autocontext is a self-improving harness for AI agents that runs a goal through evaluation, preserves useful lessons, and records artifacts for later runs. Developers use it to help agents and future versions of those agents perform better on recurring tasks. The catalogue add-ons support its agent-improvement workflow.
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.
npx skills add greyhaven-ai/autocontext --skill grid-ctf-opsgit clone --depth 1 https://github.com/greyhaven-ai/autocontextWrote 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.
[](https://agentmods.dev/skills/greyhaven-ai/autocontext/grid-ctf-ops)<a href="https://agentmods.dev/skills/greyhaven-ai/autocontext/grid-ctf-ops"><img src="https://agentmods.dev/badge/skills/greyhaven-ai/autocontext/grid-ctf-ops.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01395 |
| Opus 5 | $0.00021 | $0.00698 |
| Sonnet 5 | $0.00008 | $0.00279 |
| Haiku 4.5 | $0.00004 | $0.00139 |
Grade A, and why
grid-ctf-ops 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 8d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
1 file 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.
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.
- 8d ago First seen · 48 lines · 42 tokens per session scan A f7dae927a4c3
grid-ctf-ops is a skill published in the GitHub repository greyhaven-ai/autocontext (1,291 stars, last pushed 6d ago), licensed Apache-2.0. It adds 42 tokens to every session and 1,395 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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