Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
Guidance for SPR and BLI binding characterization experiments.
Install Binding Characterization
$ npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install adaptyvbio/protein-design-skills binding-characterization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/binding-characterization .claude/skills/binding-characterization && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "binding-characterization" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binding-characterization into .claude/skills/binding-characterization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "binding-characterization", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binding-characterizationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install adaptyvbio/protein-design-skills binding-characterization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/binding-characterization .agents/skills/binding-characterization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "binding-characterization" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binding-characterization into .agents/skills/binding-characterization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "binding-characterization", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install adaptyvbio/protein-design-skills binding-characterization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/binding-characterization .cursor/skills/binding-characterization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "binding-characterization" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binding-characterization into .cursor/skills/binding-characterization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "binding-characterization", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/adaptyvbio/protein-design-skills.git --path skills/binding-characterization--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install adaptyvbio/protein-design-skills binding-characterization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/binding-characterization .gemini/skills/binding-characterization && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "binding-characterization" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binding-characterization into .gemini/skills/binding-characterization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "binding-characterization", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install adaptyvbio/protein-design-skills binding-characterizationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/binding-characterization .github/skills/binding-characterization && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "binding-characterization" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binding-characterization into .github/skills/binding-characterization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "binding-characterization", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install adaptyvbio/protein-design-skills binding-characterization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/binding-characterization .opencode/skills/binding-characterization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "binding-characterization" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/binding-characterization into .opencode/skills/binding-characterization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "binding-characterization", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
binding-characterization- GitHub stars
- 163
- Used in
- 3 other repos
- Token cost
- ~2.2k tokens
- SKILL.md length
- 906 words
- Files
- 1
- Skills in repo
- 24
- Repo updated
- First seen
- Licence
- MIT
At a glance
Guidance for SPR and BLI binding characterization experiments.
- Works in 5 steps: Start with mildest condition (high salt) → Test 30s contact time → Verify complete dissociation (return to… → …
- Planning binding kinetics experiments
- SKILL.md covers SPR vs BLI Decision Matrix, Key differences, Troubleshooting: Why BLI works… and Mass transport considerations, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Binding Characterization is an agent skill from adaptyvbio/protein-design-skills. Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science. The repository describes itself as: Claude Code skills for protein design. The licence is MIT.
When your agent uses it
- Planning binding kinetics experiments
- Troubleshooting poor/no binding signal
- Interpreting kinetic data artifacts
- Choosing between SPR vs BLI platforms
Example prompts
- “/binding-characterization”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Start with mildest condition (high salt)
- Test 30s contact time
- Verify complete dissociation (return to baseline)
- Verify retained ligand activity (repeat binding)
- Use shortest effective contact time
What it can do on your machine
Read from SKILL.md and the folder at commit 59dd633. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
nicoyalife.comsprpages.nlsartorius.hrpath.ox.ac.ukdhvi.duke.edupubs.acs.orgpmc.ncbi.nlm.nih.gov
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Binding Characterization loads about 2.2k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 906 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 906 words, ~2,224 tokens.
Download SKILL.mdSave it as
.claude/skills/binding-characterization/SKILL.md (or your agent's skills folder).- name
- binding-characterization
- description
- Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
- license
- MIT
- category
- experimental
- tags
- binding, spr, bli, validation
Binding Characterization: SPR and BLI
SPR vs BLI Decision Matrix
| Factor | Choose SPR | Choose BLI |
|---|---|---|
| Sensitivity | Small molecules, fragments (<500 Da) | Large complexes, antibodies |
| Throughput | Low-medium (serial) | High (96-well parallel) |
| Sample purity | Required (clogs fluidics) | Tolerates crude lysates |
| Kinetic resolution | Higher (better for fast kinetics) | Lower |
| Mass transport | More sensitive (may distort kon) | Less sensitive |
| Maintenance | High (fluidics system) | Low (dip-and-read) |
| Sample consumption | Higher (continuous flow) | Lower |
| Cost per experiment | Lower chip cost, higher run cost | Higher tip cost, lower run cost |
Key differences
SPR (Surface Plasmon Resonance)
- Mechanism: Detects refractive index changes at gold surface
- Surface: Gold chip with dextran matrix (CM5, CM7, etc.)
