Creator · gaasher
Last updated · Sep 6, 2026
Use when the user has concrete failing cases in code or a guardrail/classifier/filter/prompt/API they own — a red-team failure catalogue OR a CI/CD test-failure report (failing pytest/JUnit tests) — and wants the target patched until those failures are closed without breaking wha
Do not auto-install
Install targets
Codex install prompt
Install the "blue-team" agent skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/blue-team. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when the user has concrete failing cases in code or a guardrail/classifier/filter/prompt/API they own — a red-team failure catalogue OR a CI/CD test-failure report (failing pytest/JUnit tests) — and wants the target patched until those failures are closed without breaking what already works. It points straight at the failed cases (normalize any source with tools/ingest.py), fixes one root-cause class per iteration, and re-checks with tools/verify.py — oracle mode against a red-team oracle, or tests mode against the test suite — keeping a patch only if it closes a class while nothing that passed before regresses, else reverting; loops until every class is closed (dry) or the budget runs out, then opens a pull request with the patch set. The defensive fixer half of a find→fix setup. Not for discovering new failures (that is red-team), and not for editing the oracle, tests, or holdout that define ground truth. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"gaasher-blue-team","task":"Install blue-team","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add gaasher/Agent-Loop-Skills --skill blue-team
Maintenance
active
2mo since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
163
63/100 Quality · 65/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
163 GitHub stars
Repo activity
163 stars, 19 forks
Maintenance
2mo since push
License
MIT
Install
npx skills add gaasher/Agent-Loop-Skills --skill blue-team
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add gaasher/Agent-Loop-Skills --skill blue-teamDo not use when
Alternative
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Alternative
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npx skills add mvanhorn/last30days-skill -g
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38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20blue-team%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20blue-team%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/gaasher-blue-team/install
Agent should check
Copy prompt
Task: Use blue-team in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20blue-team%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/gaasher-blue-team/install
Install command: npx skills add gaasher/Agent-Loop-Skills --skill blue-team
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/gaasher-blue-team/install
LLM text format
/api/skills/gaasher-blue-team/install?format=text
Find alternatives
/api/skills/search?q=blue-team&limit=3
Agent prompt
Use blue-team for this task. Review https://www.openagentskill.com/api/skills/gaasher-blue-team/install, then install with: npx skills add gaasher/Agent-Loop-Skills --skill blue-teamRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/gaasher-blue-team
LLM text
/api/registry/manifest/gaasher-blue-team?format=text
Install alias
/api/registry/install/gaasher-blue-team
Recommend
/api/registry/recommend?task=Use%20blue-team%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
INFO163 GitHub stars
Stars/forks activity
CHECK163 stars, 19 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2mo since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Analyze matches
I need my agent to analyze football matches, World Cup data, xG, players, teams, and predictions.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
A relentless interview to sharpen a plan or design.
--- name: blue-team description: > Use when the user has concrete failing cases in code or a guardrail/classifier/filter/prompt/API they own — a red-team failure catalogue OR a CI/CD test-failure report (failing pytest/JUnit tests) — and wants the target patched until those failures are closed without breaking what already works. It points straight at the failed cases (normalize any source with tools/ingest.py), fixes one root-cause class per iteration, and re-checks with tools/verify.py — oracle mode against a red-team oracle, or tests mode against the test suite — keeping a patch only if it closes a class while nothing that passed before regresses, else reverting; loops until every class is closed (dry) or the budget runs out, then opens a pull request with the patch set. The defensive fixer half of a find→fix setup. Not for discovering new failures (that is red-team), and not for editing the oracle, tests, or holdout that define ground truth. compatibility: Requires Python 3.9+; git + the gh CLI for the pull-request handoff (degrades to a patch series). metadata: version: "0.1.0" ---
# Blue Team
A **defensive fixer** loop — the inverse of `red-team`. The artifact is the **target, now writable**; the feedback signal is two-part, like `optimize-loop`: a **gate that must hold** (nothing that passed before regresses) and a **metric that must drop** (the count of open failure classes, toward zero). You point it at a set of **concrete failed cases** and fix them one root-cause class at a time. Each iteration you patch one class, then run `tools/verify.py`, and keep the patch only if it closes the class with no regression, else revert. You loop until every class is closed (**dry**) or the budget runs out, then hand the patch set off as a pull request. This is the *fix* half of a find→fix setup (see [Pairing](#pairing)).
The failed cases come from a real source; `tools/ingest.py` normalizes any of them into one catalogue: - **`oracle` mode** — a `red-team` `failures.jsonl`: each case is an input where the target's verdict disagrees with a ground-truth **oracle**. A case is closed when target and oracle now agree; a regression is a benign `<holdout>` input that newly disagrees (most often a new over-block). - **`tests` mode** — a **CI/CD test-failure report** (`pytest --junitxml` / JUnit XML, or a list of failing node ids): each case is a failing test. A case is closed when its test now passes; a regression is any *other* test that was passing and now fails.
