# Using Proteomics Skills > | - **Type:** Skill - **Install:** `agentstack add skill-mannlabs-proteomics-agent-skills-using-proteomics-skills` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [MannLabs](https://agentstack.voostack.com/s/mannlabs) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [MannLabs](https://github.com/MannLabs) - **Source:** https://github.com/MannLabs/proteomics-agent-skills/tree/main/plugins/proteomics/skills/using_proteomics_skills ## Install ```sh agentstack add skill-mannlabs-proteomics-agent-skills-using-proteomics-skills ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Using Proteomics Skills This is the **meta-skill** that governs how all other proteomics skills are discovered, sequenced, and applied. Each specialized skill encodes the process an expert proteomics analyst would follow for one phase of work. This skill maps an incoming task to the right phase, routes to the corresponding skill, and keeps the overall analysis on track. When a proteomics task arrives, identify the current **phase** and apply the corresponding skill. Do not improvise a phase that a dedicated skill already covers. ## Skill Discovery Route the task using the decision logic below: - **Don't know what the data is or which engine produced it yet?** → `reading-proteomics-data` - **Have a matrix but unsure if samples/features are trustworthy?** → `performing-proteomics-quality-control` - **Need to make samples comparable / stabilize variance?** → `normalizing-proteomics-data` - **Missing values blocking a method that needs a complete matrix?** → `imputing-proteomics-data` - **Suspect plate, instrument, or time-point bias?** → `correcting-proteomics-batch-effects` - **Ready to test which proteins change between conditions?** → `performing-statistical-analysis` - **Have a list of regulated proteins and need biological meaning?** → `interpreting-biological-results` - **Need to synthesize results into a narrative or hypothesis?** → `formulating-biological-findings` - **Writing or finalizing any analysis code?** → `applying-code-standards` (cross-cutting) - **Implementing any of the above in Python?** → `analyzing-proteomics-data` (use `alphapepttools`, cross-cutting) If the request spans multiple phases (e.g. "run the full analysis"), follow the **Lifecycle Sequence** below in order. ## Core Operating Behaviors These behaviors apply at all times, across all skills. They are non-negotiable. ### 1. Surface Assumptions Before implementing anything non-trivial, explicitly state your assumptions: ``` ASSUMPTIONS I'M MAKING: 1. [assumption about inputs — e.g. search engine, table type, intensity type] 2. [assumption about parameters or thresholds — e.g. completeness cutoff, FDR, fold-change] 3. [assumption about experimental design — e.g. groups, replicates, control vs. treatment] → Correct me now or I'll proceed with these. ``` Don't silently fill in ambiguous requirements. Surface uncertainty early — it's cheaper than rework. ### 2. Manage Confusion Actively When you encounter inconsistencies, conflicting requirements, or unclear specifications: 1. **STOP.** Do not proceed with a guess. 2. Name the specific confusion. 3. Present the tradeoff or ask the clarifying question. 4. Wait for resolution before continuing. **Bad:** Silently picking one interpretation and hoping it's right. **Good:** Naming the disagreement and asking which side wins. ### 3. Push Back When Warranted You are not a yes-machine. When an approach has clear problems: - Point out the issue directly - Explain the concrete downside (quantify when possible) - Propose an alternative - Accept the human's decision if they override with full information Sycophancy is a failure mode. Honest technical disagreement is more valuable than false agreement. ### 4. Enforce Simplicity Your natural tendency is to overcomplicate. Actively resist it. Before finishing any implementation, ask: - Can this be done in fewer steps? - Are these abstractions earning their complexity? - Would a senior analyst look at this and say "why didn't you just..."? Prefer the boring, obvious solution. Cleverness is expensive. For proteomics workflows this usually means reaching for `alphapepttools` rather than hand-rolling an equivalent. ### 5. Maintain Scope Discipline Touch only what you're asked to touch. Do NOT: - "Clean up" code orthogonal to the task - Refactor adjacent systems as a side effect - Delete code or data that seems unused without explicit approval - Silently re-run or alter an upstream analysis phase that wasn't requested Your job is surgical precision, not unsolicited renovation. ### 6. Verify, Don't Assume Every skill includes a verification step. A task is not complete until that step passes. "Looks right" is never sufficient — follow the verification procedure in the invoked skill, and check a phase's success criteria before advancing to the next. ## Failure Modes to Avoid These are orchestration-level failures. Method-level pitfalls (e.g. when to impute, normalize, or batch-correct) are owned by the individual skills. - Advancing to the next phase without checking the current phase's success criteria. - Re-implementing functionality that `alphapepttools` already