Agent Native

integrate-interpretations

Integrates recent interpretations from an interpretation log into an existing scientific theory, reconciling new experimental results, literature findings, or…

by ImbueOfficial publisherNot reviewed

Identity proven by GitHub's verified organisation badge (imbue-ai)

Agents
Claude Code
License
AGPL-3.0 (copyleft)
Pin
a2ee48cpinned Oct 9, 2026
Context cost
about 2,600 tokenswhen the skill loads
GitHub stars
36on imbue-ai/catalyst
Scripts
None found

What it does

“Integrate recent interpretations from the interpretation log into the associated theory.”
The author's description, from SKILL.md

Generated summary from the skill's own text

Integrates recent interpretations from an interpretation log into an existing scientific theory, reconciling new experimental results, literature findings, or solution candidates with the current body of evidence. The agent updates the theory document to reflect new insights, adjusts confidence levels, and optionally branches into two alternative theories exploring different research directions.

When to use it

  • New experimental results or literature findings need integration into existing theory
  • Conflicting evidence requires reconciliation with previous theory statements
  • Research progress needs consolidation before planning next experiments
  • Alternative theoretical directions warrant simultaneous exploration

When not to

  • Interpretation log is empty or no new findings to integrate
  • Initial theory creation from scratch is needed instead of updating existing theory

Install

These commands install from the publisher's repository and need no account.

skills CLI

npx skills add imbue-ai/catalyst --skill integrate-interpretations

Installs from the repository's default branch, which may have changed since the commit we checked.

GitHub CLI 2.90 or later

gh skill install imbue-ai/catalyst integrate-interpretations@a2ee48c34741df5d4549d97b22aa44589d9eaa6a

Installs the commit we checked (a2ee48c).

Copy by hand

Download the folder at a2ee48c and put it in .claude/skills/integrate-interpretations for Claude Code and in .agents/skills/integrate-interpretations for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.

Skill folder on GitHub at a2ee48c

Copy for your agent

A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.

Prompt

Install the agent skill "integrate-interpretations" by Imbue.

Source, pinned to commit a2ee48c34741df5d4549d97b22aa44589d9eaa6a:
https://github.com/imbue-ai/catalyst/tree/a2ee48c34741df5d4549d97b22aa44589d9eaa6a/src/claude_skills/skills/integrate-interpretations

1. Download that folder at exactly this commit. Do not use the default branch.
2. Copy every file unchanged, keeping the folder structure, into .claude/skills/integrate-interpretations/ for Claude Code and .agents/skills/integrate-interpretations/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist.
3. Do not edit the files, and do not run any script in the folder unless I ask you to.
4. When you are done, list the files you copied and where you put them.

Listing and checks: https://www.agentnative.dev/skills/imbue/integrate-interpretations

SKILL.md at the pin

---
name: integrate-interpretations
description: "Integrate recent interpretations from the interpretation log into the associated theory."
argument-hint: "theory ID, and optionally whether to branch into two distinct theories"
---

You are an expert scientific agent. We are working on a theory in order to solve a particular research goal. Recently, some additional data has become available (such as experiment results, new literature findings, or competing solution candidates). Your current task now is to update your theory to integrate the new insights from these recent findings.

## Mandate
- The resulting `theory.md` file should encompass the entirety of your research progress and current best understanding so far.
- Sometimes, new results and interpretations may conflict with your existing understanding. In such cases, be extra rigorous in reviewing the new evidence, comparing it to previous evidence mentioned in the theory, and try to reconcile any contradictions. You should update and/or remove previous statements from the theory if they are no longer consistent with the overall body of evidence.

## Input
Arguments: $ARGUMENTS

The arguments contain a theory ID (like `T_20260414_...`). Parse the theory ID from the arguments.
It might additionally contain an instruction to indicate that you should BRANCH into two distinct theories.

## Folder setup
All commands must be run in the current working directory. Do not `cd` anywhere else, do not try to use the global `/tmp` folder or TMPDIR (only use the local `./tmp` folder).

Set up two folders — one for input context, one for your own output:
CONTEXT_DIR: `mktemp -d -p ./tmp integrate-interpretations-context-XXXX`
OUTPUT_DIR: `mktemp -d -p ./tmp integrate-interpretations-output-XXXX`

Run this command to populate the context, and then initialize the output folder with the original theory files:
```bash
uv run python <SKILL_BASE_DIR>/scripts/context_manager.py create_context \
    --for_agent_type integrate-interpretations \
    --target_folder <CONTEXT_DIR> \
    --from_theory <THEORY_ID>
cp -r "<CONTEXT_DIR>/theory/"* "<OUTPUT_DIR>/"
```

- `<CONTEXT_DIR>/theory/` — the original theory (read-only input). Read `<CONTEXT_DIR>/theory/theory.md`, `<CONTEXT_DIR>/theory/interpretation_log.md`, and any artifacts.
- `<OUTPUT_DIR>/` — write your updated theory, experiments, and any supporting notes here.

