red-team-technical-plan
Skill1 starsChanged 4 months agoMITView on GitHub
Stress-test a material technical plan, architecture, migration, integration, release, or automation proposal before implementation. Use for explicit red-team review, hidden assumptions, failure modes, alternatives, and rollback—not trivial edits.
In this repository15 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- apply-project-updatesSkill
- audit-project-updatesSkill
- design-agent-capabilitySkill
- engineer-cli-toolsSkill
- maintain-agent-instructionsSkill
- maintain-project-docsSkill
- maintain-project-memorySkill
- maintain-web-design-systemSkill
- prepare-web-assetsSkill
- release-web-applicationSkill
- research-software-versionsSkill
- verify-software-changeSkill
- web-discovery-artifactsSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 35
Do not manufacture blockers for trivial work.
- Distinctive: line 86
Do not mutate the project unless the user separately authorizes implementation.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Red-Team Technical Plan
Challenge a proposal constructively before resources or operational trust are committed. Produce a stronger plan, not criticism for its own sake.
Establish the decision
Identify:
- objective, success criteria, and non-goals
- active repository and system boundaries
- known constraints and instruction authorities
- proposed architecture, sequence, and irreversible choices
- users, operators, data, systems, and environments exposed
- evidence already available and claims still assumed
Separate observations, verified facts, inferences, preferences, and speculation. Do not treat popularity, credentials, a previous model's answer, or the existence of an implementation as proof.
Choose review depth
Scale the review to consequence:
- light: local, reversible, low-cost change
- standard: persistent project change or meaningful maintenance burden
- deep: security boundary, production data, broad distribution, expensive migration, irreversible state, or multi-system automation
Do not manufacture blockers for trivial work. State the selected depth and why.
Attack the proposal
Test applicable dimensions:
- Problem fit — Does it solve the actual objective, or a convenient proxy?
- Scope — What hidden systems, repositories, users, and lifecycle work are implied?
- Assumptions — Which prerequisites are unverified, unstable, or mutually inconsistent?
- Correctness — What inputs, edge cases, concurrency, partial failures, or stale state break it?
- Security and privacy — What trust boundaries, permissions, secrets, supply-chain inputs, personal data, or unsafe defaults are introduced?
- Compatibility — Which versions, platforms, clients, formats, or APIs are untested?
- Operations — How are installation, observability, recovery, ownership, upgrades, and support handled?
- Maintenance — What duplicates an existing authority or creates a second system that will drift?
- Economics — What token, compute, storage, network, licensing, and human review costs recur?
- Exit — Can the change be disabled, downgraded, migrated, or removed without losing unrelated state?
For high-impact automation, also examine idempotency, transaction boundaries, retries, rate limits, duplicate processes, configuration corruption, and operator interruption.
Compare alternatives
Include the strongest practical alternatives, including:
- do nothing yet
- narrow pilot
- strengthen an existing capability
- simpler manual or deterministic mechanism
- staged migration
- proposed approach with revised safeguards
Compare them against the same criteria. Do not use a straw alternative to make the preferred proposal look better.
Verify material claims
Inspect current project evidence and use authoritative, current sources for version-sensitive claims. Verify critical findings against source or runtime behavior when possible. Label unverified risks and avoid converting a possible failure into a claimed fact.
Do not mutate the project unless the user separately authorizes implementation.
Produce the decision review
Return:
- verdict: proceed, proceed with changes, pilot first, defer, or reject
- strongest aspects worth preserving
- blocking flaws and why they block
- material non-blocking risks
- assumptions requiring evidence
- alternative comparison
- revised design and acceptance criteria
- validation, monitoring, rollback, and stop conditions
- unresolved questions that materially change the decision
Rank findings by consequence and likelihood. End with the smallest defensible next step.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

