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 skills add ChipAlexandru/strategy-consultant --skill researchgit clone --depth 1 https://github.com/ChipAlexandru/strategy-consultantWrote 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/chipalexandru/strategy-consultant/research)<a href="https://agentmods.dev/skills/chipalexandru/strategy-consultant/research"><img src="https://agentmods.dev/badge/skills/chipalexandru/strategy-consultant/research/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/skills/chipalexandru/strategy-consultant/research"><img src="https://agentmods.dev/badge/skills/chipalexandru/strategy-consultant/research.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 | $0.00110 | $0.05063 |
| Opus 5 | $0.00055 | $0.02531 |
| Sonnet 5 | $0.00022 | $0.01013 |
| Haiku 4.5 | $0.00011 | $0.00506 |
Grade A, and why
research 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 11d 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research — Parallel Investigation with Validation
Conduct rigorous research by deploying two independent research agents working in parallel, followed by a validation agent that cross-checks their findings. This three-agent architecture reduces confirmation bias and produces a more trustworthy evidence base than a single research pass.
Preflight Gate (run BEFORE any other step)
This phase requires upstream state and artifacts. Before doing anything else, verify ALL of the following:
engagement-state.jsonexists in the active workspace."problem-definition"is incompleted_phases.- The following artifacts exist on disk and are non-empty:
precision-anchor.mdclient-question-checklist.mdsource-material-extraction-log.mdstep0-answers.md
If ANY required item is missing or empty, STOP. Do not run Step 0, do not write a research brief, do not dispatch agents. Report the specific missing state field or artifact path to the user and route control back to engagement-manager. Do NOT reconstruct upstream context locally — the orchestrator owns that.
When the gate passes:
- Read
engagement-state.jsonand treat itsworkspace_pathas the active workspace. All artifacts produced in this phase write into that workspace. - Use the
precision-anchor.mdandclient-question-checklist.mdfrom the workspace as authoritative — do not paraphrase or rewrite them inside this skill.
At the end of this phase, after research-validated.md is written and Checkpoint 2 is approved, append "research" to completed_phases, set current_phase to the next phase (expert-interview if scenario D/E, otherwise sense-check), update artifact_paths.research_validated, set a new next_required_action, refresh last_updated, and write engagement-state.json.
Data Source Priority Hierarchy and Scenarios
Source priority (Internal data > Expert interviews > Public research), conflict-resolution rules, attribution rules by source type, and the underlying CS-1 to CS-4 scoring scale are specified in references/research-source-guide.md. Read that file before dispatching analysts. It is the source of truth for source-quality rules; do not re-explain them inside this SKILL.
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.
- 11d ago First seen · 281 lines · 110 tokens per session scan A 9fd36cfcbad4
research is a skill published in the GitHub repository ChipAlexandru/strategy-consultant (4 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 110 tokens to every session and 5,063 once invoked, about $0.0006 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 2026-08-31.
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