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 agents/microsoft/p.808/analyze-resultsgit clone --depth 1 https://github.com/microsoft/P.808What 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 | $0.00052 | $0.02840 |
| Opus 5 | $0.00026 | $0.01420 |
| Sonnet 5 | $0.00010 | $0.00568 |
| Haiku 4.5 | $0.00005 | $0.00284 |
Grade A, and why
analyze-results 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 2d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze subjective test results
Use this runbook when asked to analyze, parse, or evaluate results from a completed subjective speech quality test (ACR, DCR, CCR, P.835, P.804, echo impairment, or personalized P.835).
Trigger phrases: "analyze results", "parse results", "evaluate the study", "process the answers", "run result parser".
Platform and shell adaptation
Code examples use PowerShell on Windows (\ paths). Adapt for other OS/shells:
replace PowerShell cmdlets with equivalents, use python3 if needed, convert paths.
Replace REPO_ROOT with the actual absolute path of this repository.
Mandatory pre-check
Before running anything:
- Read
AGENTS.mdand.github\copilot-instructions.md. - Confirm this is an analysis task, not creation. For study creation, use
the
create-studyagent instead.
Environment prerequisites
Verify once at the start:
- Python deps:
pip install -r requirements.txt --quietinsrc\.
Inputs the agent must collect
Do not guess these values if they are missing:
- Test method: one of
acr,dcr,ccr,p835,p804,echo_impairment_test,pp835. - Result parser config file (
*_result_parser.cfg): generated bymaster_script.pyduring study creation. Located in the project output directory. - Answers CSV (
Batch_XXX.csv): exported from the crowdsourcing platform (AMT) or HIT App server. Contains worker responses. - Prolific demographic CSV (optional):
prolific_demographic_export_*.csv— only needed if the study was run on Prolific via HIT App server. - Payment per session (Prolific only): the reward paid to a participant per
session/HIT (e.g.
2.10). Prolific does not include the reward in its export, so the parser cannot compute payment-per-hour statistics without it. Ask the user for this value whenever the study was run on Prolific; pass it to the parser as--payment_per_session. Not needed for AMT (the reward is already a column in the AMT batch).
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.
- 2d ago First seen · 269 lines · 52 tokens per session scan A 741ed80ec31e
analyze-results is an agent published in the GitHub repository microsoft/P.808 (230 stars, last pushed 14d ago), licensed MIT. It adds 52 tokens to every session and 2,840 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 2026-08-30.
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