- Flow: Continuous microfluidics
- Best for: Small molecules, high-affinity, precise kon/koff
BLI (Biolayer Interferometry)
- Mechanism: Measures optical interference pattern shift
- Surface: Fiber optic biosensor tips (SA, Ni-NTA, AHC)
- Flow: Dip-and-read (no microfluidics)
- Best for: High-throughput, crude samples, antibody screening
Troubleshooting: Why BLI works but SPR doesn't
| Cause | Mechanism | Solution |
|---|---|---|
| Hydrophobic CDRs | Adsorb to SPR gold/dextran surface | Add 0.05% Tween-20, use CM7 chip with longer dextran |
| Aggregation | Mass transport artifacts in SPR fluidics | Filter sample (0.22μm), reduce ligand density |
| High instability | Degrades during continuous flow | Shorter cycle time, add stabilizers (trehalose 5%) |
| Charge mismatch | Nonspecific binding to charged dextran | Adjust buffer pH ±1 from pI, add BSA 1mg/mL |
| Slow dissociation | Long regeneration needed (damages ligand) | Use BLI (disposable tips) |
Why SPR works but BLI doesn't
| Cause | Mechanism | Solution |
|---|---|---|
| Small analyte | BLI less sensitive for <10 kDa | Use SPR with appropriate chip |
| Weak affinity (KD >10μM) | Fast dissociation in BLI dip | Increase analyte concentration |
| Low expression | Not enough signal | Increase biosensor loading |
Mass transport considerations
Mass transport limitation occurs when analyte cannot diffuse to the surface fast enough to maintain equilibrium. This distorts kinetic parameters.
Symptoms
- Observed kon appears slower than true kon
- Linear association phase (instead of exponential)
- kon varies with ligand density
- Rmax varies with flow rate
When mass transport matters
- High-affinity interactions (kon >10^6 M^-1s^-1)
- High ligand density (>500 RU)
- Slow flow rates (<30 μL/min in SPR)
- Large analytes (slow diffusion)
Mitigation strategies
| Strategy | SPR | BLI |
|---|---|---|
| Reduce ligand density | <200 RU for high-affinity | <0.5 nm shift loading |
| Increase flow rate | 50-100 μL/min | Increase shake speed (1000 rpm) |
| Use oriented immobilization | His-tag capture | Biotinylated ligand |
| Include in fitting | Mass transport model (kt) | Usually less critical |
Nonspecific binding mitigation
Buffer additives (ranked by effectiveness)
| Additive | Concentration | Mechanism | Best For |
|---|---|---|---|
| BSA | 0.5-1 mg/mL | Blocks hydrophobic sites | General use |
| Tween-20 | 0.02-0.05% | Prevents surface adsorption | Hydrophobic analytes |
| Trehalose | 1-5% | Stabilizes + blocks | Unstable proteins |
| Sucrose | 5% | BLI-specific blocker | BLI tips |
| Carboxymethyl dextran | 1 mg/mL | Competitive blocking | SPR with charged proteins |
| NaCl | 150-500 mM | Reduces ionic interactions | Charged proteins |
pH optimization
- Keep buffer pH at least 1 unit away from analyte pI
- pI near 7: Use pH 6.0 or 8.0 buffer
- Acidic proteins (pI <5): Use neutral or basic buffer
- Basic proteins (pI >9): Use slightly acidic buffer
Reference subtraction
Always include:
- Blank reference channel (no ligand)
- Buffer-only injections
- Non-specific binding controls
Regeneration conditions
SPR regeneration scouting (try in order)
| Condition | Targets | Caution |
|---|---|---|
| 10 mM Glycine pH 2.0-2.5 | Most protein-protein | May denature ligand |
| 10 mM Glycine pH 1.5 | Strong interactions | Harsh, limit exposure |
| 1-2 M NaCl | Ionic interactions | Mild, try first |
| 10 mM NaOH | Very stable ligands | Can hydrolyze proteins |
| 10 mM Glycine pH 9-10 | Acid-stable proteins | Can aggregate |
| 10 mM EDTA | His-tag, metal-dependent | Strips Ni-NTA |
| 4 M MgCl2 | Hydrophobic interactions | Check ligand stability |
Show full SKILL.md (344 more words)Show less
Regeneration protocol
- Start with mildest condition (high salt)
- Test 30s contact time
- Verify complete dissociation (return to baseline)
- Verify retained ligand activity (repeat binding)
- Use shortest effective contact time
BLI tips
- Tips are often disposable (no regeneration needed)
- For reuse: Same conditions as SPR, but shorter exposure
- Anti-His tips: 10 mM Glycine pH 1.5, 30s
- Streptavidin tips: Generally not regenerable
Common artifacts and solutions
Biphasic binding
Symptoms: Two-rate association or dissociation Causes:
- Sample heterogeneity (aggregates)
- Ligand heterogeneity (multiple conformations)
- Avidity effects (bivalent analyte)
Solutions:
- Filter/centrifuge sample
- Use monovalent Fab fragments
- Reduce ligand density
- Fit to heterogeneous model
Negative dissociation
Symptoms: Signal increases during dissociation phase Causes:
- Ligand leaching from surface
- Analyte aggregation on surface
- Reference channel drift
Solutions:
- Use capture antibody instead of direct immobilization
- Increase buffer stringency
- Better reference subtraction
Hook effect
Symptoms: Signal decreases at high analyte concentrations Causes:
- Surface saturation + rebinding suppression
- Crowding effects
Solutions:
- Reduce analyte concentration range
- Reduce ligand density
- Use smaller analyte fragments
Kinetic data quality checklist
Before analysis
- Reference-subtracted properly
- Buffer injection shows flat baseline
- Rmax consistent across concentrations
- No systematic drift during association
