Agent skill

Grid CTF Strategy Playbook

by greyhaven-ai in greyhaven-ai/autocontext

Operational notes for generating, evaluating and debugging strategies in the autocontext grid_ctf scenario, with tier rules and parameter ranges that worked or failed.

Apache-2.0Auto-check passedAgent Workflows

Install Grid CTF Strategy Playbook

skills CLI
$ npx skills add greyhaven-ai/autocontext --skill grid-ctf-ops -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install greyhaven-ai/autocontext grid-ctf-ops --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/greyhaven-ai/autocontext.git skills-src && mkdir -p .claude/skills && cp -r skills-src/autocontext/skills/grid-ctf-ops .claude/skills/grid-ctf-ops && rm -rf skills-src

Use ~/.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/

Facts

Skill name
grid-ctf-ops
GitHub stars
1.3k
Token cost
~1.3k tokens
SKILL.md length
595 words
Files
2
Skills in repo
5
Repo updated
First seen
Licence
Apache-2.0

At a glance

Operational notes for generating, evaluating and debugging strategies in the autocontext grid_ctf scenario, with tier rules and parameter ranges that worked or failed.

  • Generating a new strategy for the grid_ctf scenario
  • SKILL.md covers Operational Lessons and Bundled Resources
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Debugging a grid_ctf strategy that scored zero

What it does

The skill collects lessons from strategy evolution in the grid_ctf scenario, where a strategy sets values such as aggression, defense and path_bias against a given resource_density. The main warning is that parameters tuned for one density tier can score zero in another, so the tier must be checked before choosing values. In the critical_low tier, below a density of 0.20, combined aggression and defense should stay at or under 1.05.

Other rules keep defense between 0.45 and 0.55 and aggression at or above 0.48, treat efficiency as heavily weighted in scoring, and advise small changes from a proven baseline instead of large jumps. After a zero score, reset to the baseline for the current tier rather than tweaking failed values. A playbook.md file is bundled, and the excerpt is truncated.

When your agent uses it

  • Generating a new strategy for the grid_ctf scenario
  • Debugging a grid_ctf strategy that scored zero
  • Coaching or evaluating strategies against known resource tiers

Example prompts

  • “Propose grid_ctf parameters for a critical_low resource density run.”
  • “My grid_ctf strategy scored zero after I raised aggression; work out what went wrong.”
  • “Compare these two grid_ctf strategies against the lessons in the playbook.”

Requirements

  • The autocontext harness with the grid_ctf scenario

What it can do on your machine

Read from SKILL.md and the folder at commit f72c154. 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

    No URLs in SKILL.md.

    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

Grid CTF Strategy Playbook loads about 1.3k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 595 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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 greyhaven-ai/autocontext at commit f72c154, republished under its Apache-2.0 licence (© greyhaven-ai). 595 words, ~1,306 tokens.

Download SKILL.mdSave it as .claude/skills/grid-ctf-ops/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
grid-ctf-ops
description
Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.

Grid Ctf Operational Knowledge

Accumulated knowledge from autocontext strategy evolution.

Operational Lessons

Prescriptive rules derived from what worked and what failed:

  • Cross-tier parameter transfer is the #1 catastrophic failure mode. Parameters validated at one resource_density tier produce zero scores at different tiers. ALWAYS verify tier before parameter selection.
  • When resource_density < 0.20 (critical_low), total commitment (aggression + defense) MUST stay ≤ 1.05. Target ≤ 1.00 for a 5% safety buffer. Exceeding by even 10% causes energy starvation and zero scores.
  • When resource_density is moderate (0.40–0.60), total commitment ceiling is 1.20. Target 4–6% buffer (≤ 1.15). A 16% buffer wastes capacity.
  • Defense must stay in [0.45, 0.55]. Below 0.45 risks base loss; above 0.55 starves capture progress.
  • Aggression must be ≥ 0.48 to generate meaningful capture progress. Zero capture = zero score.
  • The scoring formula (score ≈ capture + (efficiency - 0.5) × 0.39) makes efficiency extremely valuable. Losing 4% efficiency costs ~1.5 score points.
  • Balanced strategy (agg=0.58, def=0.57, pb=0.55) achieved 0.7615 at density≈0.437, bias≈0.51 — moderate balanced parameters outperform extremes within the correct tier.
  • Conservative baseline (agg=0.50, def=0.50, pb=0.48) scored 0.7198 at density≈0.147, bias≈0.648 — proven viable in critical_low tier.
  • Over-aggression (agg ≥ 0.65) without proportional defense (≥ 0.52) causes defender survival drops and energy efficiency decline. Optimal moderate-tier aggression is [0.56, 0.60].
  • Generation 3 rollback (agg=0.67, def=0.52, pb=0.60, score=0.7486) and Gen 2 rollback (agg=0.62, def=0.52, pb=0.58, score=0.7369) both confirm over-commitment underperforms.
  • Perfect defender survival (1.00) signals defensive over-allocation. Optimal target is 0.95–0.99, freeing resources for capture.
  • Incremental changes (±0.02 to ±0.05) from a proven baseline within the same resource tier are the only validated safe optimization method. Large jumps (±0.09+) led to rollbacks.
  • After a zero score, RESET to the proven baseline for the current tier. Do NOT incrementally tweak failed parameters.
  • Path_bias in low-resource environments: cap at 0.50. Concentrated force projection is energy-expensive.
  • Path_bias for balanced enemy (bias ≤ 0.55): use [0.50, 0.55]. For asymmetric enemy (bias > 0.6): use [0.45, 0.50].
  • Energy efficiency of 0.90 at commitment=1.00 in critical_low confirms the ceiling is accurate; incremental increases to 1.01–1.03 are viable.
  • All validation tools (config_constants, energy_budget_validator, stability_analyzer, threat_assessor) MUST be run before deployment. Risk > 0.65 or stability < 0.45 predicts poor performance.
  • Recovery priority after zero score: (1) non-zero capture, (2) defender survival, (3) energy sustainability, (4) optimize capture progress.
  • The observation narrative is the authoritative source for environment data. Always read resource_density and enemy_spawn_bias from the actual observation state.
  • When conditions exactly match a proven baseline, deploy it directly rather than converging incrementally.
  • When aggression exceeds 0.7 without proportional defense, win rate drops.
  • Defensive anchor above 0.5 stabilizes Elo across generations.
  • Generation 2 ROLLBACK after 2 retries (score=0.7369, delta=-0.0461, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.61, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
  • Generation 3 ROLLBACK after 2 retries (score=0.7339, delta=-0.0491, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.61, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
  • Generation 4 ROLLBACK after 2 retries (score=0.7669, delta=-0.0161, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.66, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
  • Generation 5 ROLLBACK after 2 retries (score=0.7396, delta=-0.0434, threshold=0.005). Strategy: {"aggression": 0.62, "defense": 0.52, "path_bias": 0.58}. Narrative: Capture phase ended with progress 0.62, defender survival 0.96, and energy efficiency 0.87.. Avoid this approach.
Show full SKILL.md (47 more words)Show less

