binder-design-campaign

binder-design-campaign is a skill for Claude Code, Codex from SPYfighting/my-academic-skills. It costs 143 tokens per session (2,329 once invoked), scanned A, original, MIT.

A checkpoint-based workflow for designing or predicting proteins, including small binders that attach to a target protein, protein structures, and engineered protein variants.

In plain words
What is it for?
Use it to plan binder design, predict protein folds or complexes with calibrated confidence, or redesign proteins for stability, enzyme behavior, interfaces, or scaffolds.
Why use it?
It requires human approval at defined checkpoints and ties claims to executed computations or saved evidence before further computation or reporting.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to plan binder design, predict protein folds or complexes with calibrated confidence, or redesign proteins for stability, enzyme behavior, interfaces, or scaffolds.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/spyfighting/my-academic-skills/binder-design-campaign
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add SPYfighting/my-academic-skills --skill binder-design-campaign
Clone the repo
git clone --depth 1 https://github.com/SPYfighting/my-academic-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for binder-design-campaign

README.md
[![agentmods](https://agentmods.dev/badge/skills/spyfighting/my-academic-skills/binder-design-campaign/github.svg)](https://agentmods.dev/skills/spyfighting/my-academic-skills/binder-design-campaign)
Your own site
<a href="https://agentmods.dev/skills/spyfighting/my-academic-skills/binder-design-campaign"><img src="https://agentmods.dev/badge/skills/spyfighting/my-academic-skills/binder-design-campaign/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for binder-design-campaign

Your own site · 80×15
<a href="https://agentmods.dev/skills/spyfighting/my-academic-skills/binder-design-campaign"><img src="https://agentmods.dev/badge/skills/spyfighting/my-academic-skills/binder-design-campaign.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 143 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,329 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00143 $0.02329
Opus 5.5 $0.00057 $0.00932
Sonnet 5.5 $0.00029 $0.00466
Haiku 4.5 $0.00014 $0.00233

Measured 1mo ago against content hash 4ab14d0a34b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

binder-design-campaign scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/check_design_sheet.py, scripts/check_diversity.py, scripts/ledger.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

binder-design-campaign/SKILL.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Protein design campaign

Three branches, one core. First turn the user's goal into a reviewable campaign plan; after the user chooses how far to execute, the evidence rules and deliverable discipline below hold for all three branches.

Branch Read For
binder design workflows/binder-design.md miniprotein binders against a protein target
structure prediction workflows/structure-prediction.md folds or complexes with calibrated confidence
protein engineering workflows/protein-design-general.md stability, enzyme, interface, scaffold redesign

Provenance and rationale: docs/ and NOTICE.md, relative to this skill.

Non-negotiables

These hold at every depth, including inside sub-agents, and bind hardest on the steps nobody is watching.

  1. State only what you established. Every claim in a report, update or manifest traces to an executed computation or a saved artifact you can point at. Verified means you ran the check and hold its output.
  2. Every external identifier is fetched. DOIs, PMIDs, PDB IDs, UniProt accessions, residue ranges and URLs in any deliverable are the literal output of a lookup executed in this session.
  3. Anomalies are bugs until investigated. A score of exactly zero, a gate that passes everything, a gate that fails everything, a constant metric, a perfect metric, an impossible runtime, zero variance across seeds: halt that stage, diagnose, then proceed.
  4. Cheapest falsifying check first. Run the seconds-scale check that could kill a plan before committing hours of GPU to it.
  5. Lead with the unfavourable reading. Headline the worst defensible interpretation of your own data. Disclose deviations from the stated method, and name analyses tuned after seeing results along with how many configurations you tried. Report inconclusive results as inconclusive.
  6. The instrument is a filter, never an oracle. In silico confidence ranks designs within a target. It does not tell you whether a target will work, and it relates only weakly to affinity — measured, not assumed (references/failure-modes.md §1). Every summary you write says so.
  7. Verify inherited inputs. Check target structures, construct definitions and hotspot numbering against their primary source before building on them.
  8. Scope. This protocol is in silico and it ends at the design sheet. Validation is in vitro, by the user or a CRO; analysing returned assay data is a different task and does not run under these gates. Results from an earlier round enter here as operator input at G0, never as something this protocol goes and fetches. Work on select agents or toxins, and enhancement of pathogen function, is out of scope: stop and say so. See docs/responsible-use.md.

Read the full file on GitHub · 184 lines

Files

What ships with it

46 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +24 lines · -5 tokens per session 4ab14d0a34b8
  2. 1mo ago Changed 9a4a177d69d6
  3. 2mo ago First seen · 160 lines · 148 tokens per session scan A db7c470a3235

Subscribe to this mod's changes

binder-design-campaign is a skill published in the GitHub repository SPYfighting/my-academic-skills (23 stars, last pushed 1mo ago), licensed MIT. It adds 143 tokens to every session and 2,329 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

binder-design-campaign compared with skills from other repositories

binder-design-campaign next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
binder-design-campaignSPYfighting/my-academic-skillsSkill231mo agoMITA143Claude Code, Codex
claims-auditjeonnoin-alt/EurekaSkill26mo agoMITA34Claude Code
figure-designjeonnoin-alt/EurekaSkill26mo agoMITA75Claude Code
hypothesis-firstjeonnoin-alt/EurekaSkill26mo agoMITA30Claude Code
manuscript-writingjeonnoin-alt/EurekaSkill26mo agoMITA47Claude Code
research-brainstormingjeonnoin-alt/EurekaSkill26mo agoMITA39Claude Code
experiment-designjeonnoin-alt/EurekaSkill26mo agoMITA28Claude Code