## When to use Use to fix a concrete set of failing cases in code or a guardrail/classifier/filter/prompt/API the user owns — a red-team catalogue, or the failing tests from a CI run — driving the open-class count to zero without breaking what worked. A `class` is the root-cause group the loop closes as a unit (a red-team technique, or a CI failure area / test class).
Default: pick the mode that matches the source (`oracle` for red-team, `tests` for CI/CD). Escape hatch: in `oracle` mode with no separate functional test suite, the `<holdout>` alone is the regression guard; in `tests` mode the suite's own previously-passing tests are the guard. Not for finding new failures (run `red-team`), and not for editing the ground truth (the oracle, the tests, or the holdout).
## Setup Resolve bindings interactively. If `loop.run.yaml` exists, load it, confirm the values in one line, and skip to the loop. Otherwise: on Claude Code (the `AskUserQuestion` tool is available) infer a likely value per binding and recommend it; on other hosts ask each as a quoted prompt. Then write `loop.run.yaml` and confirm before creating any other files. Two worked configs: `examples/run.example.yaml` (oracle mode) and `examples/tests.run.yaml` (tests mode).
| binding | meaning | default | how to infer | |---|---|---|---| | `<source>` | where the failed cases come from: `oracle` (red-team) or `tests` (CI/CD) | — | red-team `failures.jsonl` → `oracle`; failing pytest/JUnit → `tests` | | `<target_files>` | the file(s) the loop may edit to fix the target | — | the source/guardrail/classifier behind the failures | | `<catalogue>` | the failed cases to close, JSONL; build it with `tools/ingest.py` (see below) | `<sandbox_root>/catalogue.jsonl` | red-team's `<failures_log>`, or a JUnit report | | `<oracle_cmd>` | *(oracle mode)* ground-truth verdict (frozen), same stdin→verdict contract as red-team | — | a reference checker / policy impl | | `<holdout>` | *(oracle mode)* benign inputs that must keep passing (regression guard) | `<sandbox_root>/holdout.jsonl` | known-good inputs the oracle agrees on | | `<test_cmd>` | *(tests mode)* runs the suite and writes a JUnit XML; regressions read from it | — | `pytest --junitxml=<junit>` (or any runner that emits JUnit) | | `<junit>` | *(tests mode)* path to the JUnit XML `<test_cmd>` writes | `<sandbox_root>/junit.xml` | — | | `<iter_strategy>` | `branches` (one commit per kept fix → feeds the PR) or `snapshots` (folder per iter) | `branches` | dirty / non-git tree → snapshots | | `<pr_branch>` | branch the fixes land on and the PR opens from | `blue-team/<tag>` | today's date as `<tag>` | | `<sandbox_root>` | where snapshots + the ledger live | `./sandbox` | — | | `<budget>` | max iterations | 8 | — | | `<patience>` | give up on one class after N failed attempts → mark it a residual | 3 | — |
`<skill_dir>` is this skill's installed folder; substitute the real path when writing `loop.run.yaml`.
**Build the catalogue first** with `tools/ingest.py`, which normalizes any source into `{id, ..., class}`: ``` python3 <skill_dir>/tools/ingest.py --from red-team --in <failures.jsonl> --out <catalogue> # oracle mode python3 <skill_dir>/tools/ingest.py --from junit --in <report.xml> --out <catalogue> # tests mode ```
**The signal** each iteration is `tools/verify.py`, in the mode matching `<source>`: ``` # oracle mode — <target_cmd> runs <target_files>, e.g. "python3 ./guardrail.py" python3 <skill_dir>/tools/verify.py --target "<target_cmd>" --oracle "<oracle_cmd>" \ --catalogue <catalogue> --holdout <holdout> # tests mode — <test_cmd> writes the JUnit report verify.py then reads python3 <skill_dir>/tools/verify.py --test-cmd "<test_cmd>" --junit <junit> --catalogue <catalogue> ``` Either way it prints one JSON object: `{mode, open_classes, closed_classes, open_count, closed_count, regressions, regression_count, still_failing}`.
