recall (`recall`)
When searching for specific symbols, implementations, or interfaces:
- Call `recall(query="...")` with `top_k=5` or `top_k=10`.
- Returns ranked pointers with filenames, line numbers, and snippet digests.

### 3. Reversible Output Compression (`compress` & `recover`)
When dealing with large terminal dumps (build traces, test logs):
- Call `compress(data=..., budget=...)` to distill down critical errors and signals while stripping noise.
- Every compression produces a content-addressed SHA-256 handle.
- To restore the original uncompressed text at any time, call `recover(handle="...")`.

### 4. Codebase Health & Architecture Diagnostics (`health`)
To assess codebase quality before or after major changes:
- Call `health()` to inspect clone pairs, dead symbols, god files, and architecture violations.

### 5. Grounding & Hallucination Verification (`witness_verify`)
To verify an agent claim or proposed code against source evidence:
- Call `witness_verify(claim=..., evidence=...)` for local NLI-backed verification.

Review the source

Price & running costs

Get the skill: Price unconfirmed
Run it: Requirements have not been confirmed. Check the source for agent, API and service charges.
License: Apache-2.0
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: juyterman1000/entroly
License: Apache-2.0
Version: Unknown
Last GitHub push: Oct 13, 2026
Registry updated: Oct 13, 2026
Instruction path: .agents/plugins/entroly/skills/entroly-context-control/SKILL.md @ 5ddac950a5ac

Version reported in registry metadata; check source releases before relying on it.

Quality

72/100

Strong

Trust

67/100

Sandbox only

Audit

79/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

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More details

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}

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Creator: juyterman1000
Source: juyterman1000/entroly
Indexed by: OpenAgentSkill community index

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Overview

Entroly Context Intelligence Skill

Core Workflows

1. Token-Budgeted Codebase Selection (optimize_context)

2. Semantic Code Recall (recall)

3. Reversible Output Compression (compress & recover)

4. Codebase Health & Architecture Diagnostics (health)

5. Grounding & Hallucination Verification (witness_verify)

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

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Add the evidence badges to your README

Community signal

1. Token-Budgeted Codebase Selection (`optimize_context`)

2. Semantic Code Recall (`recall`)

3. Reversible Output Compression (`compress` & `recover`)

4. Codebase Health & Architecture Diagnostics (`health`)

5. Grounding & Hallucination Verification (`witness_verify`)