Bundled Resources

  • Strategy playbook: See playbook.md for the current consolidated strategy guide (Strategy Updates, Prompt Optimizations, Next Generation Checklist)
  • Analysis history: knowledge/grid_ctf/analysis/ — per-generation analysis markdown
  • Generated tools: knowledge/grid_ctf/tools/ — architect-created Python tools
  • Coach history: knowledge/grid_ctf/coach_history.md — raw coach output across all generations
  • Architect changelog: knowledge/grid_ctf/architect/changelog.md — infrastructure and tooling changes

© greyhaven-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in autocontext/skills/grid-ctf-ops of greyhaven-ai/autocontext.

  • SKILL.md
  • playbook.md

Open the folder on GitHubat commit f72c154

Compare with similar skills

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Darwin Skill Optimizeralchaincyf/darwin-skill6.2k1 repos~4.7kAutomated safety check: PassMIT
Skill Release Gaterohitg00/ai-engineering-from-scratch66k—~1kAutomated safety check: PassMIT
CodeGraph Agent Evalcolbymchenry/codegraph74k—~950Automated safety check: PassMIT

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Categories

Questions about Grid CTF Strategy Playbook

What does Grid CTF Strategy Playbook do?

Operational notes for generating, evaluating and debugging strategies in the autocontext grid_ctf scenario, with tier rules and parameter ranges that worked or failed. The skill collects lessons from strategy evolution in the grid_ctf scenario, where a strategy sets values such as aggression, defense and path_bias against a given resource_density. The main warning is that parameters tuned for one density tier can score zero in another, so the tier must be checked before choosing values.

When should I use Grid CTF Strategy Playbook?

Grid CTF Strategy Playbook fits situations like: generating a new strategy for the grid_ctf scenario; debugging a grid_ctf strategy that scored zero; coaching or evaluating strategies against known resource tiers.

How do I install Grid CTF Strategy Playbook in Claude Code?

Run `npx skills add greyhaven-ai/autocontext --skill grid-ctf-ops -a claude-code`. Or copy the skill folder (autocontext/skills/grid-ctf-ops in greyhaven-ai/autocontext) into .claude/skills/grid-ctf-ops in your project. Claude Code loads it when a task matches its description.

How do I install Grid CTF Strategy Playbook in Codex?

Run `npx skills add greyhaven-ai/autocontext --skill grid-ctf-ops -a codex`. Or copy the skill folder (autocontext/skills/grid-ctf-ops in greyhaven-ai/autocontext) into .agents/skills/grid-ctf-ops in your project. Codex loads it when a task matches its description.

Can I use Grid CTF Strategy Playbook 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 greyhaven-ai/autocontext --skill grid-ctf-ops -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/grid-ctf-ops, .gemini/skills/grid-ctf-ops, .github/skills/grid-ctf-ops and .opencode/skills/grid-ctf-ops in your project.

What does Grid CTF Strategy Playbook need to run?

SKILL.md names no scripts, command-line tools or credentials: Grid CTF Strategy Playbook is instructions for the agent only. Our summary lists: The autocontext harness with the grid_ctf scenario.

Does Grid CTF Strategy Playbook access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Grid CTF Strategy Playbook 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 Grid CTF Strategy Playbook use?

Grid CTF Strategy Playbook is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Grid CTF Strategy Playbook use?

About 1.3k tokens (SKILL.md is roughly 5.2k 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 Grid CTF Strategy Playbook?

Skills that share tags, products or a category with Grid CTF Strategy Playbook: MCP Server Builder (anthropics/skills, 180k stars), Diagnosing Superpowers Sessions (obra/superpowers, 297k stars), Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars) and Skill Release Gate (rohitg00/ai-engineering-from-scratch, 66k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Grid CTF Strategy Playbook?

greyhaven-ai (a GitHub organization) maintains it in greyhaven-ai/autocontext, which has 1,305 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 7, 2026.

Source: greyhaven-ai/autocontext on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.