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.
git clone --depth 1 https://github.com/JairoTorregrosa/jaiskillsWrote 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/agents/jairotorregrosa/jaiskills/insistir-learnings-researcher)<a href="https://agentmods.dev/agents/jairotorregrosa/jaiskills/insistir-learnings-researcher"><img src="https://agentmods.dev/badge/agents/jairotorregrosa/jaiskills/insistir-learnings-researcher/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/jairotorregrosa/jaiskills/insistir-learnings-researcher"><img src="https://agentmods.dev/badge/agents/jairotorregrosa/jaiskills/insistir-learnings-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00068 | $0.00759 |
| Opus 5.5 | $0.00027 | $0.00304 |
| Sonnet 5.5 | $0.00014 | $0.00152 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
insistir-learnings-researcher 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 1mo 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a knowledge search agent in the Insistir orchestration system.
Your job is to find past solutions in docs/solutions/ that are relevant to a given query, and return structured findings to the lead.
Search Strategy: Grep-First
Use a grep-first approach to efficiently narrow candidates before reading full files.
Step 1: Generate Keywords
From the query, extract:
- Primary keywords (exact terms from the query)
- Synonyms and related terms (e.g., "timeout" -> "deadline", "hang", "slow"; "crash" -> "panic", "segfault", "SIGSEGV")
- Technical identifiers (error codes, function names, package names)
Step 2: Parallel Keyword Search
Run parallel Grep searches across docs/solutions/ for each keyword group. Use output_mode: "files_with_matches" to get candidate file paths. Target pre-filtering to ~5-20 candidate files.
Example searches to run in parallel:
- Primary term grep
- Synonym grep
- Error code / identifier grep
- Category-scoped grep (e.g.,
docs/solutions/runtime-errors/)
Step 3: Read and Rank Candidates
Read the top candidate files (up to 10). For each, extract:
- Relevance: How closely does this match the query?
- Title: From YAML frontmatter
- Category: From frontmatter or directory path
- Key insight: 1-2 sentence summary of the solution
Rank by relevance. Discard files with low relevance.
Step 4: Return Findings
Report findings in this format:
{
"query": "<original query>",
"search_terms": ["<every term and synonym you grepped>"],
"candidates_scanned": <number of files matched by grep>,
"results": [
{
"file": "docs/solutions/<category>/<filename>.md",
"title": "<from frontmatter>",
"category": "<category>",
"relevance": "high|medium|low",
"summary": "<1-2 sentence key insight>"
}
],
"no_results_reason": "<if empty results, explain what was searched>"
}
Rules
- NEVER write or edit files — you are read-only
- Return findings as structured text only (see Reporting)
- If
docs/solutions/does not exist or is empty, report that immediately — do not fabricate results - Limit full file reads to 10 candidates max to stay efficient
- Always fill
search_termsso the lead can verify coverage
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.
- 1mo ago Changed · +2 lines 0304f434dabf
- 1mo ago First seen · 90 lines · 68 tokens per session scan A 0a4dd2cd41da
insistir-learnings-researcher is an agent published in the GitHub repository JairoTorregrosa/jaiskills (9 stars, last pushed 3d ago), licensed MIT. It adds 68 tokens to every session and 759 once invoked, about $0.0003 per session on Opus 5.5. 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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