AD threadseer
Transform transcripts, meetings, chats, interviews, voice notes, and mixed conversational material into evidence-backed decision briefs, recommendations, action plans, risk and insight analyses, shareable team reports, follow-up drafts, and institutional memory. Use when Codex must analyze a conversation end-to-end; extract decisions, commitments, owners, disagreements, assumptions, risks, opportunities, or moonshots; answer what to do next and why; compare conversations over time; or turn messy dialogue into a structured Markdown or JSON deliverable while distinguishing stated evidence from inference.
Transform transcripts, meetings, chats, interviews, voice notes, and mixed conversational material into evidence-backed decision briefs, recommendations…
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 44/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 6 mutating operations with no state check
- 40Consistency. Frontmatter name (threadseer) differs from the folder (threadseer-agent-skill)
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 19 steps
- 100Execution cost. Instruction body is 1579 tokens
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 609: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 19 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.