## The loop Copy this checklist and tick items off:
- [ ] Iteration 0 — baseline: run `tools/verify.py` in the `<source>` mode; record the **open classes** (should match the catalogue) as the current state and confirm `regression_count` is 0 — if it is not, the catalogue or holdout is dirty, so fix that before fixing the target. Log the baseline row. Save a pristine copy of `<target_files>` to `<sandbox_root>/iter0/` (snapshots mode) or note the branch base (branches mode) — this is the **baseline** the final handoff diffs against, and also the snapshot iteration 1 reverts to. - [ ] In `branches` mode, open the run on a fresh branch: `git checkout -b <pr_branch>`. - [ ] For iteration N (≥1): snapshot the **current, pre-patch** `<target_files>` to `iter<N>/` (or note the git HEAD) *before* editing, so a discard can restore exactly this state. - [ ] Pick **one** open class. Read its `still_failing` examples + the suggested fix from the catalogue, and patch `<target_files>` at the **root cause** — one fix should close *all* payloads of that class (e.g. normalize case once, not per-keyword). One class per iteration so each delta is attributable. - [ ] Check the signal: run `tools/verify.py`. **Discard** — restore the snapshot / `git reset --hard` — if `regression_count > 0` (the gate) or the targeted class is still open. `verify.py`'s regression check *is* the gate: in oracle mode a regression is a newly-broken `<holdout>` case (e.g. a fix that closes a bypass by over-blocking benign inputs); in tests mode it is any previously-passing test the patch broke. - [ ] **Keep** if there are no regressions and `open_count` strictly dropped. In `branches` mode commit it: `git commit -am "close <class>: <one-line fix>"`. Append a ledger row. - [ ] If a class resists `<patience>` attempts, mark it an **open residual** and move on rather than thrashing. Stop when `open_count` = 0 (dry), at `<budget>`, or when every remaining class is a residual. `<budget>` counts **attempts** (each keep *or* discard is one iteration), not classes closed — a discard still consumes the budget.
On stop, restore the working files to the **best** iteration (most classes closed, zero regressions) and report: classes closed vs residual, the failures resolved (oracle mode: the bypass/over-block split), and regressions avoided. Then open the pull request (see [Handoff](#handoff-the-pull-request)).
**Fix toolkit.** In `tests` mode the patches are ordinary bug-fixes, grouped by failure area and applied one area per iteration. In `oracle` mode (hardening a guardrail/filter), reach for these root-cause patterns, mirroring red-team's attack toolkit: - **Normalize before matching** — case-fold, de-leet (homoglyph/leet → letters), strip spacing and punctuation, NFKC-normalize unicode. One normalization step closes case / leetspeak / spacing classes. - **Broaden the policy** — add missing synonyms/expansions to the blocked set (the `missing-synonym` class), keyed to the oracle's categories, not ad-hoc strings. - **Tighten over-broad rules** — scope a match to whole words / the right context so benign inputs stop tripping it (the `overblock` class), the most common source of regressions.
Mind the **interaction order** (both modes): make the narrowing/over-broad fix *before* a sweeping one. A fix that strips separators (closing `spacing`) can re-collapse a benign input into an over-broad substring and silently reopen an `overblock` class — and likewise a broad code change can reopen a test a narrower fix had to protect. Fix the narrow/over-broad case first, then generalize.
## Ledger `<sandbox_root>/ledger.tsv`, tab-separated, never commas in the description. `regr` = `regression_count` this iteration (the gate: 0 is clean); `status` ∈ {`keep`,`discard`,`baseline`,`residual`}. Header `iter class_targeted regr open_classes status description`: ``` iter class_targeted regr open_classes status description 0 - 0 5 baseline catalogue: 5 open classes 1 case-bypass 0 4 keep case-fold the input before matching 2 leetspeak 1 4 discard de-leet regex also over-blocked a holdout input (regression) 3 leetspeak 0 3 keep de-leet via translate table, holdout clean 4 missing-synonym 0 2 keep add passphrase/credentials/api-key to the policy set 5 overblock 0 1 keep require whole-word "secret key", not bare "secret" 6 spacing 0 0 keep strip non-alphanumerics before matching — dry ``` Report the **best** iteration (open_classes lowest with `regr` 0), not necessarily the last.
## Constraints - **Only edit `<target_files>`.** The ground truth — the oracle + `<holdout>` (oracle mode) or the test suite (tests mode) — and `tools/verify.py` are frozen; editing what measures the fix manufactures a pass (same rule as red-team and optimize-loop). If the oracle or a test is itself wrong, that is a finding to report, not something to patch here. - **Fix the root cause, not the payload.** One fix should close every item of a class; patching a single example string while siblings still fail means the class is not closed. This mirrors red-team's class accounting, so the two loops agree on what "closed" means. - **The regression gate is non-negotiable.** A patch that breaks something that passed before — a new over-block/bypass (oracle mode) or a previously-passing test (tests mode) — is a regression, not progress; revert it regardless of how many classes it closes. Prefer a narrower fix over a sweepin
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Free and open source. Review the report before installing into production agents.
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Scenario-led draft for blue-team, ready for a manual X post.
blue-team: Use when the user has concrete failing cases in code or a guardrail/classifier/filter/prompt/... 163 stars https://www.openagentskill.com/skills/gaasher-blue-team?ref=x
Listing + install path for blue-team: https://www.openagentskill.com/skills/gaasher-blue-team?ref=x Install: npx skills add gaasher/Agent-Loop-Skills --skill blue-team
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsPermission surface
secrets or environment access, shell or command execution
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Strong README/SKILL.md context
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