provides. - Drawing biological conclusions before the computational analysis is complete and verified. ## Lifecycle Sequence The typical end-to-end workflow runs in six sequential phases: ### Phase 1: Reading proteomics data Read search engine outputs (e.g., PSM and protein tables). * **Skill:** `reading-proteomics-data` ### Phase 2: Controlling data quality Check technical quality (e.g., feature counts, missing values) and filter out decoys, outlier samples, or low-evidence features. * **Skill:** `performing-proteomics-quality-control` ### Phase 3: Preprocessing data Apply data transformations including normalization, imputation, or batch correction. * **Skills:** `normalizing-proteomics-data`, `imputing-proteomics-data`, `correcting-proteomics-batch-effects` ### Phase 4: Performing statistical analysis Identify significant changes between conditions using differential expression analysis to obtain p-values, fold-changes, volcano plots, and more. * **Skill:** `performing-statistical-analysis` ### Phase 5: Interpreting biological results Translate regulated protein lists into biological pathways (e.g., Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA)). * **Skill:** `interpreting-biological-results` ### Phase 6: Formulating biological findings Synthesize results into findings to understand potential connections between proteins and their roles in biological mechanisms or disease genesis. * **Skill:** `formulating-biological-findings` The two cross-cutting skills apply throughout: `analyzing-proteomics-data` (implement workflows with `alphapepttools`) and `applying-code-standards` (code quality for reproducible analysis). ## Instructions 1. **Determine Phase:** Identify where the user stands in their analysis: at the beginning or in the middle. Use **Skill Discovery** to route to the right skill. 2. **Strategy Selection:** Think through the analysis **workflow** and use the **knowledge** in the specific skill for each phase. 3. **Plan:** Plan each step considering the study context and provided source data. Outline the strategy and parameters you would use for each step. Define success criteria for each phase, which Claude must verify before moving to the next phase. 4. **Verify:** Ensure the plan includes the following sequence. Copy the following checklist into the chat to track analysis progress: ```markdown Analysis Progress: - [ ] Phase 1: Reading proteomics data - [ ] Phase 2: Controlling data quality - [ ] Phase 3: Preprocessing data: Normalizing, Imputing, Batch effects - [ ] Phase 4: Performing statistical analysis - [ ] Phase 5: Interpreting biological results - [ ] Phase 6: Formulating biological findings ``` ## Example Analysis Plan 1. Reading proteomics data: Loading pg_matrix.tsv. - Success Criteria: Engine is identified as DIA-NN; columns are mapped to standardized names; decoys are removed; the resulting matrix has samples as rows and numeric intensities as values. 2. Controlling data quality: Filter out proteins with fewer than 2 peptides. Compute per-sample total intensity and feature count; flag and remove MAD-based outliers (N = 3) on both metrics. Then apply a 70% feature completeness threshold (bulk tissue default). - Success Criteria: All features with less than 70% completeness are removed 3. Preprocessing data: - Normalization: Log2-transform intensities (treating zeros as NA). Assess normalization need by computing correlation between PC1 and log10(total intensity per sample). If correlation is strong, apply median centering. Evaluate with Pooled Median Absolute Deviation (PMAD) - Imputation: Plot feature intensity versus missingness rate to classify the dominant pattern (MNAR vs. MCAR). If MNAR-dominant, apply MinProb imputation; if MCAR-dominant, apply BPCA. Validate by comparing intensity distributions before and after imputation and checking PCA stability via Procrustes analysis. - Success Criteria: PMAD decreases & PC–intensity correlation should drop after normalization; imputed value distribution does not create artificial modes; PCA structure remains stable after imputation; no NaN values remain. 4. Performing statistical analysis: Define WT as control and KO as treatment. Apply a two-sided t-test (moderated via limma's eBayes since fewer than 5 replicates per group) to compute log2 fold-changes and raw p-values. Apply Benjamini-Hochberg correction to obtain q-values. Mark proteins as significant at q-value 1. Generate a volcano plot and a z-score-normalized heatmap of significant proteins with hierarchical clustering. - Success Criteria: A results table (results_statistical_analysis.tsv) exists with columns protein_group, gene_name, log2_fold_change, p_value, -log10_pvalue, q_value, is_significant. Heatmap shows replicates clustering by condition. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [MannLabs](https://github.com/MannLabs) - **Source:** [MannLabs/proteomics-agent-skills](https://github.com/MannLabs/proteomics-agent-skills) - **License:** Apache-2.0 Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-mannlabs-proteomics-agent-skills-using-proteomics-skills - Seller: https://agentstack.voostack.com/s/mannlabs - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.