- Complete regeneration (return to baseline)
- Duplicate/triplicate injections consistent
Fitting quality
- Residuals randomly distributed (no systematic deviation)
- Chi² < 10% of Rmax (or < 1 RU² for low signals)
- kon and koff errors < 20% of values
- KD from kinetics matches equilibrium KD (within 3-fold)
- Fitted Rmax reasonable (close to theoretical)
Red flags
- kon approaching the mass transport limit (>10^7 M^-1s^-1), where rates are unreliable
- koff too fast to sample (> 0.1 s^-1) or too slow to measure in the dissociation window (< 10^-5 s^-1)
- Rmax >> theoretical maximum (aggregation or avidity)
- Large difference between kinetic and equilibrium KD
References
Platform comparisons
SPR protocols
Troubleshooting
- 4 Ways to Reduce NSB in SPR - Nicoya
- 3 Ways to Limit Mass Transfer Effects - Nicoya
- Suppressing NSB in BLI - ACS Omega
Regeneration
Mass transport
© adaptyvbio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/binding-characterization of adaptyvbio/protein-design-skills.
Open the folder on GitHubat commit 59dd633
Used in 3 other repositories
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Binding Characterization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Binding Characterization this skilladaptyvbio/protein-design-skills | 163 | 3 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 83k | 5 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 46k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Read arXiv Paperkarpathy/nanochat | 58k | 2 repos | ~494 | Automated safety check: Pass | MIT | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT |
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GitHub Deep Research
bytedance/deer-flow
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Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
Read arXiv Paper
karpathy/nanochat
Fetches the TeX source of an arXiv paper from its URL, reads it and writes a markdown summary tied to the nanochat project.
Content Research Writer
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
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Structured manuscript/grant review with checklist-based evaluation.
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Related
Categories
Questions about Binding Characterization
What does Binding Characterization do?
Guidance for SPR and BLI binding characterization experiments. Binding Characterization is an agent skill from adaptyvbio/protein-design-skills. Guidance for SPR and BLI binding characterization experiments.
When should I use Binding Characterization?
Binding Characterization fits situations like: planning binding kinetics experiments; troubleshooting poor/no binding signal; interpreting kinetic data artifacts; choosing between SPR vs BLI platforms.
How do I install Binding Characterization in Claude Code?
Run `npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a claude-code`. Or copy the skill folder (skills/binding-characterization in adaptyvbio/protein-design-skills) into .claude/skills/binding-characterization in your project. Claude Code loads it when a task matches its description.
How do I install Binding Characterization in Codex?
Run `npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a codex`. Or copy the skill folder (skills/binding-characterization in adaptyvbio/protein-design-skills) into .agents/skills/binding-characterization in your project. Codex loads it when a task matches its description.
Can I use Binding Characterization in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add adaptyvbio/protein-design-skills --skill binding-characterization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/binding-characterization, .gemini/skills/binding-characterization, .github/skills/binding-characterization and .opencode/skills/binding-characterization in your project.
What does Binding Characterization need to run?
SKILL.md names no scripts, command-line tools or credentials: Binding Characterization is instructions for the agent only.
Does Binding Characterization access the network?
SKILL.md names 7 domains. As links in the text: nicoyalife.com, sprpages.nl, sartorius.hr, path.ox.ac.uk, dhvi.duke.edu, pubs.acs.org and pmc.ncbi.nlm.nih.gov. This is read from the text; nothing was executed.
Is Binding Characterization safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Binding Characterization use?
Binding Characterization is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Binding Characterization use?
About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Binding Characterization?
Skills that share tags, products or a category with Binding Characterization: Hypothesis Generation (spacering-net/codeg, 3.8k stars), GitHub Deep Research (bytedance/deer-flow, 83k stars), Nature Paper Card (Yuan1z0825/nature-skills, 46k stars) and Read arXiv Paper (karpathy/nanochat, 58k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Binding Characterization?
adaptyvbio (a GitHub organization) maintains it in adaptyvbio/protein-design-skills, which has 163 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on June 11, 2026.
Source: adaptyvbio/protein-design-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.