## Obtaining cited experiment IDs
Your inputs may cite specific experiment IDs (`X_...`). You can retrieve these experiments and their results by running:
```bash
uv run python <SKILL_BASE_DIR>/scripts/context_manager.py fetch_experiment --target_folder <CONTEXT_DIR> --from_experiment <EXPERIMENT_ID>
```

This command will place the experiment description (`description.md`), Python script (`script.py`), and results into the `<CONTEXT_DIR>/experiments/<EXPERIMENT_ID>` folder.

## Obtaining cited solution IDs
Your inputs may cite specific solution IDs (`U_...`). You can retrieve these solutions and their contents by running:
```bash
uv run python <SKILL_BASE_DIR>/scripts/context_manager.py fetch_solution --target_folder <CONTEXT_DIR> --from_solution <SOLUTION_ID>
```

This command will place the solution candidate description (`solution.md`) and any related files into the `<CONTEXT_DIR>/solutions/<SOLUTION_ID>` folder.

## Theory Output Format
Your `theory.md` file must be: A revised theory that integrates the new interpretations from the interpretation log.

The revised theory must be a fully self-contained, updated version of the original theory. Do NOT add any notes inside the file about the adherence review or the improvement process itself. The file should read like a standalone document that presents the final improved theory.

Please maintain the following guidelines for the improved theory:
- Start with including the current research goal itself. You can always rever back to the file `goal.txt` in the current working directory to read the research goal again if you need to.
- Structure the main part of your theory as a set of key observations and insights. For each, cite supporting evidence (such as experiment IDs `X_...`, literature citations, or mathematical reasoning). For each observation or insight, add a tag that expresses your confidence in that statement as high, medium, or low, depending on the strength of the evidence that has been seen so far. Be conservative in your confidence assessments: Typically, you'll need many independent experiments or at least one reliable literature source to warrant a confidence level that's medium or high!
- Feel free to organize the observations and insights into logical subsections, tables, or other types of groupings to improve the readability and brevity of the theory.
- After the main part, add two sections subsectionsthat express: 1. open questions and uncertainties (e.g. unresolved contradictions in the evidence, areas where evidence is still lacking, or important knowledge gaps that remain), and 2. a "Research Plan" section in which you lay out your current ideas and plans to move further towards solving the stated goal.
- When integrating solution candidates from the interpretation log, keep track of the highest-potential solution candidate seen so far in a short separate section of the theory. Include its solution ID (`U_...`) and verification experiment ID (`X_...`) for easy lookup.
- Image references in the markdown file need to be relative to `<OUTPUT_DIR>`. NEVER use absolute paths. Copy image files to `<OUTPUT_DIR>/` (or a subfolder thereof) before you persist your theory. Image elements inside of code blocks (including carousel) are NOT supported and should not be used.
- Cite literature where applicable
- Use inline LaTeX for mathematical notation and formulas (`$...$` for inline math, and `$$...$$` for display math). Do NOT put formulas into code blocks.
- Keep the title of the theory representative of its current key direction. Update the title `# ...` whenever the direction of the theory has changed and its current title is no longer representative.

## Branching
*If* the input tells you to branch into two distinct theories, do this as follows:
1. First create the regular updated theory as usual, and save it to the database (this will capture the current state of `<OUTPUT_DIR>/theory.md` at the time).
2. If any of the new interpretations were contradictory or unclear, consider updating your second theory to express the opposite or alternative view.
3. Then, most importantly, come up with a second distinct "Research Plan" that is fundamentally different from your first one. Be bold here, and explore less-common approaches. Try to come up with novel ideas that go outside of the obvious directions and which are significantly different from anything tried so far! Consider going back to the drawing board, or taking big high-risk/high-reward bets, rather than just continuing with conservative, incremental steps.
4. Once you have updated the theory in `<OUTPUT_DIR>/theory.md`, save the second theory to the database as well.

