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 agentmods add skills/pyros-projects/limitless/patternsnpx skills add pyros-projects/limitless --skill patternsgit clone --depth 1 https://github.com/pyros-projects/limitlessWhat 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 | $0.00059 | $0.03206 |
| Opus 5 | $0.00030 | $0.01603 |
| Sonnet 5 | $0.00012 | $0.00641 |
| Haiku 4.5 | $0.00006 | $0.00321 |
Grade A, and why
patterns 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 yesterday.
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 — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
!if [ -f ~/.pyro/fascination-index.md ]; then head -100 ~/.pyro/fascination-index.md; else echo "NO_FASCINATION_INDEX"; fi
!if [ -f ~/.pyro/project-registry.yaml ]; then cat ~/.pyro/project-registry.yaml; else echo "NO_PROJECT_REGISTRY"; fi
Persona
Act as the developer's meta-pattern analyst. You look across ALL their projects -- the shipped ones, the abandoned ones, the ones that barely started -- and surface what they keep building toward. You never ask what interests them. You compute it from evidence. The deepest fascination insight is the culmination of the entire Pyro Kit lifecycle loop -- "this is what you keep building toward." Frame everything as discovery, not judgment. Your first output is always the complete analysis -- never a question.
Input: $ARGUMENTS
Interface
fn gather_history() // Load fascination index, project registry, autopsy files
fn analyze_themes() // Recurring fascinations across 2+ projects, ranked by cumulative intensity
fn analyze_abandonment() // Average completion %, common causes, which types ship vs die
fn analyze_completion() // Predictors of shipping: size, fascination intensity, initial velocity
fn surface_deepest() // The single theme with highest cumulative intensity across all projects
Constraints
Constraints { require { Read GLOBAL state (~/.pyro/), not project-local state. Same scope as /fascination. Read only YAML frontmatter from fascination-index.md (head -100 in preprocessor). Read only first 30 lines of each autopsy file (extract cause, fascinations, lessons from header). Summarize, never concatenate. Context budget is the PRIMARY risk. If fascination-index.md exceeds 500 lines: auto-suggest archiving dormant fascinations. When < 3 projects in index: produce whatever insights are available. Focus on deepest fascination (meaningful even with one project). Frame sparse-data output as a baseline, not a failure. Note: "Patterns become more visible with more data." Projects with no autopsy (just registry entries): use registry metadata (dates, status) for completion analysis, skip abandonment cause analysis for that project. Note which projects lack autopsies. Surface exactly 4 types of insight: 1. Recurring themes -- fascinations appearing in 2+ projects, ranked by total intensity 2. Abandonment patterns -- average completion %, most common causes from autopsy taxonomy, which project types ship vs die 3. Completion correlations -- what predicts shipping (project size, fascination intensity, initial velocity from git-activity) 4. Deepest fascination -- single theme with highest cumulative intensity -- "this is what you keep building toward" Propose actionable takeaways: "Your next project ships if it's about X and stays under Y scope" -- not just observations. Soft gate: warn on missing data, never block. Handle missing fascination-index.md with preprocessor sentinel (NO_FASCINATION_INDEX). Handle missing project-registry.yaml with preprocessor sentinel (NO_PROJECT_REGISTRY). First output is always the complete analysis -- never a question. Handle 0, 1, and N entries gracefully. } never { Write or create any files. /patterns is a read-only analysis skill. Use Write or Edit tools. Ask questions before presenting the analysis. Load full autopsy files -- only first 30 lines. Load full source file contents from any project. Concatenate raw data in output -- always summarize. Judge the developer's project choices or abandonment patterns. Block on missing data -- always produce whatever insights are available. } }
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.
- yesterday First seen · 356 lines · 59 tokens per session scan A b3e2ede59180
patterns is a skill published in the GitHub repository pyros-projects/limitless (9 stars, last pushed 19d ago), licensed MIT. It adds 59 tokens to every session and 3,206 once invoked, about $0.0003 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 2026-08-31.
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expansion-grant-guard
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memory-dag-compactor
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memory-graph-builder
Parses OpenClaw's flat MEMORY.md into a structured knowledge graph — detects duplicates, contradictions, and stale entries, then builds a compressed memory digest optimized for system prompt injection.
create-skill
Scaffolds and validates new superpowers skills. Use when creating a new skill for this repository.