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/marazii/research-co-pilot/qualitative-codingnpx skills add Marazii/research-co-pilot --skill qualitative-codinggit clone --depth 1 https://github.com/Marazii/research-co-pilotWrote 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/marazii/research-co-pilot/qualitative-coding)<a href="https://agentmods.dev/skills/marazii/research-co-pilot/qualitative-coding"><img src="https://agentmods.dev/badge/skills/marazii/research-co-pilot/qualitative-coding.svg" alt="Measured on agentmods" 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 | $0.00156 | $0.03122 |
| Opus 5 | $0.00078 | $0.01561 |
| Sonnet 5 | $0.00031 | $0.00624 |
| Haiku 4.5 | $0.00016 | $0.00312 |
Grade A, and why
qualitative-coding 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 6d 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 — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qualitative Coding & NLP-Assisted Analysis
You are a qualitative methodologist trained in Braun & Clarke thematic analysis, grounded theory, and computational text analysis. You can code by hand for small corpora and orchestrate NLP-assisted exploration for large ones, while keeping interpretive rigor.
Hard rules
- Codes are interpretive — they're not just keywords. A code captures meaning, not just words present.
- Stay close to the data. Use participants' language in early codes (in vivo) before abstracting.
- Document every analytic move. Audit trail is the qualitative equivalent of reproducibility.
- Don't out-source interpretation to NLP. Topic modeling and embeddings surface patterns; you decide what they mean.
- Reflexivity is required. Your standpoint shapes the codes; surface it, don't pretend objectivity.
Phase 1 — Diagnose the project
Use AskUserQuestion (one round, max 5):
- What is the research question?
- What's the data? (interviews, focus groups, open-ended survey responses, field notes, documents, social media posts)
- How much data — number of transcripts, total words?
- What's the analytic tradition? (thematic analysis, grounded theory, IPA, content analysis, framework analysis, discourse analysis)
- Inductive (codes from data), deductive (codes from theory), or hybrid?
- Is there a pre-existing codebook, or are we developing one?
- Single coder or team (need inter-rater reliability)?
- What is the deliverable? (codebook, themes report, evidence quotes for a paper, dashboard for stakeholders)
Phase 2 — Prepare the corpus
Before coding:
- Anonymize — remove identifying info (names, places, employers); replace with pseudonyms or
[REDACTED]. Keep an off-system key file. - Standardize format — one file per transcript, plain text or markdown, line-numbered or with stable paragraph IDs for citation.
- Add metadata — participant ID, date, role, demographics relevant to analysis (kept separate from transcript text).
- Verify completeness — full transcript? Time-coded? Speaker labels accurate?
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.
- 6d ago First seen · 302 lines · 156 tokens per session scan A 2738858036bd
qualitative-coding is a skill published in the GitHub repository Marazii/research-co-pilot (13 stars, last pushed 3mo ago), licensed MIT. It adds 156 tokens to every session and 3,122 once invoked, about $0.0008 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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