## Execution Steps
1. **Context Checkout**: Run the bash command above to obtain the existing theory using `context_manager.py`.
2. **Review Current Theory and Interpretations**: Read `<CONTEXT_DIR>/theory/theory.md` and additionally `<CONTEXT_DIR>/theory/interpretation_log.md` to understand the research goal, current integrated theory we have developed so far, and the additional recent interpretation notes that have not yet been integrated back into the theory.
   - Special case: If the `interpretation_log.md` file does not exist or is empty, stop here. Simply report back the original theory ID from your inputs, together with a note that no changes were needed. You DO NOT need to read the `theory.md` or store a new result in this case. Simply report your input theory ID back unchanged.
3. **Refinement Strategy**:
   - For each new result in the `interpretation_log.md`, identify the necessary updates and additions to the theory's observations and conclusions. Especially look out for any conflicting statements or ideas that need to be revised. In extreme cases, a complete restructuring of the theory may be needed.
   - Check if, given the new interpretations, the confidence of any of the existing statements should be adjusted (either upwards or downwards).
   - Obtain cited experiment IDs as needed to review the experiment details and results to inform your revisions.
   - Then, revise the open questions and current ideas / plan sections in the theory based on the new information. Remove questions that have been answered, and add new questions or ideas that are emerging.
   - This is the time to consider whether the current research direction is still the most promising one, or if it has shown diminishing returns. Please zoom out and consider other approaches to achieve the goals and whether it might be worth a shot to switch to one of them, as radical as they might seem.
4. **Polish & Restructuring**: After you have made the necessary content updates, review the overall structure and flow of the theory. If any section has become too long or contains too many items, consider reorganizing the content into different (sub-)sections, adding new headings, combining similar items, organizing related items together into tables, or doing whatever else is needed to improve the readability and coherence of the document.
5. **Write Improvements**: Edit the theory in `<OUTPUT_DIR>/theory.md` to apply your improvements. Then, delete the `interpretation_log.md` file in `<OUTPUT_DIR>/` (or clear its contents) since all of the interpretations have now been integrated back into the theory.
6. **Store results**: Persist your output:
   ```bash
   uv run python <SKILL_BASE_DIR>/scripts/context_manager.py store_results --from_agent_type integrate-interpretations --from_folder <OUTPUT_DIR> --parent_theory <THEORY_ID>
   ```
   Note down the returned theory ID (e.g. `T_20260414_150000_x1y2z3`) as the result of this skill and include it in your final message.
7. **Branching (optional)**: If (and only if) you were instructed to branch into two distinct theories, come up with a second alternative theory, as described above in the section on Branching. You can edit the theory in `<OUTPUT_DIR>/theory.md` in-place. Make sure to update the title of the second theory to reflect its new direction. Then, run the previous step a second time to store the alternative theory in the database, and note down its returned theory ID (e.g. `T_20260414_150000_a1b2c3`) as well. In this case, you should return both theory IDs in your final message.

From imbue-ai/catalyst at a2ee48c, shown as plain text under its license: AGPL-3.0 (copyleft). The license text is in LICENSE.

View SKILL.md on GitHub at a2ee48c

Files

1 file, 11 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.

Skill folder on GitHub at a2ee48c
All 50 skills from Imbue

More from Imbue

  • architect

    Generates an architecture document for a feature by reading the existing spec, analyzing the codebase deeply, and conducting guided Q&A with the user to…

    Claude Code · about 4K tokens · pinned 6 days ago

  • build

    This skill executes a multi-step implementation plan by working through task files sequentially.

    Claude Code · about 1.1K tokens · pinned 6 days ago

  • build-sculptor-extension

    Build or modify Sculptor extensions, which are runtime ES modules that add panels, overlays, widgets, home views, or settings UI to the Sculptor frontend.

    Contains scripts

    Claude Code, Codex, Cursor +3 · about 3.3K tokens · pinned 6 days ago

  • fix-bug

    Fixes bugs using test-driven development with an interactive or autonomous workflow.

    Claude Code · about 9.8K tokens · pinned 6 days ago

  • handoff

    This skill hands off ongoing work to a fresh agent, either in the same workspace or a new isolated workspace, while preserving context through a synthesized…

    Claude Code · about 1.1K tokens · pinned 6 days ago

  • help

    This skill fetches the latest Sculptor documentation from GitHub and answers user questions about how to use Sculptor's features, including workspaces…

    Claude Code · about 880 tokens · pinned 6 days ago

All Docs and writing skills
  • n8n-docs-assistant

    This skill answers questions about n8n configuration, setup, credentials, nodes, and troubleshooting by retrieving information from current n8n documentation.

    by n8nOfficial publisher

    Claude Code, Codex, Cursor +3 · about 450 tokens · pinned 6 days ago

  • algodocs-automation

    This skill automates Algodocs document processing tasks through Composio's toolkit via the Rube MCP server.

    by ComposioOfficial publisher

    Claude Code, Codex, Cursor +3 · about 690 tokens · pinned 6 days ago

  • remotion-docs

    This skill enables agents to search and retrieve Remotion documentation via the Algolia API and fetch pages as Markdown.

    by RemotionOfficial publisher

    Claude Code, Codex, Cursor +3 · about 320 tokens · pinned 6 days ago

  • remotion-docs

    This skill searches the Remotion documentation using the Algolia API and retrieves documentation pages as Markdown.

    by RemotionOfficial publisher

    Claude Code, Codex, Cursor +3 · about 320 tokens · pinned 6 days ago

  • receipts

    This skill generates a personal Claude Code usage report from local session transcripts stored in ~/.claude/projects, mining activity across projects without…

    by AnthropicOfficial publisher

    Contains scripts

    Claude Code · about 4.1K tokens · pinned 6 days ago

  • solo

    This skill enables an agent to work through a single difficult mathematics problem independently within one session.

    by AnthropicOfficial publisher

    Claude Code · about 1.4K tokens · pinned 6 days ago

Open roles at Imbue

No open roles at Imbue on the Agent Native jobs board right now.

Browse the jobs board

The skills listed here are third-party software, collected from public GitHub repositories as a catalog of recent skills and provided as is. Agent Native does not write or control them and is not responsible for any harm, loss or damage caused by installing or using them. Labels and checks describe what we saw at one commit and are not a guarantee. Read a skill's files before you run it. Terms of use