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/alonf/mcppythondemo/reskillnpx skills add alonf/MCPPythonDemo --skill reskillgit clone --depth 1 https://github.com/alonf/MCPPythonDemoWhat 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 | $0.00012 | $0.00799 |
| Opus 5 | $0.00006 | $0.00400 |
| Sonnet 5 | $0.00002 | $0.00160 |
| Haiku 4.5 | $0.00001 | $0.00080 |
Grade A, and why
reskill 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.
This is a copy
95% identical to reskill — 184 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
When the coordinator hears "team, reskill" (or similar: "optimize context", "slim down charters"), trigger a team-wide optimization pass. The goal: reduce per-agent context consumption by extracting shared patterns from charters and histories into reusable skills.
This is a periodic maintenance activity. Run whenever charter/history bloat is suspected.
Process
Step 1: Audit
Read all agent charters and histories. Measure byte sizes. Identify:
- Boilerplate — sections repeated across ≥3 charters with <10% variation (collaboration, model, boundaries template)
- Shared knowledge — domain knowledge duplicated in 2+ charters (incident postmortems, technical patterns)
- Mature learnings — history entries appearing 3+ times across agents that should be promoted to skills
Step 2: Extract
For each identified pattern:
- Create or update a skill at
.squad/skills/{skill-name}/SKILL.md - Follow the skill template format (frontmatter + Context + Patterns + Examples + Anti-Patterns)
- Set confidence: low (first observation), medium (2+ agents), high (team-wide)
Step 3: Trim
Charters — target ≤1.5KB per agent:
- Remove Collaboration section entirely (spawn prompt + agent-collaboration skill covers it)
- Remove Voice section (tagline blockquote at top of charter already captures it)
- Trim Model section to single line:
Preferred: {model} - Remove "When I'm unsure" boilerplate from Boundaries
- Remove domain knowledge now covered by a skill — add skill reference comment if helpful
- Keep: Identity, What I Own, unique How I Work patterns, Boundaries (domain list only)
Histories — target ≤8KB per agent:
- Apply history-hygiene skill to any history >12KB
- Promote recurring patterns (3+ occurrences across agents) to skills
- Summarize old entries into
## Core Contextsection - Remove session-specific metadata (dates, branch names, requester names)
Step 4: Report
Output a savings table:
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 · 93 lines · 12 tokens per session scan A e29518b57609
reskill is a skill published in the GitHub repository alonf/MCPPythonDemo (0 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 799 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to reskill, differing in 184 lines, and is treated as a copy.
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